![(a) Artefact A's surface condition post-test roof radius R1, (b) roof radius after fatigue testing, (c) surface condition of reference radius R2 (oriented bottomup), and (d) reference radius after fatigue testing (Obilanade, Törlind, & Dordlofva 2022), [Courtesy of P. Åkerfeldt, Luleå University of Technology].](https://www.researchgate.net/publication/386333464/figure/fig4/AS:11431281294336002@1733143012086/a-Artefact-As-surface-condition-post-test-roof-radius-R1-b-roof-radius-after_Q320.jpg)
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Content may be subject to copyright.
Characteristics of effective design
support: insights from evaluating
additive manufacturing design
artefacts
Didunoluwa Obilanade
1
, Peter Törlind
1
and Christo Dordlofva
1,2
1
Product Innovation, Department of Social Sciences, Technology and Arts, Luleå University of
Technology, Luleå, Sweden
2
GKN Aerospace Engine Systems, Trollhättan, Sweden
Abstract
Evaluation approaches are needed to ensure the development of effective design support.
These approaches help developers ensure that their design support possesses the general
design support characteristics necessary to enable designers to achieve their desired out-
comes. Consequently, evaluating design support based on these characteristics ensures that
the design support fulfils its intended purpose.
This work reviews design support definitions and identifies and describes 11 design support
characteristics. The characteristics are applied to evaluate a proposed design support that
uses additive manufacturing (AM) design artefacts (AMDAs) to explore design uncertain-
ties. Product-specific design artefacts were designed and tested to investigate buildability
limits and the relationship between surface roughness and fatigue performance of a design
feature in a space industry component. The AMDA approach aided the investigation of
design uncertainties, identified design solution constraints, and uncovered previously
unknown uncertainties. However, the results provided by product-specific artefacts depend
on how well the user frames their problem and understands their AM process and product.
Hence, iterations can be required. Based on the evaluation of the AMDA process, setting test
evaluation criteria is recommended, and the AMDA method is proposed.
Keywords: additive manufacturing, characteristics, design artefact, design support, surface
roughness
1. Introduction
Additive manufacturing (AM) refers to the joining of materials to create parts
through a layer-by-layer process (International Organization for Standardization
2021). Using metal AM technologies like laser powder bed fusion (LPBF), which
uses a laser beam to melt powdered metal to form parts, designers can manufacture
innovative and customised near-net-shape part designs. As the layer-by-layer
process of AM technologies provides unique capabilities for shape, hierarchical,
functional, and material complexities (Gibson, Rose, & Stucker 2015), over time,
traditional subtractive design methods have become unsuitable for the capabilities
offered by AM technologies, and designers require new design support for thinking
additively (Yang & Zhao 2015; Prabhu et al. 2020; Valjak et al. 2020). As designers
Received 07 March 2023
Revised 19 September 2024
Accepted 20 September 2024
Corresponding authors
Didunoluwa Obilanade
Didunoluwa.Obilanade@ltu.se
Peter Törlind
Peter.Torlind@ltu.se
Christo Dordlofva
Christo.Dordlofva@ltu.se
© The Author(s), 2024. Published by
Cambridge University Press. This is
an Open Access article, distributed
under the terms of the Creative
Commons Attribution licence (http://
creativecommons.org/licenses/by/
4.0), which permits unrestricted
re-use, distribution and
reproduction, provided the original
article is properly cited.
Des. Sci., vol. 10, e38
journals.cambridge.org/dsj
DOI: 10.1017/dsj.2024.43
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have spent many years working with conventional manufacturing techniques, it
may be challenging to break out of their conceptual barriers and conceive designs
compatible with AM (Seepersad 2014). Hence, the design for AM (DfAM) field has
emerged to support designers as they try to exploit the design potentials of AM
(Laverne et al. 2015).
DfAM support has been developed in several formats. Adam and Zimmer
(2014) developed geometry-focused process-independent design rules for AM by
printing test specimens using three AM technologies (LPBF, laser sintering, and
fused deposition modelling). However, they stipulate that their design rules are
only valid within the set boundary conditions from which they were derived (e.g.,
the material, machine, and parameters). Becker et al. (2005) summarised some of
the AM design opportunities and provided a set of design principles to direct
designers to adapt their design thinking to the possibilities of AM. Diegel, Nordin,
and Motte (2019) created a practical guide for DfAM that provides considerations
for designing a part with minimal print time, reduced post-processing require-
ments, and reduced anisotropy. However, the rules and principles listed by Becker,
Grzesiak, and Henning (2005) and Diegel, Nordin, and Motte (2019) are quite
general, focusing on aiding designers in considering the best utilisation of AM and
its capabilities in their products. Diegel, Nordin, and Motte (2019) also stated that
the number-one rule of DfAM is ‘it depends’, which means that design rules are
variable and dependent on the geometry, part, materials, and AM technology.
Thus, few design rules are universally applicable to AM, and emphasis should be
placed on using process-specific design supports and test prints to balance design
innovation and product realisation feasibility.
Design support is an overarching term that covers many ways to improve
design, such as methods, rules, and guidelines (Blessing & Chakrabarti 2009). This
article uses ‘design support’as an umbrella term for these concepts. The results or
knowledge obtained from design support depends on how the support was
developed, and several factors contribute to the effectiveness of design support
in aiding the designer and providing design understanding (Cash, Daalhuizen, &
Hekkert 2023). Developers of design support need to ensure that their design
support can achieve its intended outcomes correctly. To provide this assurance,
evaluating the design support to validate it by demonstrating its usefulness
concerning a purpose is required (Pedersen et al. 2000). Blessing and Chakrabarti
(2009) and Jagtap et al. (2014) highlight that many tools, methods, and guidelines
have weak foundations due to being poorly evaluated. Lack of evaluation has been
noted as one of the issues concerning the transfer of methods from academia to
industry (López-Mesa & Bylund 2011; Gericke, Eckert, & Stacey 2022). Despite
design supports being significant and standard in design practice, little information
is available to understand how and why design methods work (Daalhuizen 2014;
Dalsgaard 2017).
Appropriate evaluation approaches are needed to evaluate DfAM supports for
their effectiveness to confirm the development of helpful design support. Design
support developers need to evaluate their support to determine if its application has
led to the achievement of their intended goals and improved the design situation
had the support not been used (Blessing & Chakrabarti 2009). To evaluate design
support appropriately, one must understand how design supports work, the
content of the support, and what makes the design support ‘good’, i.e., how one
can say design support has performed well.
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1.1. The AM design artefact process
A proposed DfAM support for addressing the variability of design issues specific to
AM is utilising AM design artefacts (AMDAs). As illustrated in Figure 1, the
AMDA process proposed by Dordlofva and Törlind (2020) describes a systematic
means of creating artefacts that investigate specific AM design uncertainties
relating to the AM process and part geometry.
The AMDA process was proposed through interactive research between
researchers and engineers at companies in the European space industry to develop
an understanding of AM process capabilities (Svensson, Brulin, & Ellström 2015).
The engineers identified AM-related design uncertainties within their products
and then purposely designed test artefacts to explore these uncertainties.
The AMDA process encourages a designer to constrain the prototype design to
test a critical assumption using the prototype. The AMDA process is a prototype-
driven design approach in which the designer identifies design uncertainties and
designs and prints product-specific design artefacts. These artefacts are subse-
quently evaluated to help reduce uncertainties and increase knowledge, thereby
supporting DfAM through iterative testing. As indicated in Figure 1, the artefacts
serve different purposes in each phase of the three-phase model: inspiration,
evolution, and validation. During the inspiration phase, multiple AMDAs are
constructed to explore feasible design opportunities through AM and investigate
the uncertainties associated with achieving these opportunities. During the evo-
lution phase, the design features acquire detail through further testing and evalu-
ation of evolved solutions. In this manner, the AMDAs drive the design
specifications (AMDA-driven specifications). Finally, more comprehensive proto-
types can be used for validation as the design specification is set (specification-
driven AMDAs) (Dordlofva & Törlin 2020).
In contrast, other DfAM supports are often set within the boundary conditions
of their development and focus solely on ensuring the buildability of an AM design
(Adam & Zimmer 2014). Metal AM guidelines predominantly focus on guiding to
ensure the manufacturability of parts (Kokkonen et al. 2016; Schnabel, Oettel, &
Mueller 2017; Diegel, Nordin, & Motte 2019). However, they often lack detailed
guidance on the specific adverse effects of a particular design choice. For example,
Figure 1. The design process with AMDAs as support (adapted from Dordlofva & Törlind (2020, p. 5), with
permission).
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although Kokkonen et al. (2016) provide a guide for minimum wall thickness,
stating that it should be 2/3 times the laser focus diameter to reduce heat accu-
mulation and subsequent thermal stresses and distortion, they do not guide the
magnitude of distortion at thickness variations. As a DfAM support, the AMDA
process can provide targeted design support by investigating the specific AM
process’s capability and design implications. Design artefacts can be designed to
investigate both buildability and the impact of specific design choices on other
factors, such as performance, thereby allowing a holistic and comprehensive
approach to design support. Through the prototype-driven approach, the AMDA
process acknowledges the context-dependent nature of DfAM problems, encour-
aging the creation of context-specific design knowledge rather than adhering to
pre-established general design rules and guidelines.
1.2. Purpose of the study
The purpose of this study is to evaluate the AMDA process and identify areas for
improvement. This paper begins with a background on design support evaluation,
followed by an analysis of design support literature to formulate and propose an
evaluation approach. Subsequently, the AMDA process is evaluated based on its
use in an industrial case study. The evaluation is conducted using the formulated
evaluation approach, and the AMDA process is compared against alternative
design support. The evaluation and comparison leads to a proposal for further
developing the AMDA process. The study closes with conclusions, limitations, and
future research directions.
2. Design support evaluation
The research outline of the AMDA process is illustrated in Figure 2. The obser-
vations of Dordlofva and Törlind (2020) were formulated into the AMDA process
and proposed as a process that engineers employ to aid them in AM design.
However, an evaluation of the AMDA process and its usefulness in supporting
engineers in achieving their desired outcomes has not been thoroughly done.
Additionally, such an evaluation can identify areas for further development of
the AMDA process as a design support.
2.1. Design support effectiveness, efficiency and efficacy
Blessing and Chakrabarti (2009) state that a difficulty in developing design support
is that the characteristics that make support effective depend on the user, the
Figure 2. Illustration of the AMDA research development.
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support itself, and their interaction. They review the two types of measurement
suggested by Rossi, Lipsey, and Freeman (1982) regarding efficiency in a design
support context: cost–benefit and cost-effectiveness. Understanding the financial
implications of design choices is crucial. Cost–benefit assessment, for instance, is a
measure of the monetary investment in using a support, such as a cost calculation
for achieving a particular result. On the other hand, cost-effectiveness is a measure
of the outcomes of the support, such as the cost spent to reduce development time
by x%, which may not always be easily translated into a monetary value. Hence,
creating metrics for design support effectiveness can be difficult and is relative to
the user’s needs. For example, CAD software that takes 1 h to analyse several
variables accurately to process a design solution could be seen as good in a
multinational industrial project with a 5-year development timeline. In compari-
son, 1 h for analysing a component design as part of an undergraduate student
project with a 2-week deadline could be considered an inefficient use of project
resources. In both scenarios, the solution outputted has high fidelity, but in the
second scenario, the fidelity is not required.
Daalhuizen and Cash (2021) proposed that a method’s efficacy and effectiveness
should be evaluated to understand the relationship between its content and per-
formance. They conceptualise and define method content as the internal elements of
a method and their inter-relationships. They describe efficacy as how well a method
supports the transferof knowledge to a user and the effectiveness of a method as how
well it supports designers in achieving the desired effect in context. They further state
that the efficacy is typically tested under controlled conditions, whereas the effect-
iveness is typically tested in real-life conditions. Similarly, Gericke, Eckert, & Stacey
(2022) suggest that during an evaluation, the performance or quality of a design
support is assessed by measuring its effectiveness, efficiency, and overall impact. The
validation square proposed by Pedersen et al. (2000) suggests that assessing the
effectiveness and efficiency of design support is required for method validity.
