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Investigating Perceived Meanings and Scopes of Design for Additive Manufacturing



The concept of Design for Additive Manufacturing (DfAM) is gaining popularity along with AM, despite its scopes are not well established. In particular, in the last few years, DfAM methods have been intuitively subdivided into opportunistic and restrictive. This distinction is gaining traction despite a lack of formalization. In this context, the paper investigates experts' understanding of DfAM. In particular, the authors have targeted educators, as the perception of DfAM scopes in the future will likely depend on teachers' view. A bespoke survey has been launched, which has been answer by 100 worldwide-distributed respondents. The gathered data has undergone several analyses, markedly answers to open questions asking for individual definitions of DfAM, and evaluations of the pertinence of meanings and acceptations from the literature. The results show that the main DfAM aspects focused on by first standardization attempts have been targeted, especially products, processes, opportunities and constraints. Beyond opportunistic and restrictive nuances, DfAM different understandings are characterized by different extents of cognitive endeavor, convergence vs. divergence in the design process, theoretical vs. hands on approaches.
Cite this article: Berni, A., Borgianni, Y., Obi, M., Pradel, P., Bibb, R. (2021) ‘Investigating Perceived Meanings and
Scopes of Design for Additive Manufacturing’, in Proceedings of the International Conference on Engineering Design
(ICED21), Gothenburg, Sweden, 16-20 August 2021. DOI:10.1017/pds.2021.455
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Berni, Aurora (1);
Borgianni, Yuri (1);
Obi, Martins (2);
Pradel, Patrick (2);
Bibb, Richard (2)
1: Free University of Bolzano-Bozen;
2: Loughborough University
The concept of Design for Additive Manufacturing (DfAM) is gaining popularity along with AM,
despite its scopes are not well established. In particular, in the last few years, DfAM methods have
been intuitively subdivided into opportunistic and restrictive. This distinction is gaining traction
despite a lack of formalization. In this context, the paper investigates experts' understanding of DfAM.
In particular, the authors have targeted educators, as the perception of DfAM scopes in the future will
likely depend on teachers' view. A bespoke survey has been launched, which has been answer by 100
worldwide-distributed respondents. The gathered data has undergone several analyses, markedly
answers to open questions asking for individual definitions of DfAM, and evaluations of the
pertinence of meanings and acceptations from the literature. The results show that the main DfAM
aspects focused on by first standardization attempts have been targeted, especially products, processes,
opportunities and constraints. Beyond opportunistic and restrictive nuances, DfAM different
understandings are characterized by different extents of cognitive endeavor, convergence vs.
divergence in the design process, theoretical vs. hands on approaches.
Keywords: Design for Additive Manufacturing (DfAM), Early design phases, Design methods,
opportunistic DfAM, restrictive DfAM
Borgianni, Yuri
Free University of Bolzano-Bozen
Faculty of Science and Technology
1938 ICED21
The enhanced capabilities of new digital manufacturing techniques such as Additive Manufacturing
(AM) and 3D Printing are capable of drastically changing the mindset of designers and engineers
toward the design process. This change of mindset is beginning to gain traction but remains far from
completed and designers ability to fully exploit the potential of AM is questioned (Thompson et al.,
2016). In this context, the need arises for new design methodologies, Design for Additive
Manufacturing (DfAM), that can support designers in exploiting the capabilities of these technologies.
DfAM guides designers to consider the opportunities provided by AM such as shape complexity,
material complexity, no-need for tooling and customization to mention a few (Kumke et al. 2018).
This new way of re-thinking the whole design process raised interest both in academia and industry,
leading to the development of methods and frameworks over the past decade (Thompson et al 2016).
However, the objectives of these methods are diverse, and some of them target design constraints,
rather than new opportunities, mirroring the scope of traditional Design for Manufacturing and
Assembly (Boothroyd, 1994).
In consideration of this plurality of objectives, Laverne et al. (2015) reviewed and classified, for the
first time, the main DfAM methods into two main categories: opportunistic and restrictive DfAM. As
more widely explained in the following section, this differentiation is increasingly diffused in the AM
literature, although it has not undergone a validation process yet. This classification might be useful in
formalizing DfAM knowledge, which is still an open issue (Kim et al., 2019), as well as in its ongoing
standardization (Mani et al., 2017).