Pedersen et al. (2000) define design method efficiency as the provision of design
solutions ‘correctly’and efficiency as a method’s competent use of resources, i.e., costs
and time. Further, Pedersen et al. (2000) suggest that one reviews the design
solutions’performance to measure a design method’s effectiveness. However, it
should be noted that a poor-performing design solution does not necessarily imply
that the design method used was poor. As Roozenburg and Eekels (1996)argue,
design methods aid in searching for design solutions, but they do not guaranteethat
the desired solution will be found. Seepersad et al. (2010) build on the work of
Pedersen et al. (2000), stating that a method is effective implies that the individual
constructs of the method are acceptable, the internal consistency (how the constructs
are assembled) is acceptable and that the method is being used in an appropriate
problem that allows for the verification of the performance of the method. Seepersad
et al. (2010) additionally consider that method efficiency implies that the outcome of
a design method is useful with respect to its purpose, that this usefulness is linked to
the application of the method and that the usefulness of the method is beyond some
limited instances, i.e., in a more general sense. In examining design supports within
the works of Daalhuizen and Cash (2021), Gericke, Eckert, and Stacey (2022),
Pedersen et al. (2000), Seepersad et al. (2010), and Blessing and Chakrabarti
(2009), no author employs all three terms: effectiveness, efficiency and efficacy.
For comparison, Table 1 presents definitions of effectiveness, efficiency and efficacy
from design research literature and dictionaries.
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The AMDA process supports designers in understanding how to design a
product for AM, utilising AM’s potential benefits, ensuring buildability, and
fulfilling performance requirements. Therefore, this paper focuses on evaluating
the effectiveness of the AMDA process and defines design support effectiveness as
how well it allows designers to achieve their desired outcomes.
2.2. Approaches for design support evaluation
Approaches to the evaluation of methods have focused on the effect of the method
on the design output/outcome (Frey & Dym 2006; Daalhuizen 2014). However, it is
important also to understand the relationship between the method and its user and
its final impact when evaluating its effectiveness (Cash, Daalhuizen, & Hekkert
2023). The design research methodology (DRM) of Blessing and Chakrabarti
(2009) recommends conducting a descriptive study to validate developed design
Table 1. Design literature textbooks and dictionary definitions of effectiveness, efficiency and efficacy
Ref. Effectiveness Efficiency Efficacy
Pedersen et al.
(2000,p.4)
‘We associate usefulness of a
design method with whether
the method provides design
solutions ‘correctly
(effectiveness) …Correct in
this context are design
solutions with acceptable
operational performance…’
‘We associate usefulness of
a design method with …
whether it provides
‘correct’design solutions
(efficiency). Correct in
this context are design
solutions…, that are
designed and realized
with less cost and/or in
less time.’
No definition.
Daalhuizen and
Cash,
(2021,p.6)
‘Effectiveness relates to how well
a method allows designers to
achieve the desired effect in
context, typically tested in
real-life conditions.’
No definition. ‘Efficacy relates to how
well a method
supports the transfer
of knowledge to the
user and its effect on
the designer’s
behaviour, that is the
direct effect of the
content on a designer,
typically tested under
controlled conditions.’
Blessing and
Chakrabarti
(2009, p. 204)
No definition. ‘Efficiency of the support,
that is the cost against
the benefits.’
No definition.
Cambridge
Dictionary
(2023)
‘The quality of
being successful in achieving
what is wanted.’
‘The quality of achieving
the largest amount of
useful work using as
little energy, fuel, effort,
and so forth as possible.’
‘The ability of
something to produce
the intended result.’
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support. Validation involves the evaluation of a support’s impact and the extent to
which it improves the previous design situation. The DRM provides an approach to
support evaluation, but that evaluation method requires specific steps to be
followed during support development. Hence, assessing support against the met-
rics stipulated in the DRM will be complex if the support has not been developed
following the DRM steps exactly. AM design support evaluation approaches have
included user workshops (Kumke et al. 2018; Lindwall & Törlind 2018; Blösch-
Paidosh & Shea 2022) and user surveys (Kumke et al. 2018; Lauff et al. 2019). The
use and review of design support with designers in practice reveal the capabilities of
the design support; however, little information is provided regarding the elements
of the support that enable it to be successful (Gericke, Eckert, & Stacey 2022; Gray
2022), and no established procedure for design support assessment exists (Gray
2022). Gray (2022) described the characteristics of design methods through
different vocabularies to help identify and describe the components of methods
that are key to their performance potential. Gray (2022) built three vocabularies for
describing the characteristic qualities of methods: a codification-oriented vocabu-
lary, a performative vocabulary, and a presentation-focused vocabulary. First, a
codification-oriented vocabulary involves the shape and purpose of the method.
Second, a performative vocabulary involves the explicit inputs, potential outputs,
and mechanism of the method. Third, a presentation-focused vocabulary includes
the type of guidance, format, and medium of the method. Kumke et al. (2018) used
a workshop and post-workshop user feedback to evaluate opportunistic DfAM
support, focusing on understanding how design supports can optimally support
the user. They derived a set of evaluation criteria from research on design methods
and DfAM requirements to formulate their basis for evaluation. Several researchers
have conducted evaluations of DfAM support for early-phase design in an indus-
trial setting (Kumke et al. 2018; Dordlofva 2020; Prabhu et al. 2020; Blösch-Paidosh
& Shea 2022). Early-phase DfAM support helps designers understand AM design
capabilities (Blösch-Paidosh & Shea 2018; Lindwall & Törlind 2018). There are
a few industrial examples of evaluating DfAM support for more detailed design
stages. At the detail design stage, the designer’s decisions are related to the design
optimisation of their product and addressing specific DfAM challenges like feature
size, feature shape, eliminating features requiring support, and adding material for
post-processing operations (Pradel et al. 2018).
Evaluation of design supports against their characteristics has previously been
used to assess and compare the effectiveness of design support (Self 2011; McAtee
et al. 2009; Zhang et al. 2019; Blösch-Paidosh & Shea 2022). A characteristic is
defined as a typical or noticeable feature of someone or something (Cambridge
Dictionary 2022). Blösch-Paidosh and Shea (2022) conducted a literature survey to
identify 18 early design phase DfAM methods characteristics from 19 academic
works published between 2011 and 2019. Using these characteristics, they evalu-
ated AM design heuristics and objects used in the industry with design experts.
Blösch-Paidosh and Shea (2022) focused on the characteristics of early-phase
DfAM support alone, and their work did not provide an in-depth description.
Through a review of literature in different fields of design practice, Self et al. (2016)
developed a framework called the Five Universal Tool Characteristics (UTC) to
describe and evaluate the abilities of design tools for supporting designers. Their
five characteristics were ‘mode of communication’(the extent to which the support
communicates design ideas to others), ‘level of ambiguity’(how ambiguous an idea
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can be represented), ‘transformational ability’(the extent the tool can aid the
movement between design ideas), ‘level of detail’(how detailed a design can be
communicated), and ‘commitment’(how the tool communicates the level of
commitment to a design idea). Zhang et al. (2019) expanded the list of UTCs by
analysing literature on design tool performance. Subsequently, they used charac-
teristics as an evaluation tool through a case study to evaluate the use of digital
sketching tools in practice.
The characteristics produced by Self et al. (2016) were developed based on
designer behaviour and interaction with design tools. The UTCs were further
developed by Zhang et al. (2019) by exploring literature regarding the capabilities
designers gain from using design tools and designers’physical and mental input.
Considering that ‘good’design supports have characteristics that allow them to
effectively support the user (Self 2011; McAtee et al. 2009), this study proposes to
formulate a set of general design support characteristics derived from literature
definitions and descriptions of various design support types. Definitions and
descriptions of design support in the literature provide explicit or implicit explan-
ations of what makes design support effective. By comparing definitions and
descriptions and grouping their themes, general characteristics of different types
of design support can be identified, thereby forming a basis for evaluating the
effectiveness of design support grounded in their constructs. Additionally, design
support developed within a specific industry may have contextual relevance,
emphasising specific characteristics unique to their needs. Understanding these
contextual differences can help distinguish general characteristics from context-
dependent ones.
2.3. Design support definitions
A literature review of design support terms, definitions, and descriptions was
conducted to identify design support characteristics and provide a method to
evaluate and improve design support. Two approaches were used to identify the
definitions. First, a structured, systematic literature review was conducted using
four PRISMA review phases: identification, screening, eligibility checks, and
inclusion of articles (Moher et al. 2009). The systematic review comprised five
stages: database searches, de-duplication, an eligibility assessment, a full-text
review, and qualitative synthesis. The search term (“design support’‘AND defin-
ition) was used for the systematic review, thereby enabling the identification of
definitions and descriptions of design support in product and engineering design
across multiple industries. The objective of the literature review was to identify
articles relating to the research or development of design support that included
clear definitions of support terms or design support requirements.
The search was conducted on titles, abstracts, and keywords using the SCOPUS
and Web of Science web databases. These databases were selected based on the
breadth of subject areas and the literature types covered (Aghaei Chadegani et al.
2013). Once both databases had been searched, 161 articles were acquired, com-
piled into a spreadsheet, and screened for duplicates using conditional formatting,
following which duplicates were manually deleted. Following de-duplication,
125 articles remained, and their titles and abstracts were read to assess their
eligibility for a full-text review. The articles reviewed were published in the
10 previous years at the time of writing (2012 to 2022) and were required to focus
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on product or engineering design and design support development to be eligible for
the full-text review. Following the first round of eligibility evaluation, 16 articles
remained for the full-text review, and nine provided a precise definition or
description of the design support type under development. The search was
repeated 1 year later, and seven more papers were identified. However, none
met the eligibility criteria, and they were all excluded. A more targeted literature
review was conducted using canonical and seminal design engineering and product
development textbooks to complement the systematic review. This approach
identified design literature textbook definitions and descriptions, allowing for a
more informative analysis (Huelin et al. 2015). The scope of the definitions
searched within the targeted literature was determined by the terms identified
from the articles in the qualitative synthesis of the systematic review. The sources
were constrained to design literature textbooks and selected according to their
credibility and contribution based on their scholarly impact in the design field. The
books can be described as classics owing to their longevity; that is, the first edition
was published over 20 years ago, and the book had high citation rates; that is, they
have been cited more than 500 times (according to Google Scholar at the time of
writing). A flow diagram of the systematic literature review and the details of the six
targeted review sources are listed in the Appendix.
The definitions of design support terminology have previously been reviewed;
for example, in the work of Fu, Yang, and Wood (2016), who conducted a targeted
review to identify definitions of the terms ‘design guideline’,‘design heuristics’, and
‘design principles’to propose more formalised definitions. Various definitions of
design support exist, and multiple disciplines conduct design work. Therefore, it is
beneficial to consider the different types of design support, how they operate, and
the general characteristics that make them effective in supporting design. Owing to
the substantial diversity of design support types, reviewing design support terms
allows for the differentiation and detailing of their positive support characteristics.
Moreover, studying definitions from researchers who have developed design
support can enable an understanding of suggested considerations within design
support.
The systematic review of design support revealed definitions and descriptions
for the following design support terms: design support,design support system,
design tool,design method,design methodology,design guidelines,design heuristics,
design principles, and design guidelines. These terms were identified as the types of
design support developed or researched within the acquired articles. Once the
terms were identified, the corresponding definitions for each design support term
from the systematic review were searched in the targeted literature. Upon review-
ing the definitions, it was noted that the terms rule and procedure were commonly
used in the definitions of the design support terms. Hence, the targeted literature
search was extended to include the definitions of design rules and design procedures.
The definitions and descriptions of the design support terms obtained from the
systematic and targeted reviews are presented in Table A2 of the Appendix. Eight
terms to describe design support were identified from 16 sources. In total, the
review identified 39 descriptions and definitions of design support. The definitions
obtained were not evenly distributed in the literature; most were found in the books
of the targeted review, with 27 definitions from the books and 12 from the literature
search. The term design methodology was found the most, with nine definitions
from six sources. Then, the term design method,design support, and variations of
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both terms were identified within five sources each. Design tool was defined within
four sources, Heuristic and guidelines in three, and procedure,rules and principles
in two. There was also variation in the naming of some of the support terms. The
review identified three variations of design support and heuristic and two variations
of the terms methodology,tool and procedure.
2.3.1. Design support/support system
Three variations of the term design supportwere found in the review: design support,
support system, and process support system. Blessing and Chakrabarti (2009)
defined design support as: “all possible means, aids and measures that can be used
to improve design. These are prescriptions –suggesting ways by which design tasks
should be carried out –and include strategies, methodologies, procedures, methods,
techniques, software tools, guidelines, knowledge bases, workbooks, etc.’The definition
by Blessing and Chakrabarti (2009) of design support covers everything a designer
can use to aid design. Scurati et al. (2022) surveyed academic and industrial
stakeholders to create design support for sustainability considerations during the
early design phase. They defined eight success criteria from their interviews and
ranked them as high/medium/low according to the priority level of the criteria for
successful design support. The three high-priority success criteria were ‘communi-
cate complexity’to avoid oversimplifying the problem, ‘enable quick what-if assess-
ment loops’,and‘support tacit knowledge sharing’. Design support was described as a
system by Zanic, Andric, and Prebeg (2013)andZanic(2013), whereas Pikas et al.
(2019) defined the term design process support system. Zanic, Andric, and Prebeg
(2013) and Zanic (2013) defineda design support system as something that supports
stakeholders in their decision-making during design. In contrast, Pikas et al. (2019)
described its purpose as a means of aiding error, performance, and knowledge
management. Pikas et al. (2019) and Scurati et al. (2022) noted the need for design
support owing to the complexity of design activities.
2.3.2. Design tool
Blessing and Chakrabarti (2009) described a design tool as something that is used
in design support; that is, a design tool supports the use of a design method or
design guidelines. Other authors have defined design tools more generally. Akturk
(2017) described design tools as things that aim to aid a designer by simplifying and
connecting the theory of a product to its practical design; a design tool should
provide guidance and approachable goals to users. Yang, Ong, and Nee (2016)
defined design tools and methods as entities that help address and relieve prob-
lems. Moreover, Pahl et al. (2007) equated design tools and procedures, noting that
they support a designer in analysing their design and improving the design by, for
example, reducing the cost or improving the quality.
2.3.3. Design method
Cross (2000) defined a design method as any identifiable means of conducting
design. This definition suggests that the term design method could be used as an
umbrella term for all design support terms, such as design aids,tools, and
procedures. Design methods have also been defined as a composite of design
activities (Cross 2000; Blessing & Chakrabarti 2009) and sets of rules to be followed
(Hubka & Eder 1982; Roozenburg & Eekels 1996). Design methods support design
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by providing a path for designers to follow, which leads them towards a design
solution (Hubka & Eder 1982; Roozenburg & Eekels 1996). As suggested by the
word ‘intended’in the definition of Hubka and Eder (1982) and the phrase ‘no
guarantee’in that of Roozenburg & Eekels (1996), while a method provides a path
in the hope of moving a designer towards a design solution, design methods lack an
assurance that it will lead where the designer hopes. A design method can be viewed
as an activity that aids in moving forward within the design process but not
necessarily in the desired direction of the designer. A design method provides all
users with the same logical pathway; however, their interpretation of the method
dictates their destination. Different users of the same method that addresses the
same problem may produce different results.
2.3.4. Design methodology
The design methodology was revealed to have multiple definitions. Design meth-
odology refers to the study of methods (Roozenburg & Eekels 1996). It has been
used to describe the use of a group of design supports, such as methods, rules, and
procedures, together (Hubka & Eder 1982; Roozenburg & Eekels 1996). Further-
more, the term generally describes the overall design activity (Hubka & Eder 1982;
Blessing & Chakrabarti 2009; Delponte et al. 2015). Wollschlaeger & Kabitzsch
(2020) defined four requirements for a design methodology to support the design
of personalised assistance systems for patients. According to their requirements, a
design methodology should be efficient, customisable, and automatable and pro-
vide the designer with multiple solutions. Roozenburg and Eekels (1996) further
stated that a design methodology should encourage creativity. According to Cross
(2000), a design methodology should ensure that the design problem is fully
understood to guarantee that an excellent solution to the correct problem is
obtained. Pahl et al. (2007) stated that design methodology must emphasise the
need for an objective evaluation of the design results. Hence, a methodology should
not only guide a design solution but also encourage the assessment that the solution
is good. Furthermore, Pahl et al. (2007) defined a ‘general working methodology’as
something that should be widely applicable, independent of discipline, and not
require specific technical knowledge for its use. Pahl et al. (2007) described a
general working procedure as a systematic procedure consisting of heuristic
principles that must satisfy a set of conditions. These conditions are as follows:
‘define goals’to provide insight into the design problem and ensure motivation to
find a solution, ‘clarify conditions’by defining constraints, ‘dispel prejudice’to allow
consideration of all possible solutions, ‘search for variants’to enable the identifi-
cation of multiple solutions, ‘evaluate’using the goals and constraints that are set,
and ‘make decisions’according to the evaluation.
2.3.5. Design heuristics and design principles
Valjak and Lindwall (2021) reviewed design heuristics and principles in the context
of AM. They stated that heuristics help designers perceive the unique capabilities of
AM and can be used as creative inspiration during concept generation. Valjak and
Lindwall (2021) also noted that design principles support the early design phase
and help designers realise their designs in a suitable form for AM. Hubka and Eder
(1982) described design principles as a fundamental truth on which other laws are
dependent or from which they are derived, and as idealised methodical rules that
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guide the design process implementation. Pahl et al. (2007) used the term heuristic
principles to describe the constituents of a general design methodology and defined
a heuristic as a creativity technique or method for idea generation and solution
finding. Hubka and Eder (1982) described the term heuristic procedures in their
discussion of design engineering as a mental activity that can be considered
through the psychology of thought processes. Hubka and Eder (1982) further
defined the following principles for heuristic procedures: ‘ensure motivation’,‘show
limiting conditions’(identify constraints), ‘dissolve prejudice’to allow for object-
ivity, ‘search for variants’, and ‘reach decisions based on evaluations of maximum
objectivity’. The principles for heuristic procedures listed by Hubka and Eder
(1982) are almost identical to the conditions of a general working procedure
outlined by Pahl et al. (2007).
A notable work on design heuristics, guidelines and principles definition that was
not accounted for within the parameters of the literature review but is discussed in
the creation of the definitions by Valjak and Lindwall (2021) is that of Fu, Yang, and
Wood (2016). Fu, Yang, and Wood (2016) characterise design heuristics as the
context-dependent provision of design direction that increases the chance of reach-
ing a satisfactory, but not necessarily optimal, solution. They emphasise that
heuristics are based on intuition and/or experimental understanding, focusing on
reducing solution search time in a ‘quick and dirty’manner. Hence, they provide
generally reliable results but are fallible depending on the circumstances in which
they are being applied (Fu, Yang, and Wood 2016). Conversely, design principles,
like the definition provided by Hubka and Eder (1982), are fundamental rules that
provide design guidance towards reaching a successful solution, which has been
derived from extensive experience and evidence.
2.3.6. Design guideline
Using design guidelines in the earlier product design phase aids decision-making
when detailed information is unavailable (Ulrich & Eppinger 2012). The descrip-
tion of a design guideline provided by Yang, Ong, and Nee (2016) implies that
design guidelines efficiently address the barriers and challenges with the specific
conditions of a product design. Blessing and Chakrabarti (2009) defined guidelines
as useful for designers to follow to achieve their design objectives. Furthermore,
Blessing and Chakrabarti (2009) combined the terms rules, principles, and heur-
istics under the label of design guidelines. Notably, for a clearer differentiation
between design guidelines principles and heuristics, Fu, Yang, and Wood (2016)
describe design guidelines as a characteristic blend of design principles and
heuristics. They assert that while guidelines are based on extensive experience,
they are not fundamental; instead, they are context-dependent means of directing
the design process to reach a successful solution.
2.3.7. Design procedure and design rule
Through a review of the obtained definitions, the words procedure and rule were
observed to be common in the definitions. Hence, the targeted literature search was
expanded to include definitions of the terms design rule and design procedure for a
deeper analysis of design support. The term procedure was found in the definitions
of design support, design method, design methodology, and design tool. Pahl et al.
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(2007) stressed that it is important to define a design procedure to identify good
design solutions, but that a design procedure should also be flexible, plannable,
optimisable, and verifiable. They also highlighted that a designer must work
systematically and already have knowledge of their field to realise a design
procedure.
The word rule was found in the definitions of design method, design method-
ology, design principle, and design guideline. Design rules can be used to com-
municate constraints to a designer (Ulrich & Eppinger 2012). According to
Roozenburg and Eekels (1996), design rules can be of two types: algorithmic and
heuristic. Algorithmic rules are unambiguous and set in order, leading to a precise
result. In comparison, a heuristic design rule promotes the determination of a
result but leaves room for creativity and serendipitous results (Roozenburg &
Eekels 1996).
2.4. Design support characteristics
Through analysis and summarisation of the design support definitions obtained,
11 characteristics were identified and are presented in Table 2.
The effectiveness of design support relates to how well the design support
allows designers to achieve the desired outcomes, that is finding a design solution
with an acceptable operational performance. Additionally, effective design support
can find a solution even with minimal detail and in a short time, often defined
intuitively as a certain percentage of the project time. Though the time saving from
using the design support is relative, a characteristic of design support is to prevent
time wastage by enabling quick and iterative provision of solutions and analyses as
required (Scurati et al. 2022). The solutions should be ‘correct’enough to aid
decision-making towards the next step in the design process (Zanic 2013). In
doing so, the design support outlines the steps needed to advance in the design
process. It provides a path to either the correct solution or a functional solution or
Table 2. Design support characteristics
Characteristic Description Supporting literature
1. Aids decision-
making
Design support should aid designers in making
well-informed design-related decisions
Ulrich and Eppinger (2012); Zanic
(2013); Zanic, Andric, and Prebeg
(2013)
2. Emphasises the
need for
evaluation
Evaluation is a high-priority criterion for
design support. A designer should be able to
assess the likely outcome of their decisions
based on the design support results. Hence,
an objective evaluation of the design support
results should receive particular focus within
the design support structure, and design
support should ensure that the evaluation
and assessment of results are clearly defined
activities.
Hubka and Eder (1982); Pahl et al.
(2007); Scurati et al. (2022)
Continued
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Table 2. Continued
Characteristic Description Supporting literature
3. Communicates
constraints
Design support should assist the designer in
identifying the constraints and clarifying the
feasible solution space.
Hubka and Eder (1982); Pahl et al.
(2007); Ulrich and Eppinger
(2012); Yang, Ong, and Nee (2016)
4. Aids creativity Design support should encourage creativity
during the process of idea exploration when
generating design solutions.
Roozenburg and Eekels (1996); Pahl
et al. (2007); Valjak and Lindwall
(2021)
5. Provides a path Design support should aid designers in moving
forward during the design process and lead
them towards a design solution.
Hubka and Eder (1982); Roozenburg
and Eekels (1996); Cross (2000);
Pahl et al. (2007); Blessing and
Chakrabarti (2009); Delponte et al.
(2015)
6. Communicates
complexity
Design support should be capable of
informing designers of all relevant factors
that affect and are affected by their design
decisions, thereby capturing the reality of
the design situation without
oversimplification.
Pikas et al. (2019); Valjak and
Lindwall (2021); Scurati et al.
(2022)
7. Supports
knowledge
management
Design support should facilitate knowledge
management to prevent errors during the
design process. Additionally, the sharing and
communication of all available knowledge that
can be used to solve design problems should be
encouraged through design support.
Blessing and Chakrabarti (2009);
Pikas et al. (2019); Scurati et al.
(2022)
8. Is quick and
iterative
Design support should enable designers to
predict outcomes for the most informed
decisions, preventing designers from wasting
time through quick decisions and allowing
rapid progress.
Scurati et al. (2022)
9. Defines goals
(ensures
motivation)
Design support should assist a designer in
describing their design’s goals and assessing
whether they are achievable. The designer
then becomes assured of their motivation to
use the design support to find design
problem solutions.
Hubka and Eder (1982); Pahl et al.
(2007); Blessing and Chakrabarti
(2009); Akturk (2017)
10. Is objective An unbiased evaluation of the results must be
possible for the design support to be effective.
Hubka and Eder (1982); Pahl et al.
(2007)
11. Finds a
solution and
allow for
multiple
solutions
Design support should not internally pre-
specify a solution but facilitate the
identification of all possible solutions within
the frame of the design problem that is
presented. The quality of the solutions
depends on the quality of the problem
description; however, even with little detail,
the design support should provide workable
solutions.