The distinction between opportunistic and restrictive methods represents a starting point; however, as
it has been currently proposed, this distinction is affected by the following shortcomings:
Whilst the distinction is intuitively sound, the belonging of specific methods and approaches to
one of the two categories has not been universally accepted or subject to rigorous evaluation. It
remains an arbitrary decision whether a technique should be considered opportunistic or
The ready acceptance of these two classifications has neglected the exploration of other
potentially useful classifications.
Any definitive classification should be evaluated, recognized as a guiding principle, accepted and
internalised by all stakeholders in the field. This would support efficient communication between
different actors within the discipline.
Starting from these premises, this paper explores the interpretation of the DfAM concept, which is
relatively novel and consequently still fluid. In particular, the paper aims to ascertain the degree of
agreement on the distinction between opportunistic and restrictive methods, to understand whether
some experts perceive the two classes as antithetic, and to investigate the presence of other latent and
yet unelicited classification criteria. The study addresses these issues by gathering feedback from a
representative sample of educators active in the teaching of AM and DfAM. Here, the role of
educators is considered critical as the future understanding of DfAM will depend on their viewpoint.
At the same time, consensus, shared definitions and classifications would benefit educators in
achieving international consistency and compatibility of DfAM teaching and learning.
Therefore, this paper aims to investigate this distinction between opportunistic and restrictive methods,
look for consensus among experts and explore whether there are other potentially more useful
classification criteria.
Section 2 analyses the current uptake of the distinction between opportunistic and restrictive methods.
Section 3 describes the approach to determining different interpretations of DfAM, with the results
presented in Section 4 and discussed in Section 5. Conclusions are drawn in Section 6 with emphasis
on the pursuance of the research objectives and the major implications of this study.
Laverne et al. (2015) characterised opportunistic DfAM methods as those that lead to the creative
exploration of new shapes and concepts. The authors considered methods based on optimization
techniques (e.g. parametric and topological), elementary shapes (e.g. lattice structures), bionic
structures and features produced by specific AM technologies. Such methods provide designers with
an opportunity to extend the possibilities of design thinking, aiming to overcome the fixation on
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constraints imposed by conventional manufacturing techniques (Booth et al., 2017). On the other
hand, restrictive DfAM methods address the limitations and constraints of AM technologies such as
limited materials and properties, performances and characteristics of AM machines, product
manufacturability and quality. Laverne and colleagues also investigated the distribution of the
implementation of these methods suggesting that opportunistic and restrictive methods are equally
distributed. They also provided case studies where both categories of DfAM were involved. The
distinction between opportunistic and restrictive methods became a landmark for successive studies,
which embraced and shared this classification. Consistently, the literature shows that AM is
characterised by great opportunities and considerable constraints while highlighting the relevance of
this process for industrial applications (Thompson et al., 2016).
In general, many scholars agree that opportunistic DfAM is more relevant to the early, more
explorative divergent stages of the design process (Kumke et al., 2018; Laverne et al., 2015; Blösch-
Paidosh and Shea, 2019; Design Council, 2005) since these methods aim to stimulate creativity and
creative ideas (Watschke et al., 2017; Prabhu et al., 2020a; Barclift et al., 2017). On the other hand,
restrictive DfAM methods are proven to be more effective as guidelines (Watschke et al., 2017) to be
followed in the later, convergent stages of the design process (Kumke et al., 2018; Prabhu et al.,
2018a; Reichwein et al., 2020) where the concept is fixed and the design progresses towards
Beyond scholars stances on the role of opportunistic and restrictive DfAM approaches, the two
categories have been purposefully used in specific applications concerning design processes and
training activities. While the former has focused more on opportunistic methods as a means to improve
ideation, the latter has involved predominantly restrictive methods and markedly the use of guidelines
to ensure successful printing.
In particular, the integration of specific methods in design and product development processes can be
found in the contributions that follow.
Zhu et al. (2017) focused on restrictive DfAM only, since they were interested in design
optimization to ensure geometric consistency of the designed product.
Laverne et al. (2017) prepared a questionnaire where they asked designers which of the two
DfAM methods they usually use. The scope of the questionnaire was to understand the
relationship between AM and innovation.
Blösch-Paidosh & Shea (2019) encouraged the integration of opportunistic DfAM in the early
design stages.
Meisel et al. (2017) took into consideration the whole design process and presented a case study
where an existing product had been redesigned with AM techniques. The study showed how
using specific DfAM guidelines can significantly reduce manufacturing time and costs, while
obtaining new and novel design geometries. In this process, opportunistic methods were mainly
used to widen the exploration of the design space, while restrictive ones were leveraged to
consider fabrication issues.