Hubka and Eder (1982); Roozenburg
and Eekels (1996); Cross (2000);
Pahl et al. (2007); Wollschlaeger
and Kabitzsch (2020)
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helps eliminate poor solutions, thereby narrowing the scope of solutions. However,
in narrowing the solution scope, design support should not have an internal
construct that leads to a pre-specified solution. The inputs should affect the output
and allow multiple solutions to be found when feasible. An example of exhibiting
this characteristic would be CAD software, which allows for many solutions
depending on how much input a designer gives. Conversely, a website design
template with limited customisation options would not.
Internal evaluation constructed within the design support was found to be a
high-priority characteristic of the design support description (Hubka & Eder 1982;
Pahl et al. 2007; Scurati et al. 2022). Therefore, there should be an emphasis on the
need to evaluate solutions within the support (Pahl et al. 2007). For example,
design supports that present data on design considerations like structural integrity
exhibit this characteristic well, as the supports enable a designer to evaluate if the
solution will meet their design requirements. The design support and solution
evaluation should also be objective (Hubka & Eder 1982; Blessing & Chakrabarti
2009). A designer should be able to assess the design support solutions without any
influences or preferences not set by the designer. Design supports developed by a
specific organisation may have a bias for design styles or a specific design approach
within their design support. If not aware of these internal preferences when
presented with the results through the support, they may be influenced to evaluate
a solution that meets those preferences more highly.
Design support should allow for a clear and concise understanding of feasible
designs within the design problem (Pahl et al. 2007; Ulrich & Eppinger 2012). AM
nesting tools enable users to prepare their builds within models of their AM
machine. These tools make the user aware of constraints impacting their print,
such as build volume and allow them to evaluate these constraints, thus exhibiting
the characteristic of communicating the constraints of a design to the user.
Further, design supports should communicate the complexity of design solutions
to reduce errors (Pikas et al. 2019). AM build preparation tools have features that
highlight recommended zones for support structures, showing a designer where a
design feature may be unfeasible to build through the software’s understanding of
build complexity. Some AM design supports simplify these complex decisions
automatically by inserting support structures or adjusting the design orientation.
Design support should communicate the complexity to a designer but also allow
them to make their own decision on if, for example, support structure is required
for a section. Further, effective design support aids the creativity of the designer by
keeping their design options open (Roozenburg & Eekels 1996). Oversimplification
of the complexities of the design can take away some of the creative freedom of a
designer and prevent serendipitous design solutions.
In using design support, the designer should be able to articulate and commu-
nicate the objectives of their design clearly, that is the support should help define
the goals of the design (Pahl et al. 2007; Akturk 2017). When designing a product, a
goal could be reducing manufacturing time by x%. Effective design support for this
goal in AM would provide information on factors like print time or required post-
machining area (Ahn, Kim, & Lee 2007), enabling a designer to evaluate the
feasibility of their x% goal and ensure the designer is motivated to achieve
it. Design support is further effective when the knowledge of the feasibility of
solutions, the constraints of design spaces and the complexities impacting a
solution is shared and managed through it. By supporting knowledge
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management, design support reduces the chances of users making errors (Pikas
et al. 2019). Design teams have tools such as collaborative platforms that can be
integrated into design software to share learnings. Similarly, open-source design
guideline libraries can be integrated within design supports to help communicate
an up-to-date understanding of design knowledge and solutions.
Reviewing the definitions in section 2.3 led to identifying characteristics of
effective design support; that is, characteristics that make design support ‘good’.
These characteristics differ from those identified by Blösch-Paidosh and Shea
(2022), who focused on characteristics for early-phase DfAM evaluation. Some
of their characteristics are general and are applicable for evaluating the AMDA
process, such as ‘is easy to use’,‘is easy to learn how to use’, and ‘structured in an easy
to understand way’[sic]. Other characteristics, such as ‘Provides information in a
variety of formats’and ‘Provides the information necessary early in the design
process’, are more specific to early-phase DfAM support evaluation. There are also
similarities in the characteristics identified by Blösch-Paidosh and Shea (2022) and
this work, such as ‘structured in a useful way’[sic] and the design support
characteristic of provides a path. Both characteristic descriptions refer to the ease
at which a user navigates and is guided towards design knowledge through design
support. Similarly, their characteristics related to AM ideas generation, such as the
support increases the ‘number’,‘quality’,‘variety’or ‘novelty’of AM ideas gener-
ated, are similar to the aids creativity and finds a solution and allows for multiple
solutions design support characteristics. These characteristics are related to design
support encouraging variations in design solutions.
Assessing the general applicability is essential in defining and proposing
general design support characteristics. This assessment ensures that the charac-
teristics are not dependent on the application area or specific type of design
support. A review of the sources of the literature was conducted to evaluate this
and examine the distribution of the characteristics across the different types of
design support and their development contexts. The review revealed that of the
39 definitions and descriptions of design support terms, 27 came from design
literature textbooks, providing definitions in a general context. The remaining
12 definitions were sourced from discipline-specific literature on design support
development in AM, healthcare, architecture, sustainability, ship manufacturing
and construction. Thus, the identified characteristics could have been influenced
by the design support development research context, potentially making them
specific to particular contexts. Upon reviewing for potential contextual biases, it
was found that the characteristics were distributed across the various design
support types and development contexts, signifying their general applicability.
However, some characteristics were notably associated with specific design support
types. The characteristic ‘provides a path’was noted most frequently, identified
from 12 definitions. It was especially prominent in design method and method-
ology, with five and four instances respectively. The next most general character-
istic for design support was ‘Finds a solution and allows for multiple solutions’,
noted seven times, with three instances for design methodology and two for design
procedure. Other characteristics were each identified in no more than two defin-
itions across various types of design support, with the characteristic ‘define goals’
noted twice in the definitions of design tools and ‘aids creativity’noted twice for
design heuristics. The generalisability of the 11 characteristics means that they can
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be used to evaluate a wide range of design supports across different contexts and
applications. However, the findings indicate that the specific type of design support
being developed influences which characteristics are particularly important or
expected by users. Therefore, when using general design support characteristics for
evaluation, it is important to recognise that not all characteristics will hold equal
importance in every situation. Thus, consideration should be taken of the kind of
design support being developed and its objectives when evaluating the various
characteristics, as some may conflict. If, for example, a design support prioritises
offering a distinct, clear, and detailed path towards finding a design solution, this
might inhibit the designer’s creativity, potentially reducing the support’s ability to
aid creativity. Similarly, if design support is intended for use within a limited time,
the characteristic of quick and iterative becomes more relevant for its evaluation.
However, design support that is quick to use and easy to repeat may not fully
communicate the complexity of a design problem due to the limited time available
to process or define the problem in detail.
The review of the definitions provided valuable insight into characteristics for
consideration when developing design support. Further, the review highlighted the
variety of available design support types and the complexity of their content and
structures, providing details on the distinctive features that differentiate the design
support types. The 11 characteristics listed in Table 2 are initial proposals. Further
research will be necessary to validate them. However, understanding these differ-
entiating features of design support can aid in evaluating and developing design
support with appropriate structure and naming, depending on the goal of said
support.
3. AMDA case study
A detailed description of the implementation of the AMDA process is required to
evaluate its effectiveness as a design support using the 11 identified design support
characteristics. The following section presents the background of the design issue,
which is the context for the AMDA evaluation. First, the description of a unique
AM design uncertainty related to a design feature in a rocket engine turbine
manifold with a consolidated design, illustrated in Figure 3, is presented.
Figure 3. A rocket turbine manifold with an integrated stator.
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3.1. LPBF surface roughness design considerations
Although AM technologies such as LPBF provide design engineers with new
geometric freedoms, there are still limitations to this freedom. Complex geometries
may require support structures to ensure a successful build when taking advantage
of the geometric complexity that LPBF offers. In many cases, the support structure
is impossible or impractical to remove or adds unnecessary post-processing that
diminishes the advantages of AM (Zink et al. 2020). An example of accessibility
issues of support structure removal is LPBF part designs with enclosed inner
volumes, making reaching the internal areas challenging (Obilanade, Törlind, &
Dordlofva 2022). When using LPBF for manufacturing parts with unsupported
areas, the newly printed material in the unsupported area overhangs the powder,
and the melt pool may sink into the powder bed below, mixing with unsolidified
powder and cooling, causing rough surfaces and dross formation (Charles et al.
2022). Dross is an LPBF phenomenon that mainly occurs on the down-facing
unsupported and overhanging surfaces of LPBF parts owing to insufficient heat
transfer and, as illustrated in Figure 4, may result in a reduction in the dimensional
accuracy (Kokkonen et al. 2016). Furthermore, when two separately built part
islands meet, the residual stresses at their junction layer pull the material islands
together, disrupting the underlying part and powder, thereby increasing rough-
ness. This phenomenon is known as transversal shrinkage, and as with dross
formation, it causes rough surfaces and geometric deviations from the part design,
as illustrated in Figure 4. Hence, the roughness of surfaces is intrinsically linked to
design choices.
Down-facing overhang areas, such as the inner roof of the manifold depicted in
Figure 3, may be critical for a part, as the surface roughness is high in these regions,
which can negatively impact the part’s mechanical properties (Dhansay, Tait, &
Becker 2014). Post-processing methods are often used to improve the surface and
performance (Sagbas 2020). For example, Kahlin et al. (2020) found that using
centrifugal finishing, shot peening, and linishing increased the fatigue strength of
rough as-built LPBF Ti-6Al-4 V test specimens by 125%, 70% and 25%, respect-
ively. However, consolidated parts can make post-processing difficult due to the
challenge of accessing enclosed areas. The surface roughness complicates the use of
Figure 4. Illustration of an unsupported roof section with geometric inaccuracy owing to partially melted
powder, inspired by Gumbleton et al. (2021).
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LPBF as it presents challenges for characterisation and analysis; hence, there is a
need for more standardised data on the effect of the surface finish on the
mechanical performance of the parts (Diaz 2019).
Research and development of DfAM support for addressing LPBF surface
roughness is ongoing. For example, Zhou et al. (2021) have developed design
guidelines and a friction factor prediction model for calculating the pressure loss in
LPBF-fabricated fluid channels. Other DfAM support for surface roughness
focuses on aiding the designer in selecting the optimal build orientation to
minimise surface roughness (Ahn, Kim, & Lee 2007; Azar et al. 2021). However,
such supports are ineffective when, for example, size limitations or other factors
restrict the orientation. Furthermore, few standards are available regarding under-
standing the surface finish requirements of LPBF parts (Lee, Nagalingam, & Yeo
2021). Moreover, the variation in the thermal history of AM parts causes concerns
regarding the mechanical properties generated from standard test specimens when
designing and evaluating load-bearing AM components (Pegues et al. 2018).
Therefore, it is necessary to develop design procedures and standards to better
understand the relationship between the AM test specimens and part performance
(Yadollahi & Shamsaei 2017).
3.2. AMDA industrial case study
Dordlofva and Törlind (2020) studied the use of the AMDA process as engineers at
a space industry company used it to investigate uncertainties related to designing
the rocket engine turbine manifold depicted in Figure 3. The consolidated design
with an integrated stator outlet implied an unsupported inner design due to
concerns about support removal. The designers identified the unsupported roof
of the enclosed internal volume as a critical design feature and used the AMDA
process to investigate LPBF design limits regarding unsupported roofs. The
purpose of the investigation was to obtain an additional understanding of LPBF
unsupported roof design limits and the impact of surface roughness on fatigue and
investigate alternative testing methods for verification/validation. Dordlofva &
Törlind (2020) conducted an analysis of the engineers’use of artefacts to investi-
gate the relationship between LPBF process limits, design choices, surface rough-
ness and fatigue. The engineers began by evolving the roof design by designing a
series of artefacts to explore the buildability limits of unsupported roof geometries
(RG), as shown in Figure 5.
The build results of printing the RG artefacts showed the machine’s capability
to manufacture unsupported geometries and enabled the identification of an
appropriate unsupported roof geometry for the product. Once an appropriate roof
geometry was identified, the design uncertainty of the impact of the roof surface
roughness on the mechanical properties was investigated through artefact A,
shown in Figure 6 (left), and its geometry is detailed in Table 3. Artefact A was
printed and fatigue tested at two radii, R1 and R2. R1 represented the unsupported
roof radius, and R2 represented a radius printed parallel to the build direction, that
is, supported from the build plate. The fatigue testing results on the two different
radii indicated a negative impact of surface roughness on fatigue life (Dordlofva &
Törlind 2020).
As explained by Dordlofva and Törlind (2020), artefact A was designed to
evaluate an alternative test method for investigating the roof radius (R1) surface
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Figure 5. Roof geometry artefacts designed to test the machine’s capability for manufacturing potential roof
geometries, revised from Dordlofva and Törlind (2020).
Figure 6. Diagram of artefact iterations A (left) and B1/B2 (right) (Obilanade, Törlind, & Dordlofva 2022).
Table 3. AMDA artefact geometries (all internal angles = 90°) (Obilanade, Törlind, & Dordlofva 2022)
Artefact
Diagonal width, d
(mm)
Artefact width, w
(mm)
Thickness, t
(mm)
Inner radius, R
(mm)
A451284
B1 (as-built) 45 12 8 4
B2 (to be machined) 44 12 8.5 4
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condition and to compare it to a smoother radius, the reference radius (R2). The
investigation aimed to understand the fatigue behaviour and predictability of
R1/2. Hence, a similar, but not identical, radius value to the selected design was
deemed sufficient for this investigation. On visual inspection of the A artefacts,
the R1 radii had a substantially rougher surface than that of R2, as observed in
Figure 7(a)and(c).
R1 and R2 of artefact A were measured. The average radius for the printed
artefacts was 2.7 mm and 3.9 mm for R1 and R2, respectively (Obilanade, Törlind,
& Dordlofva 2022). Figure 7(a) shows that R1 had a high level of dross formation,
thereby creating a much rougher surface than R2. In R1, transversal shrinkage
occurred, as indicated by the distortion line at the roof junction layer in Figure 7(b),
leading to the geometric deviation from the design. The fatigue testing results
suggested that the geometry and surface roughness affected the artefact mechanical
properties. However, a transition line from a smooth to a rough surface was
observed where the artefact arm design changed from the R2 radius to an
unsupported 45° overhang surface, as seen in Figure 7(c). The abrupt change in
the surface condition at the transition line is believed to have caused the point of
fatigue failure of R2 to occur off-axis, as can be observed from Figure 7(d). Due to
the off-axis fatigue failure, the engineers concluded that the artefact fatigue results,
although informative, lacked a comparison between the roof radius and the
reference radius. Consequently, these results did not accurately represent the
design uncertainty under investigation. Thus, they designed and printed
artefact B, as described in Figure 6 (right) and Table 3, to better focus the artefact
Figure 7. (a) Artefact A’s surface condition post-test roof radius R1, (b) roof radius
after fatigue testing, (c) surface condition of reference radius R2 (oriented bottom-
up), and (d) reference radius after fatigue testing (Obilanade, Törlind, & Dordlofva
2022), [Courtesy of P. Åkerfeldt, Luleå University of Technology].
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design on the studied uncertainty, i.e., the comparison of the impact of surface
roughness in the roof radius and reference radius.
Artefact B was designed with two significant changes: a tilt and a square
geometry. The tilt was introduced to counteract the impact of transverse shrinkage
and reduce dross formation at R1. With a tilt, the R1 junction plane gradually
connects over several layers, thereby reducing the impact of thermal stresses as
these layers cool (Kokkonen et al. 2016). The square geometry of artefact B was
designed to prevent R2 off-radius failure at the roughness transition line by causing
the failure to occur at the weakest point on the artefact, the radius. Furthermore, the
scope of investigation for artefact B was expanded to include a verification study of
the obtained results. Thus, artefact B had two designs, as shown in Table 3. The B1
design was fatigue tested in the as-built condition, whereas the B2 design was
machined using milling to obtain the same dimensions as B1 before fatigue testing.
An indication of the material properties of the design geometry could be obtained
by testing and comparing results from the two conditions. This would help
determine whether the performance of the feature could be better understood
with these artefacts.
The B artefacts were cut at two locations in preparation for fatigue testing:
opposite the R1 roof radius and opposite the R2 reference radius. The positions of
the cuts are denoted as CR1 and CR2, which correspond to their related radii, as
illustrated in Figure 8(a). A cyclic tensile load could be applied to the radius
opposite the cut by applying the load perpendicular to the location of the cuts,
as depicted in Figure 8(b). The printing and fatigue testing for both artefacts A and
B was conducted using the same LPBF machine and fatigue test settings.
4. Evaluation of the AMDA process
The identified design support characteristics are used in this section to evaluate the
effectiveness of implementing the AMDA process. This article defines design
support effectiveness as its ability to help designers achieve their desired outcomes.
Therefore, the AMDA process results are also evaluated against alternative design
Figure 8. (a) Image of the B artefact indicating the radius notations R1 (roof) and R2
(reference) and the cut locations for radius investigation, namely CR1 (cut to
investigate R1) and CR2 (cut to investigate R2). (b) Diagram of the applied cyclic
load for examining the artefact radius.
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support approaches, i.e., what would have happened during the case study if only
existing DfAM methods and guidelines had been used. Blessing and Chakrabarti
(2009) suggest that to evaluate the state of improving the effectiveness of design
support, one needs to either compare a design situation before and after using
support or compare the situation with and without the support. By comparing the
case study results (supported by the AMDA process) with alternative designs
(using existing methods), one can better deliberate on the added value of the
design support (Daalhuizen 2014). Suggestions for developing the AMDA process
are then proposed based on the evaluation of its use in the case study. An overview
of the AMDA process case study is provided in Table 4.
4.1. Aids decision-making
The designers in this study used the RG artefacts to evaluate unsupported roof
designs. The results obtained from the RG artefacts gave the designers a clearer
understanding of the unsupported design limits; hence, the RG artefacts aided
decision-making by providing the designers with an initial proof of concept for the
roof geometry during the embodiment phase.
The designers could have found a solution from the self-supporting hole
designs of Thomas, Computer, and Product (2010) or Diegel, Nordin, and Motte
(2019), although both suggest a maximum unsupported wall angle of 45°. Alter-
natively, Kranz, Herzog, and Emmelmann (2015) and Diegel, Nordin, and Motte
(2019) suggest 12 mm and 8 mm as a limit for unsupported channel diameters,
respectively. Diegel, Nordin, and Motte (2019) also offers a picture series on the
impact of unsupported angles on an LPBF part and a guide to maximum overhang
angle for various materials. Standards such as ISO/ASTM 52910 (2017) and
ISO/ASTM 52911-1-19 (2019) describe the issue of self-supporting features but
direct to the use of support structure or orienting the part. ISO/ASTM 52911-1-19
(2019) states a self-supporting limit range from 30° to 45°. While these design
guidelines, standards and 45° limit provide valuable design support information,
they are limited in their application by factors like material, machine and specific
product requirements.
The results obtained from the RG artefacts overcome this issue. They indicated
to the designers the feasibility of designing wall angles less than 45° and provided
insights on the condition of the down-skin surfaces at various roof designs. These
Table 4. AMDA process overview
Artefact Roof geometry (RG) Artefact A Artefact B
Identified uncertainty Design limits of self-
supporting overhangs
Internal surface roughness and its impact on
mechanical properties
Artefact Design
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insights enabled the designers to make informed decisions on a feasible design,
considering both product-specific geometry and process-specific capability. The
AMDA process in this case study strongly aided decision-making as the RG
artefacts enabled the selection of a feasible solution with more context than had
the alternative guidelines or standards been followed. To better aid decision-
making, the AMDA process could include a more detailed and structured
approach to the recording and presenting of artefact results, directing the user to
conduct detailed documentation.
4.2. Emphasises the need for evaluation
The AMDA process is based on an iterative design-build-test loop where evalu-
ation is explicitly part of the DfAM process. The direction for evaluation comes at
the end of the process, in the fourth stage. However, the case study highlighted that
consideration for evaluation should begin earlier in the process.
Artefacts A and B were used to investigate internal surface roughness and its
impact on design adherence and performance of the design feature. To investigate
the design adherence, the engineers measured the R1 and R2 radii of the B1
artefacts and found the average deviation to be less than 10% and less than 5%,
respectively, improving the adherence of the radii of artefact A. Additionally,
transversal shrinkage was less pronounced at the R1 roof radius of artefact
B. Thus, the design changes between the iterations of the AMDA process produced
a design artefact more representative of a 4 mm unsupported roof radius than
artefact A, as shown in Figure 9(a).
The resultant surface condition was mainly attributed to the expected dross
formation in this area. As observed in the case study, further iterations of design
artefacts are required if the results of an artefact investigation are still not con-
sidered representative. Of the 10 printed as-built B1 artefacts, six were cyclically
fatigue tested. Additionally, three machined B2 artefacts were built and cyclically
fatigue tested. After fatigue testing, failure was observed to have occurred along the
radial axis, as shown in Figure 9(b), leading to the artefact results being deemed
representative of the uncertainty investigation (roof radius vs reference radius).
Figure 9. B1 artefacts: (a) B1-R1 (as-built roof) and (b) B1-R2 (as-built reference) post-fatigue testing,
indicating on-axis failure.
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The case study underscored the importance of evaluating design artefacts to
ensure that the artefact represents the investigated uncertainty, providing feedback
that reduces uncertainty. This necessity was demonstrated in the case study where
artefact A failed to represent the uncertainty correctly. Although the AMDA
process already includes an ‘evaluate and test’stage, the review and case study
highlighted the value of elaborating on the evaluation further. Given that the
evaluation of whether the artefact results adequately represented the uncertainty
was based on the subjective judgement of the designers rather than objective
measurement, it is difficult to definitively determine that the evaluation goal had
been achieved. For the AMDA process to emphasise evaluation, the ‘Design
AMDA’stage could be detailed by adding the requirement for the designer to
express the design rationale for the artefact (what the designer wishes to learn)
through the setting of test evaluation criteria. These criteria should be constructed
to tell the designer whether their evaluation is sufficient or whether another AMDA
iteration is required during the ‘Evaluate/test’stage. The evaluation criteria and
assessment table for the performance investigation of this study could have
resembled Table 5, with a pass/fail assessment of the degree to which the artefact
represented the design uncertainty. The uncertainty could have been described as
the degree of tolerable geometric deviation from the radius value X. The addition of
setting the evaluation criteria during the artefact design stage aids the designer’s
decision-making by simplifying the act of evaluating and identifying unknown
uncertainties.
4.3. Communicates constraints
The AMDA process is intended to aid the designer in understanding the restric-
tions and limitations of the LPBF process. In this case, the designer wishes to
understand how they could consider the design limits of the self-supporting
overhangs, the internal surface roughness and its impact on mechanical properties.
In this study, the RG artefacts were used to explore the limitations of the AM
solution space, that is testing the buildability of different roof geometry designs.
Hence, an understanding of the machine’s capabilities to build self-supporting RG
was communicated, and AMDA-driven specifications (constraints) were created.
The results from artefact A highlighted how surface roughness can impact per-
formance, communicating a potential material property constraint on the artefact
design feature. Artefact B was designed to provide further insight into the impact
on design feature performance. Together, the investigations of both artefacts
communicate potential design adherence outcomes and effects on the material
Table 5. Test evaluation criteria and pass/fail table for the performance uncertainty case study
Criteria Artefact A Artefact B
Requirement Value Result Pass/Fail Result Pass/fail
Average radius X +/0.5 mm R1 X=X1 mm Fail R1 X=X0.2 mm Pass
R2 X=X0.1 mm Pass R2 X=X0.1 mm Pass
On-axis failure? Yes No Fail Yes Pass
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properties of the design feature, exploring an alternative method for feature
verification. The AMDA process successfully provided insights into the build-
ability constraints and surface roughness implications on the performance of an
unsupported design feature.
Other AM design support provides guidance on the constraints of the pro-
cesses. However, in doing so, support can sometimes hinder the full exploration of
AM’s capabilities. For instance, in the design method proposed by Orquéra et al.
(2017), it is noted that not all functional surfaces can be attained using AM. This
suggestion does not communicate the real constraints, but instead, Orquéra et al.