Reichwein et al. (2020) focused on the functionality of the product and used an opportunistic
DfAM approach to a larger extent. Their contribution took into account the detailed design
phases too.
Other contributions consider DfAM in the training of practitioners and the education of students
through workshops and other practical activities, as in the following.
Prabhu et al. (2020a) underlined the need to further investigate opportunistic and restrictive
DfAM as training methods for industry professionals. The scholars used a workshop-based study
to introduce opportunistic and restrictive DfAM to industrial practitioners.
Conversely, Watschke et al. (2017) proposed an academic workshop where opportunistic and
restrictive approaches were combined to support the generation of creative solutions concepts in
design education.
Barclift et al. (2017) evaluated students creativity when performing a design task using
opportunistic methods only.
Prabhu et al.(2018a) investigated the effect of opportunistic and restrictive DfAM methods in
education, focusing on engineering students creative process.
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Prabhu et al. (2018b) studied the impact of timing in teaching opportunistic and restrictive
methods on the students design processes. The scholars compared two DfAM educational
interventions conducted at different points in the academic semester.
Prabhu et al. (2020b) proved that the order of teaching opportunistic and restrictive DfAM on
engineering students can affect the way they approach the design process. This highlights the
need for educators to account for the order of presenting content to students.
The above examples show that the distinction between opportunistic and restrictive DfAM is no longer
limited to theoretical constructs but is also becoming operationalized. It follows that the classification
of DfAM methods is perceived as a necessity by scholars and that more proven categorizations can
increase the robustness of future research on the topic. To the best of authors knowledge and based on
the analysis of the literature, there are no further classifications of DfAM methods and approaches.
The introduction revealed the need for increasing formalization of DfAM which would align with
ongoing standardization initiatives and inform and support the development of education and training
in DfAM.
To investigate educators current understanding of DfAM and the current diffusion of this topic in
tertiary education, the authors implemented an online survey. While a full description of the survey,
which is available in an article currently under review, goes beyond the scope of the present paper, a
clone is accessible at (data will
not be saved). Briefly, the survey included questions to pursue the objectives that follow.
Evaluate the uptake of DfAM in education and specifically to the broader AM field.
Characterise units of study in which DfAM is taught in terms of the number of students,
University level (e.g. Bachelor, Master), training activities (e.g. lectures, hands-on projects),
assessment methods, taught DfAM contents, and the relevance of DfAM-related subjects.
Characterize educators in terms of their experience, University grade, attitude to research DfAM
and AM.
Characterize institutions and programmes that include DfAM education in terms of discipline
(e.g. engineering, design), country, etc.
Elucidating educators understanding of DfAM, taught DfAM-related topics out of a tentative list
extrapolated from Thompson et al. (2016), familiarity with DfAM, and agreement on possible
definitions of DfAM, which had been previously collected in the literature.
The survey was completed in spring 2020 by 100 internationally distributed educators that teach AM
and claimed to be familiar with DfAM. Out of this sample, 71 educators include DfAM in their taught
content. The issues in the last bullet point above were investigated to address the problems identified
in this paper. In particular, we considered the answers to the two survey questions below.
1. What does Design for Additive Manufacturing mean to you?, which represents an open
question and, therefore, the respondents were free to answer in an unconstrained way the
analysis of the answers to this question is given in Section 4.1.
2. How pertinent are the definitions or topics below to your understanding of Design for Additive
Manufacturing?, which was followed by 17 randomly ordered alternative DfAM meanings
extracted from the literature and largely inferable from (Thompson et al., 2016). The respondents
answered through a five-point Likert scale ranging from not at all pertinent to extremely
pertinent. The analysis of the answers to this question is given in Section 4.2., which includes
the 17 options.
The latter followed the former to avoid bias and maximize respondents freedom of answering the first
4.1 Open-ended definitions of DfAM
The open-ended questions were analysed using Nvivo (version 12). Keywords indicating individual
concepts were identified and coded. To gain additional insight, an affinity diagram was developed to
categorize the codes that were cited twice or more. Affinity diagrams are useful for organizing ideas
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into categories based on their underlying semantic similarity and assist the identification of patterns
and categories that exist in qualitative datasets (Pyzdek, 2003).
To develop the affinity diagram shown in Table 1, one of the authors worked independently to code
the entries and cluster them into categories. At that point, the preliminary categorisation was shared
with other two authors, who independently reviewed the pertinence of the coding and clustering.