(2017) describe the limitation and suggest that a designer must identify if machin-
ing is needed on a surface during design. They advocate for using post-processing
to overcome surface finish issues, a recommendation also suggested and guided by
Salonitis and Zarban (2015). In this case, post-processing would be complex, so the
designers have used artefacts to explore the limitations of their design. Kranz,
Herzog, and Emmelmann (2015) provide a guide on the average roughness for
parts through a table of surface roughness at various angles from the horizontal.
However, the table was created specifically for Ti6Al4V LPBF parts. Hence,
depending on the material used, the guidance may not be directly applicable. Their
guide also suggests orienting the radius of a part to the y–x plane to mitigate the
staircase effect, which would not have been feasible in the case study. They further
highlight the roughness variability across part shapes, materials, and machines,
advocating using small radii for better surface quality.
A key constraint of DfAM supports is their tendency to be highly general or
applicable only within specific contexts, such as particular machines, processes, or
materials. The underlying idea of the AMDA process is that it should be a relatively
quick and iterative process. Consequently, the results depend on the artefact’s
design, the selected material, and the process. Ultimately, it will only communicate
constraints within a limited scope of the solution space. The AMDA process
complements both general guidelines and parameter studies, contributing to
broader knowledge and a specific understanding of identified design uncertainties.
To learn from others’results, designers must understand the constraints of the
design knowledge. Clarifying each stage of the AMDA process with more detailed
steps and a greater emphasis on documenting decisions and reasoning will enhance
the communication of the design knowledge developed.
4.4. Aids creativity
The RG artefacts were built to inspire a self-supporting roof geometry design and
are depicted in Figure 5. These artefacts aided the creativity of the engineers by
allowing them to explore the design space and test the buildability boundaries,
thereby enabling them to find a design that challenges the 45° overhang rule that
can be found in many design guidelines and standards (Kokkonen et al. 2016;
Diegel, Nordin, & Motte 2019; International Organization for Standardization
2019). The AMDA process mainly aids creativity in the first phase (inspiration),
where the engineers used the RG artefacts to explore which designs and radii were
buildable. The RG artefacts enabled an investigation into opting for a part con-
solidation design approach. The A and B artefacts were created in the second phase
(evolve) and were designed to evaluate and inspire new (creative) ideas for
verification/validation. They focused on investigating the implications of a creative
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design choice. Thomas, Computer, and Product (2010) outline a range of rules for
achieving geometric accuracy through oversizing a part design to enable material
removal in the post-processing stage. Oversizing increases costs and manufactur-
ing time by requiring additional post-processing to achieve the desired dimensions,
and such an approach would lessen the foreseen benefits of using AM in this case
study.
The designers used the artefacts to explore creative solutions and investigate the
capabilities of AM. The process output was design knowledge on the practical
feasibility of their creative solutions. Design supports can aid creativity by being a
means to learn from others, helping designers to understand the boundaries of
what is achievable and what has not yet been achieved. A wealth of AM design
knowledge is generated in mechanical testing and industry-specific literature that
is not documented or shared in tangible ways by designers. Hence, designers can
make errors and waste time when being creative due to a lack of awareness of their
design’s feasibility despite the available useful knowledge. Hence, to strengthen the
aids creativity characteristic in the AMDA process, documentation of design
knowledge should be an explicit activity through the loop, helping the designers
and others explore future creative ideas.
4.5. Provides a path
The AMDA iteration loop provides a logical path for the designer to reduce design
uncertainties. The RG artefacts provided a path towards a roof design solution by
aiding the designer in exploring possible design solutions when using the LPBF
process. The design solution would likely not have been achieved if the general
DfAM guidelines were followed (owing to the 45° limit). Artefacts A and B led the
designer to gain knowledge regarding the LPBF process and the specific design
features. Additionally, even if the AMDA process provides a path of identifying
uncertainties, the designers are responsible for developing an artefact that satis-
factorily represents this uncertainty: ‘Selecting the focus of a prototype is the art of
identifying the most important open design questions’according to Houde & Hill
(1997, p. 368). As shown in the case study with artefacts A and B, several iterations
may be required to reach the intended outcome.
4.6. Communicates complexity
The AMDA process directs a designer to break down the complexity by identifying
the most important uncertainties and how to remedy them. In this manner, the
problem is simplified so that it can be investigated quickly. As the artefact is
printed, tested, and evaluated, designers learn whether the design uncertainty is
sufficiently embodied in the artefact or reveals additional complexity (unknown
unknowns). Through the case study, the designers gained an understanding of the
complexity involved in manufacturing their geometry.
Regarding the communication of complexity for manufacturing LPBF geom-
etries, other design supports recommend zones of angles for manufacturing. Wang
et al. (2013) state that the overhang angle needs to be greater than or equal to 40° for
a“stable fabrication zone”.Within 40° to 35°, they designate the “critical fabrication
zone”, and anything below 35 degrees falls into the “hard fabrication zone.”They
further communicate the complexity of the design decisions through suggestions
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of the various process parameters that can be optimised, such as laser energy, to
reduce the powder adhesion to the surfaces. Similarly, Thomas, Computer, and
Product (2010) advise that the angles should range from around 20° up-facing to
45° down-facing and state that surfaces under 45° require support structures. The
design supports of Wang et al. (2013) and Thomas, Computer, and Product (2010)
communicate the complexity of manufacturing AM geometry in a general manner.
DfAM support tools and methods acknowledge the complexity of machine-
material-geometry relationships by stipulating the limitations of their applicability.
This complexity becomes apparent through the evaluation of design artefacts, as
the systematic approach allows the communication of the complexity of the
machine-material-geometry relationship as the designer considers their uncer-
tainty. The communication of complexity would be better exhibited by the process
with more detailed step-by-step guidance rather than the simple logic loop in its
current form. More straightforward guidance on the steps for the loop stages will
help users navigate and understand the complexities associated with describing
and investigating their uncertainty. Coupled with the setting of test evaluation
criteria, the designer will also be better directed to identify unknown uncertainties
when reviewing their results.
4.7. Supports knowledge management
The AMDA process has been used to explore possible design solutions (validating
the buildability of unsupported roof sections) and to acquire knowledge regarding
the impact of the surface roughness (the performance indications provided by
artefacts A and B). In the case study, most of the knowledge was gained in the
evaluation/test stage. Comparing the AMDA process with other more general
design methods and tools reveals the absence of a step for documentation and
knowledge generation. In this documentation step, knowledge synthesis and
generalisation are performed to identify and understand the implications of the
results. This step involves interpreting the data in the context of the experimental
conditions and comparing it with existing knowledge or predictions. Based on this
understanding, designers can develop generalised models or theories that predict
behaviour or outcomes beyond the specific conditions of the initial experiments.
Knowledge of the influence of other factors would allow for the generalisation of an
AMDA study’s results when applied in a similar context. However, the window for
generalisability could be narrow, particularly if factors are radically changed.
Other design supports require the user to capture and input factor values of
their design to utilise the design support. For example, Piscopo, Salmi, and Atzeni
(2019) conducted an experimental analysis of AlSi10Mg LPBF AM parts to
propose an equation that relates overhang length, the angle from the horizontal
(α) and the surface curvature to calculate a surface quality index, suggesting that a
surface quality index of less than 0.4 indicates a ‘good’surface. They advise that, for
αlower than 37.5°, the combination of overhang lengths less than 6 mm and low
curvature leads to favourable results. Their design support supports knowledge
management by requiring the designer to record the different variables that impact
their design decision. A designer can then refer to the record variables and the
knowledge generated to modify their design appropriately. While, like Diegel,
Nordin, and Motte (2019), Piscopo, Salmi, and Atzeni (2019) offer a general figure
as a guide for the effect of varying the build angle on the surface roughness, the
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figure’s applicability to the material and machine settings of the case study is
uncertain, unlike design artefact results.
Boyard et al. (2014) suggest leveraging a CAD database to help define the
capabilities of a part design to meet a set function by comparing it to the parts in the
database. Design databases could have helped investigate the design uncertainties
of the case study. However, creating an effective database requires significant effort
and substantial data. Boyard et al. (2014) state that such an exhaustive database’s
development time and complexity are hard to quantify. Similarly, collating the
research regarding process parameters, material characteristics, geometries, and
roughness within a database would be of benefit. Further, linking this to a CAD
program would enable real-time analysis of the geometric adherence of the part
due to the build parameters. However, by using a database, the designer limits the
design for AM based on the constraints in which the knowledge of the database was
built. When trying to push the design barriers of AM for innovative applications, a
designer may come across design uncertainties which have not yet been con-
sidered. It may be less effective if the database lacks the information required to
address an uncertainty under investigation.
Currently, no specific tasks within the process loop are available to capture
knowledge. The setting of evaluation criteria could be an avenue for improving the
knowledge management of the AMDA process by creating a more explicit intent
for the artefact. With evaluation criteria, designers would be required to clarify
their understanding of the uncertainty and expectations of the design artefact
through these criteria. The recorded criteria can be reviewed to demonstrate how
the knowledge was obtained and the design decisions were made according to what
was achieved, thereby aiding the management of knowledge regarding the AM
process and the specific design features obtained from the process. If a design
artefact satisfies all criteria, the designer gains greater confidence in the knowledge
obtained. If an artefact fails a criterion, the designer discovers previously unrec-
ognised uncertainties, as the failure highlights an area in which knowledge of the
AM process or AM design feature is lacking. Further, there should be a direction
within the AMDA process loop to capture (document) design knowledge to
encourage any new knowledge developed from the artefact investigation and
criteria to be recorded by the designers.
4.8. Is quick and iterative
One aim of design support is to prevent time from being wasted later in the design
process through poor design decisions, thereby helping ensure that the correct
decision is made earlier. Hence, the design support should be relatively quick to
use. The inspiration for the AMDA process is rooted in the three-stage prototyping
model proposed by IDEO, as described by Hartmann (2009). This model leverages
prototypes for various purposes: inspiration, evolution, and validation. During the
inspiration stage, the prototypes are used rapidly and iteratively to explore different
design concepts, as described by Lawrence (2003), who suggested the use of ‘rapid,
rough and right’principles, whereby early prototypes should be rapidly produced
and refined through a series of iterative prototypes. Furthermore, Lawrence
states: ‘Instead of spending your time and resources speculating solutions and
analysing the problem, spend your time solving it. Fail early in order to succeed
sooner.’Thus, the first stages of AMDA are quick and iterative. However, the speed
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of the process depends on several factors, such as print time and post-processing.
The RG artefacts exemplified the concept of rapid prototyping in the case study, for
which many geometries were used to assess the construction feasibility. In the
subsequent stages, (artefacts A and B), prototypes are used to evaluate specific
design questions. Finally, more comprehensive prototypes are employed for val-
idation purposes, in which the artefacts must represent the product (specification-
driven prototypes). At this stage, more advanced evaluation methods are used,
such as material characterisation, SEM analysis, and microtomography, at which
point each prototype cycle becomes slow and expensive.
Further detailing the stages of the process and providing more structure will
allow for clarifying the basic steps at each stage. The added clarity will enable the
designer to work quicker through the process and iterate their steps more specif-
ically. Further, evaluation criteria will enable a quicker evaluation of the process
results.
4.9. Defines goals (ensures motivation)
The AMDA process aims to help designers define their design goals by identifying
the most important design issues and creating artefacts to explore and understand
them. A designer defines the goal of testing an artefact: what they wish to learn
about their design choice through this investigation. In this study, the goal of the
RG artefacts was to assess achievable designs and inspire a self-supporting roof
design. The goal of artefacts A and B was to explore alternative test methods to
validate the surface roughness and its impact on fatigue life. Overall goals for a
product are generally set during product development, and these goals may not be
specific to Design for AM. However, design supports such as simulation tools, like
topology optimisation software, enable designers to define design goals to meet
specific properties while adhering to specific design features.
Integrating evaluation criteria into the AMDA process will support designers in
clarifying their uncertainty investigation goals by guiding the designer in articu-
lating the goal of the artefact design and testing. When using artefacts, the designer
decides when the knowledge generated is ‘good enough.’However, complementing
this judgement with clear, objective criteria that provide understanding of when
the designer should be satisfied with the knowledge acquired, i.e., when the goal of
the artefact uncertainty investigation has been achieved, will make it easier to
decide on when to continue and move on to the next step of the design process.
4.10. Is objective
The AMDA process aims to be objective by focusing on unravelling specific
uncertainties to provide product-specific design and performance indications.
However, as the designer’s current understanding of the design and AM process
determines the investigated design uncertainties, there is a possibility of bias
towards a solution in the first artefact. The designer’s subjective perspective of
the design uncertainty influenced their choice of geometries for the RG artefacts,
using the AMDA process to inspire a suitable geometry. Moreover, when evalu-
ating the A artefact the designers found that the design did not lead to a repre-
sentation of the design uncertainty, indicating that the issue was more related to the
artefact’s design rather than process-related capabilities.
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The objectivity of the artefact investigation is revealed during testing and result
evaluation. Data is typically objective, and experimental testing is often relatively
objective due to having a defined framework. Design guidelines derived from
parametric studies offer objectivity through this approach. Incorporating evalu-
ation criteria can enhance the objectivity of the AMDA process. The designer can
make informed decisions based on objective artefact design assessment by estab-
lishing goals through evaluation criteria. Moreover, when one can generalise
results, the objectivity of the design support increases.
4.11. Finds a solution and allows for multiple solutions
The first stage of the AMDA (inspire) focuses on exploring multiple solutions, and
the later stages are more focused on improving the current solution (evolve) and
finding and evaluating a final solution (validation). The RG artefacts aided the
identification of a selection of buildable unsupported roof geometries, while
artefacts A and B aimed to investigate an alternative test method for validating
the design–roughness–fatigue relationship for an unsupported roof design feature.
The AMDA process has been used to investigate the practical implications of a
design choice, thereby reducing uncertainty by providing a means to assess the
properties of the design feature. In general, in the final artefact iteration of an
AMDA process, the designer has reduced the uncertainties and is provided with a
clear understanding of the design effect. Thus, ideally, a design specification with
no remaining uncertainties can be created to validate the final design solution.
Furthermore, an AMDA investigation may not provide a solution; instead, it is
similar to the concept of co-evolution of problem and solution (Cross 2004;
Wiltschnig, Christensen, and Ball 2013). In this case study, artefact A could not
be used to validate the design feature, but it identified issues with the artefact design
that affected adherence.
4.12. Evaluation reflection
It is inherent that several of the AM design support and guidelines could have been
helpful for the designers; however, their generality makes their solutions conser-
vative because they are based on experience from earlier designs. To push bound-
aries and create innovative designs, one needs to see these supports as guidelines,
not limitations. This concept is very similar to the old proverb, ‘You have to know
the rules before you start breaking them’, implying that a thorough understanding
of the foundational principles and conventions provides the knowledge and
credibility required to push boundaries and explore new possibilities with inten-
tion and insight. In this case study, it is evident that many existing guidelines and
standards highlight the problem of unsupported overhangs. However, they do not
propose a solution to mitigate the problem. Here, the AMDA process is designed to
help the user challenge the limits of conventions by identifying uncertainties and
exploring the unknown. The knowledge generated that the AMDA facilities could
then further be captured within guidelines for the process, the product or the
specific design feature.
The AMDA process embodies the mental method strategy of ‘keep going’when
prototyping (Daalhuizen 2014). With each artefact iteration, the level of uncer-
tainty gradually decreases until the designer feels they have enough information to
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progress with their design development. When the artefact behaves as expected,
and the result provides the required knowledge, then design uncertainty is reduced.
However, if the artefact does not perform as anticipated or a new uncertainty is
identified, the designer must ‘keep going’in their investigation, learning from each
iteration.
The AMDA process encouraged the engineer’s initial creativity by enabling
them to explore the initial design space as they tested the buildability boundaries.
How the engineers used the AMDA process highlights a need for developing DfAM
support that helps designers balance creativity with ensuring a design’s feasibility.
In this case study, an unsupported roof geometry was a necessity, and a feasible
solution was identified through the roof geometry artefacts. However, this
approach introduced conflicting constraints as the unsupported roof design
resulted in rough surfaces, potentially having a negative impact on the feature
performance.
When investigating the alternative test method for validating the impact on the
feature performance through the artefacts, no explicit criteria for evaluating the test
were set. Instead, prior knowledge was used to determine if the artefact results
accurately described the design uncertainty. Hence, the suggestion to explicitly set
test evaluation criteria in the process will aid the designer in outlining the goals of
the evaluation/test stage. The process was found to aid the designers in addressing
the unique design challenges, understanding the process limitations, and grasping
machine capabilities, thus facilitating the comprehension of possible design spe-
cifications. The AMDA process enabled an exploration of the design options
beyond the standard guides and rules while investigating an alternative method
of validating the performance of a specific design feature more deeply than current
guidelines could suggest.
4.13. The AMDA method
The systematic review found definitions and descriptions for design support,
support system,tools,methods,methodologies,guidelines,heuristics, and principles.
Notably, the term design process was not found to be a descriptor of structured
design support in this review. References of the design process from the identified
definitions stated how design support acts within the design process. Design
support aids stakeholders of the design process in making educated decisions
(Zanic 2013), improves particular stages of the design process (Blessing & Chak-
rabarti 2009), can be combined to form a design process (Cross 2000), and guide
the execution of the design process (Hubka & Eder 1982). The design process
realises a transformation (Hubka & Eder 1982), whereas design support acts within
the process, and its output is not necessarily a transformation. The design process is
a broad and general concept encompassing various design activities. In contrast,
the AMDA process focuses on a specific activity and a more detailed design aspect.
Considering the definitions of the design support types presented and the descrip-
tion of a design process, if the AMDA process were to become more detailed and
formalised, the AMDA process may be more appropriately described as a design
method. Based on the evaluation of the AMDA process, an improved version is
presented in Figure 10.
In the current formulation of the AMDA process, shown in Figure 1, the first
stage is titled ‘Identified uncertainty’, indicating that the process began with the
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designer already aware of the uncertainty. The proposal now highlights the steps to
identify uncertainty. Firstly, the user must define the AM uncertainty (as shown in
this case study, design limits of self-supporting overhangs). Then, the designers
must define the goal of their design investigation, i.e., what they wish to learn from
the investigation. For the RG artefacts, the goal was to identify a suitable choice
based on three different design solutions for the roof geometry and explore the
buildability of different geometries. The second stage of the AMDA method is
Design AMDA, starting with defining the test method. With the testing method and
the defined uncertainty, the designer can design the artefact according to the
product’s specific geometry, incorporating their understanding of the test and
uncertainty. Furthermore, depending on the phase (inspire, explore, validate), the
test scope and parameters to be investigated are defined by designing artefact
variations, such as those for the RG artefacts. In the final step of Design AMDA, the
designer establishes the evaluation criteria to outline the expected behaviour of the
artefact. For the RG artefacts, the evaluation criteria could have been a simple pass
or fail based on whether the artefact’s structure was fully built, while artefacts A and
B could have been evaluated using the criteria outlined in Table 5. In the Print stage,
stage 3, the printing parameters for the machine and material are established and
recorded, and then the artefacts are printed accordingly. Once the printing process
is finished, the results are documented. Finally, the test is performed in the
Evaluate/test stage, and results are recorded. Then, the results are assessed against
the evaluation criteria to determine how well they represent the uncertainty and,
consequently, the correctness of the results. At this step, if the test fails to meet the
evaluation criteria or exhibits unexpected behaviour, the designer is directed
towards identifying potential unknown uncertainties as the artefact behaved
contrary to the designer’s understanding of the uncertainty. Hence, it is important
to acknowledge the possibility of the test meeting the criteria while uncovering an
unknown uncertainty. Finally, knowledge produced from the artefact investigation
is documented, and the designer decides if they have found a resolution to the
uncertainty and can move forward in their design process or if the uncertainty
remains. If so, the gained knowledge is used as a base for the next iteration loop. In
the case study, the off-axis failure due to the design of artefact A was fed back into
the design loop and addressed in the design of artefact B.
Figure 10. Proposal of the improved AMDA method.
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The review of the generalisability of the characteristics suggests that certain
characteristics are of more importance than others for evaluating different types of
design support. While all the characteristics are considered important components
of effective design support, Section 2.4 highlights that the characteristic provides a
path that is particularly relevant to design methods. Therefore, in evaluating the
newly proposed AMDA method, it is crucial to consider how well it conveys this
characteristic. The added detail and direction from the formalisation provide
stronger support for the user in understanding the path they can take to use the
AMDA method as an effective design support.
5. Conclusions, limitations, and future research
The purpose of this study was to evaluate the AMDA process and identify areas for
improvement. As the AMDA process helps designers leverage AM benefits, ensure
buildability, and meet performance requirements, the evaluation focused on
assessing the AMDA process’s effectiveness, i.e., how well it helps designers achieve
their desired outcome. A literature review of design support definitions and
descriptions was conducted to identify general characteristics to be used as a
framework for evaluating effectiveness. The review identified the following
11 design support characteristics:
1. Aids decision-making.
2. Emphasises the need for evaluation.
3. Communicates constraints.
4. Aids creativity.
5. Provides a path.
6. Communicates complexity.
7. Supports knowledge management.
8. Is quick and iterative.
9. Defines goals (ensures motivation).
10. Is objective.
11. Finds a solution and allows for multiple solutions.
All the identified characteristics are considered important for effective design
support. However, the review revealed that certain characteristics may be signifi-
cant for specific types of design support, such as the characteristic of design
methods to provide a path. These characteristics have been used to evaluate the
proposed AMDA process, as it was applied in an industrial case study with the
design of a space component. The characteristics also offer potential value to
researchers aiming to develop and evaluate design support. In addition to the
design support characteristics, the proposed AMDA process was evaluated against
alternative design supports. The case study first used a series of prototypes to
explore the design space (i.e., buildability). Then, the artefact design was narrowed
to create a more focused prototype for an alternative method of verification for a
specific uncertainty (i.e., surface roughness impact on fatigue performance).
Design artefacts were found to be useful for testing and evaluating the limitations
and constraints of a design solution and uncovering previously unrecognised
uncertainties, thereby providing knowledge to aid design decision-making. Design
artefacts also help challenge existing guidelines and provide context-specific design
support that other DfAM support cannot provide due to their generality. The
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results confirm that the AMDA process is an iterative procedure that requires
iterations to provide satisfactory conclusions regarding the design uncertainty.
With each iteration, valuable knowledge regarding the AM process and product
design can be gained. When analysing the case study, it is obvious that the
evaluation stage is important. However, the test evaluation relied on the designers’
subjective assessment, with no metrics in place to help determine if the desired
outcomes from using the design support were achieved.
Based on the evaluation, an improved AMDA method is presented. The
AMDA method provides detailed recommended steps for each stage of the loop.
Additionally, evaluation criteria are created during the second stage to improve the
evaluation of the test results. The evaluation criteria will improve the assessment of
the test result (i.e., if the artefact correctly represents the design uncertainty) and
provide the designer with increased confidence in the knowledge gained from the
artefact testing.
The literature search in this study was limited because it only considered
articles published between 2012 and 2023 to focus on identifying current design
support characteristics, using the search term (‘design support’AND definition)to
identify different design support types. A broader search is recommended to
validate the results further. A more extensive search could collate the results of
repeating the search term and using the identified terms, such as guidelines,tool,
and method, instead of the general term support, to identify articles with definitions
in which the design support type is specified. Also, adding a more extensive
literature study into the evaluation and effectiveness of design support should
strengthen the validity of the literature study.
This work is a continuation of the previous work of Dordlofva and Törlind
(2020), and the identification of design support characteristics was conducted after
their work and four iterations of the AMDA method. Future research should
investigate how lessons from the exploratory phase can be used to create and
evaluate verification artefacts. The knowledge generated from the AMDA method
in this study can be replicated to produce information on similar geometries. While
knowledge can be generated quickly through an AMDA loop, the simplicity of the
artefacts limits them to offering only indicative information regarding the general
understanding of the design uncertainty. Additionally, the case study centred on
evaluating the AMDA method used for an AM product within the space industry.
To improve understanding of DfAM supports used in practice, conducting a study
on AM design supports within the space industry would provide valuable insights
and help identify any existing gaps in support for addressing design challenges,
such as surface roughness.
Acknowledgements
The authors would like to acknowledge GKN Aerospace Sweden AB for supporting
this work and their colleagues at Luleå University of Technology Solid Mechanics
for the fatigue testing during this research. Figure 7 courtesy of Dr P. Åkerfeldt.