Thereafter, the three authors met and discussed the categories to reach a final consensus., Each author
has at least 5 years research experience in the field of DfAM evidenced by peer reviewed papers.,
The content analysis provided 241 different codes. Codes were subsequently compared to determine
which belonged together, thereby forming a Category. Category names are short overarching
keywords. The codes were grouped into 7 broader categories (Erlingsson and Brysiewicz, 2017).
Table 1 reports the categories with at least five entries (the number of entries in brackets), while the
codes with one entry only are grouped for the sake of brevity.
Table 1 Definitions of DfAM (n = 241 themes, n = 100 respondents).
Outcomes (63)
Desired function
New functionalities
Other subcategories with an entry
Design approaches (48)
Design rules
Design methods
Design tools
Design guidelines
Design thinking
Design process
Other subcategories with an entry
Opportunistic DfAM (43)
Design freedom
Use AM freedoms
Investigating design possibilities
Understand and capitalise opportunities
Other subcategories with an entry
Restrictive DfAM (42)
Design for Manufacturability
Factors to consider
Other subcategories with an entry
Individual AM processes (7)
Subcategories with an entry
Optimisation (6)
Optimal use of the manufacturing process
Other subcategories with an entry
Uncategorized (32)
Four main categories Outcomes, Design approaches, Opportunistic DfAM and Restrictive
DfAM were identified from the codes while the categories Individual AM processes and
Optimisation were also identified, but by a limited number of codes. The majority of the elicited
definitions included the outcomes of DfAM. These consisted of generic and widely mentioned terms
such as parts and products (illustrative definitions given by respondents are reported in italics in the
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Any tools or methods that are useful for synthesizing and/or refining parts that can be fabricated with
AM successfully and/or make use of the freedoms enabled by AM,
More specific product-related outcomes such as lightweight, quality, price, complexity, were
also frequent.
Designing a part with lightweight, optimal shape (complex) and manufacturable using AM.
The Design approaches to achieve these outcomes were also widely reported in the gathered
definitions. These included a wide variety of approaches and synonyms such as Design rules,
Design methods and Design tools.
Any tools or methods that are useful for synthesizing and/or refining parts that can be fabricated with
AM successfully and/or make use of the freedoms enabled by AM.
The interpretation of DfAM as Restrictive or Opportunistic was also well recognized among our
participants, although these classes cannot be considered as fully representative of DfAM-related
principles. As evident from Table 1, the distribution of definitions compliant with the two classes is
extremely balanced. The most cited Opportunistic DfAM meanings were generic synonyms of
opportunities such as Capabilities, Advantages, Benefits, etc.
Exploiting the potential of AM in the design step and optimize geometries and shape.
In terms of Restrictive, the most cited terms were Design for manufacturability and Limitations,
Constraints and Restrictions.
The process of designing parts or products to take into account the constraints and abilities of an
additive manufacturing process.
Besides the mention of specific AM technologies, which the authors judge misleading to characterize
DfAM, and optimization-oriented definitions, there was a long tail of codes that did not clearly match
the categories and other codes, or they were mentioned too rarely to justify a new category. These
themes included for instance Adapted design to end-user, Life cycle requirements or New
Basically, it can be design methods or tools which consider the functional and mechanical
performance and key product life-cycle requirements, including manufacturability, complexity,
reliability, time and cost can be optimized leveraging on the unique capabilities of additive
4.2 Evaluated pertinence of DfAM meanings
The pertinence evaluations attributed by the 100 respondents to the 17 DfAM meanings (see Table 2)
were transformed into 17 corresponding ordinal variables. Consistent with the Likert scale used, the
values attributed to these variables ranged from 1 to 5. The statistical analyses that follow were
performed with the software Stata SE 13.
The variables were firstly used to verify the redundancy of the meanings by submitting them to a
Spearman correlation. As a common rule of thumb, values over 0.8 could be considered as an (almost)
perfect agreement (Landis and Koch, 1977), which, in the specific case, could be interpreted as a
repetition of a definition because of a systematic repetition of pertinence evaluations. The correlation
values ranged from -0.03 to 0.75, which led us to conclude that none of the variables pairs represent
fully overlapping DfAM-related concepts.
Therefore, to merge these meanings and capture independent dimensions of DfAM definitions, the
same variables were processed with a Principal Component Analysis (PCA). The PCA provides a
number of independent dimensions, namely the components, which are featured by their capability of
describing the variability of the sample, which is expressed by the eigenvalue and, accordingly, the
proportion of explained variability. The extracted components are a linear combination of input
variables (here the stated agreement on DfAM meanings) and their calculated weights. In the
presented case, each respondent could therefore feature a number of independent variables and their
associated values. However, the key point here is to extract and characterize the fundamental
independent components and interpret their significance to DfAM.