Financial support
This research was funded by the LTU Graduate School of Space Technology and
the EU Regional Growth Project RIT (Space for Innovation and Growth). This
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work was also supported by the Swedish National Space Research Programme
(NRFP), funded by the Swedish National Space Agency (SNSA).
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Table A2. Literature review results of definitions and descriptions of design support terms and the
characteristics related to them according to Table 2.
Term Definitions and descriptions
Source
[Characteristics]
Design support/
Support system
Design support (1) Eight ‘success criteria’for a gamified
design support are defined for
sustainability considerations:
“communicate complexity”,“enable quick
what-if assessment loops”,“support tacit
knowledge sharing”,“support cross-
functional negotiation”,“provide
examples”,“support lateral thinking”,
“stimulate acceptance of sustainability
engineering’, and ‘stress that engineering
is not happening yet”.
Scurati et al.
(2022,p.9)
[C.6], [C.7], [C.8]
Design support
system
(2) “…to support multiple stakeholders in
their design-related decision-making.”
Zanic, Andric,
and Prebeg
(2013), p. 383)
[C.1]
Design support
system
(3) “…endow stakeholders with direct
involvement in the design process and
will support their educated decisions by
sophisticated techniques for the
subjective decision making.”
Zanic (2013,
p. 226)
[C.1]
Design process
support system
(4) “…to facilitate the error, performance
and knowledge management; needed
because design as a complex activity is
prone to errors.”
Pikas et al. (2019,
p. 92)
[C.6]
Continued
Table A1. List of the seminal literature selected for the targeted review
Reference
Year of 1st
edition Citations*
Hubka, V., & Eder, W. E. (1982). Principles of Engineering Design. 1982 625
Blessing, L., & Chakrabarti, A. (2009). DRM, a Design Research
Methodology.
1999 2402
Roozenburg, N.F., & Eekels, J. (1995). Product Design: Fundamentals and
Methods.
1995 2118
Ulrich, K.T. (1995). Product Design and Development. 1995 2539
Pahl, G., Beitz, W., Feldhusen, J., & Grote, K. H. (2007). Engineering Design: A
Systematic Approach. Engineering Design: A Systematic Approach.
1984 13622
Cross, N. (2000). Engineering design methods: strategies for product
design.
1989 4514
*(citations of all editions according to Google Scholar on 02/01/2023).
43/49
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Table A2. Continued
Term Definitions and descriptions
Source
[Characteristics]
Design support (5) “All possible means, aids and measures
that can be used to improve design. These
are prescriptions –suggesting ways by
which design tasks should be carried out –
and include strategies, methodologies,
procedures, methods, techniques, software
tools, guidelines, knowledge bases,
workbooks, etc.”
Blessing and
Chakrabarti
(2009, p. 142)
[C.5]
Design tool Design support
tools
(6) “…bridge the gap between the theory
and current building practice by offering
indicators and frameworks.”
Akturk (2017,
p. 337)
[C.9]
(7) “…aim to simplify the theoretical
underpinnings and thinking process of
[regenerative design] and to provide
guidance and approachable goals for
practitioners.”
Design tools and
methods
(8) “Design tools and methods are
formulated …to address and alleviate the
problems’of design for
remanufacturing.”
Yang et al. (2016,
p. 145)
[C.1]
Design tools (9) “…hardware and software for
supporting design, based on some
design approach, method or set of
guidelines. The design tool supports the
effective and efficient use of the
approach, method or guideline.
Sometimes, their use would not be
possible without a computer tool.”
Blessing and
Chakrabarti
(2009, p. 143)
[C.7]
(10) “Procedures and computer-based
tools have been developed to help
designers analyse and define tolerances
that maximise the quality and minimise
the cost of complex parts and
assemblies.”
Pahl et al. (2007,
p. 181)
[C.9]
Design method (11) “System of methodical rules that
determine (classes of) possible
procedures and actions which are
intended to lead via a planned path
to the accomplishment of a desired
aim.”
Hubka and Eder
(1982, p. 103)
[C.5]
(12) “Sequences of activities to be
followed in order to improve particular
stages of the design process (task
clarification, conceptual design, detail
design, etc.), and specific tasks within
these stages (e.g., generation,
evaluation, etc.).”
Blessing and
Chakrabarti
(2009, p. 142)
[C.5]
Continued
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Table A2. Continued
Term Definitions and descriptions
Source
[Characteristics]
(13) “In a sense, any identifiable way of
working, within the context of designing,
can be considered a design method….
Design methods can, therefore, be any
procedures, techniques, aids or “tools”
for designing. They represent a number
of distinct kind of activities that the
designer might use and combine into an
overall design process.”
Cross (2000,
p. 46)
[C.5]
(14) “A method is the consciously applied
diachronous structure of an action process.”
(15) “A method itself can be seen as a
composite of a number of rules …They aid
in finding something, but there is no
guarantee that it will be found always and
by everyone.”
(16) “Design methods are heuristic methods
which are based on ‘weak’knowledge. They
do not guarantee a result, but do increase
thechanceofachievingaresult.”
Roozenburg and
Eekels (1996,
pp. 40; 42; 45)
[C.5], [C.5],
[C.11]
Design
methodology
Design
methodology
(17) “…the wholeness of issues that
determine how the design is done,
including target setting, organisation of
design tasks, use of techniques, criteria
and assessment methods.”
Delponte et al.
(2015, p. 900)
[C.5]
Design
methodology
(18) “A design methodology needs to meet
the following requirements: (Req. A)
Efficiency and Sustainability allows the
methodology to incorporate the re-use of
existing components. (Req. B) Allowing
for individual Customizability states that
fine-grained customization capabilities
are required [(Maeder & Williams, 2017;
Meyer et al. 2015)]. Since manually
exploring the design space is not feasible
due to a large number of potential
solutions, (Req. C) Capability of
Automation demands the automation of
key processing steps of the design
methodology. Considering the variety of
possible solutions, determining the most
suitable solution will involve multiple
criteria (such as costs, installation, and
maintenance effort). Thus, (Req. D)
Multiple Solutions states that a design
methodology needs to be able to offer a
Wollschlaeger
and Kabitzsch
(2020, p. 829)
[C.11]
Continued
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Table A2. Continued
Term Definitions and descriptions
Source
[Characteristics]
“design space”containing possible
alternative designs [(Estudillo-
Valderrama et al. 2010)].”
Design
methodology
(19) “Design methodology is the science of
methods that are or can be applied in
designing. In English the word
‘methodology’has two meanings. The
first meaning is: a science or study of
method, i.e., the description, explanation
and valuation of methods. The second
meaning of ‘methodology’is: a body of
methods, procedures working concepts
and rules employed by a particular
science, art or discipline.”
Roozenburg and
Eekels (1995,
p. 29)
[C.5]
Design
methodology
(20) “Design methodology, however, is a
concrete course of action for the design of
technical systems that derives its
knowledge from design science and
cognitive psychology, and from practical
experience in different domains. It
includes plans of action that link working
steps and design phases according to
content and organisation…
(21) Design methodology should therefore
foster and guide the abilities of designers,
encourage creativity, and at the same
time drive home the need for objective
evaluation of the results.”
Pahl et al. (2007,
p. 9)
[C.2], [C.4], [C.5]
Design
methodology
(22) “The intention is to try to ensure that
the design problem is fully understood,
that no important elements of it are over-
looked, and that the real problem is
identified. There are plenty of examples
of excellent solutions to the wrong
problem.”
Cross (2000,
p. 34)
[C.2]
Design
methodology
(23) “General theory of procedures for the
solving of design problems. It involves
both the general design strategy and also
the tactical approach to individual
portions of design work.”
(24) “System of methods that may be used
by an individual to attain a desired
objective.”
Hubka and Eder
(1982, pp. 102;
103)
[C.5], [C.11]
Design
methodology
(25) “By a design approach or
methodology, we mean an overall
framework for doing design.”
Blessing and
Chakrabarti
(2009, p. 142)
[C.5]
Continued
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Table A2. Continued
Term Definitions and descriptions
Source
[Characteristics]
General working
methodology
(26) “A general working methodology
should be widely applicable, independent
of discipline and should not require
specific technical knowledge from the
user.”
(27) “The following conditions must be
satisfied by anyone using a systematic
approach:
•Define goals by formulating the overall
goal, the individual subgoals and their
importance. This ensures the motivation
to solve the task and supports insight into
the problem.
•Clarify conditions by defining the initial
and boundary constraints. Dispel preju-
dice to ensure the most wide-ranging
search for solutions possible and to avoid
logical errors.
•Search for variants to find a number of
possible solutions or combinations of
solutions from which the best can be
selected.
•Evaluate based on the goals and condi-
tions.
•Make decisions. This is facilitated by
objective evaluations. Without decisions
and experiencing their consequences
there can be no progress.”
Pahl et al. (2007,
p. 53)
[C.3], [C.9],
[C.10], [C.11]
Design guidelines (28) “The most commonly used and
effective approach to facilitate product
design for remanufacturing is through
generating design guidelines to
addressthevariousbarriersand
challenges during the remanufacturing
process.”
Yang et al. (2016,
p. 145)
[C.3]
(29) “Design guidelines are rules, principles
and heuristics that are useful to follow in
attaining some design objectives.”
Blessing and
Chakrabarti
(2009 p. 143)
[C.5], [C.9]
(30) “Guidelines help product design teams
to make early [design for environment]
decisions without the type of detailed
environmental analysis that is only
possible after the design is more fully
specified.”
Ulrich and
Eppinger (2012,
p. 240)
[C.1]
Continued
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Table A2. Continued
Term Definitions and descriptions
Source
[Characteristics]
Design heuristics
and principles
Design heuristics (31) “…to help designers to perceive the
unique capabilities of AM and to be a
source of inspiration for creative
activities during concept generation.”
Valjak and
Lindwall (2021,
p. 2577)
[C.4]
Design principles (32) “…their main purpose is to support
the early design and its realisation in a
form suitable for AM.”
Valjak and
Lindwall (2021,
p. 2577)
[C.6]
Design principle (33) “The source, or basis, or law (e.g. of
nature), or primary element, or
fundamental truth (e.g. an idea), from
which the other laws, elements, etc., may
be derived or on which they are
dependent, may also refer to idealised
methodical rules to guide the execution
of the design process…”
Hubka and Eder
(1982, p. 108)
[C.5]
Heuristic
principles
(34) “[Systematic procedures] are also
known as “heuristic principles”(a
heuristic is a method for generating ideas
and finding solutions) or “creativity
techniques”.”
Pahl et al. (2007,
p. 53)
[C.4], [C.11]
Design rules Two types of rules are defined: algorithmic
and heuristic.
(35) Algorithmic rule: “a rule that can be
transferred in to an algorithm”, where an
algorithm is defined as “an unambiguous
set of questions or commands that have
to be dealt with in the dictated order, and
will lead to reaching a clearly described
result.”
(36) Heuristic rules: “behavioural rules
that promote the finding of something
in an –at least partially –goal-rational
situation …Also with divergent
(creative) thinking all kinds of
behavioural rules are used more or less
consciously, including methods to keep
the mind open, to derive inspiration,
and to promote inventions that were
not intended (‘serendipity’).”
Roozenburg and
Eekels (1995,
p. 43)
[C.5], [C.11]
(37) “The constraints of a process can be
concisely communicated to designers in
the form of design rules.”
Ulrich and
Eppinger (2012,
p. 264)
[C.3]
Continued
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Table A2. Continued
Term Definitions and descriptions
Source
[Characteristics]
Design procedure Design procedure (38) “…it is important to have a defined
design procedure that finds good
solutions. This procedure must be
flexible and at the same time be capable
of being planned, optimised and verified.
Such a procedure, however, cannot be
realised if the designers do not have the
necessary domain knowledge and cannot
work in a systematic way. Furthermore,
the use of such a procedure should be
encouraged and supported by the
organisation.”
Pahl et al. (2007,
p. 9)
[C.5], [C.11]
Heuristic
procedures
(39) Principles on which ‘heuristic
procedures’are based are defined: “(a)
Ensure motivation,
(b) show limiting conditions (expanded,
clarified problem),
(c) dissolve prejudice (no fixations), (d)
search for variants (possibilities of
optimisation), and (e) reach decisions
based on evaluations of maximum
objectivity (without decisions the design
process is impossible).”
Hubka and Eder
(1982, p. 28)
[C.3], [C.9],
[C.10], [C.11]
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