As a common rule of thumb, those PCs reporting an eigenvalue greater than 1 were processed further
to infer the relevant independent dimensions, leading us to focus on five PCs accounting for 67.7% of
the data variability. The weights of the original components (first column) on the five selected PCs are
reported in Table 2.
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Table 2. Extraction of Principal Components out of the evaluation of pertinence of DfAM
definitions (n=100 respondents)
PCA weights
1. Design methods aimed at better exploiting the potentials
of Additive Manufacturing
2. Exploring design methods in which the functional
performance and lifecycle considerations of products are
optimized to Additive Manufacturing capabilities
3. Design freedoms enabled by Additive Manufacturing
4. Understanding the constraints in the design of parts to be
produced by Additive Manufacturing
5. Design rules, guidelines and suggestions for parts
conform with Additive Manufacturing production
technologies in light of their current affordances and
6. Supporting the choice between traditional and Additive
Manufacturing technologies
7. Supporting the choice between the most suitable AM
process for the production of parts or products
8. Part consolidation, reducing the number of components
9. Designing parts with lattice or cellular structures
10. Utilising Topology Optimization or targeting the
creation of lightweight structures
11. Design of parts with multiple or composite materials
12. Design of products with enhanced functionalities that
are enabled by Additive Manufacturing
13. The redesign of a part to make it compatible with
Additive Manufacturing production
14. The development and optimal use of CAD systems for
the design of parts that match Additive Manufacturing
15. The overcoming of cognitive barriers due to the
knowledge of the limitations of traditional technologies
16. The change of thinking style previously imposed by
past experience and conventional fabrication techniques,
which leads to an extension of the design space
17. The research aimed at pushing the boundaries of
Additive Manufacturing technologies and its repercussions
on design capabilities
The subsequent interpretation of the PCs based on the weight of the original variables led to the
determination of the following DfAM-related concepts (see the attributed names in italics), which are
ordered according to diminishing proportion of variability attributed to each PC (in brackets). Where
applicable, a tentative definition of DfAM classes featured by high (a.) and low (b.) values of the PCs
is given.
1. General (38.0%): the weights of the original variables are substantially evenly distributed; as
such, all the meanings contribute to a general understanding of the whole scope of DfAM
regardless of restrictive or opportunistic nuances.
2. Explorative (9.9%): the variables with the highest positive weights feature the overcoming of
barriers enabled by AM and divergent design processes. Conversely, variables associated with
the most significant negative weights deal with constraints and decision-making processes. This
PC has expectedly high values for those respondents with a predominant opportunistic
interpretation of DfAM.
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a. Explorative DfAM methods are those intended to guide designers in divergent design
activities keeping in mind the opportunities offered by AM.
b. Non-explorative DfAM includes criteria and tools for making decisions on design
alternatives with a particular focus on designs feasibility by means of AM devices.
3. Conceptual (7.1%): the variables with highest positive weights feature theoretical constructs
concerning the importance of exploiting AM potential, as opposed to markedly negative weights
attributed to tasks that involve detailed design phases (topology optimization, choice of
a. Conceptual DfAM includes techniques that support the consideration of AM potential
in the early design phases.
b. Non-conceptual DfAM tools are those meant to finalize the design of products to be
manufactured with AM.
4. Cognitive (6.6%): the variables with highest positive weights feature DfAM meanings concerning
the change of designers mindset and the overcoming of their psychological inertia, as opposed
to markedly negative weights attributed to practical computer-supported design tasks (topology
optimization, lattice structures).
a. Cognitive DfAM involves the research aimed to change designers mindset towards
the understanding of benefits enabled by the existence and the progress of AM.
b. Non-cognitive DfAM includes methods and tools that support geometric
modifications of designed parts, whose fabrication is enabled by AM capabilities.
5. Constraint (6.1%): the variables with highest positive weights feature constraints and guidelines
for product design and redesign, mostly oriented to feasibility and compliance with AM
technologies. Conversely, variables associated with the most significant negative weights deal
with new opportunities offered by AM. This PC has expectedly high values for those
respondents with a predominant restrictive interpretation of DfAM.
The analysis of the definitions attributed by the surveys respondents reveals that product and process
aspects constitute the foci of DfAM. This finding can be interpreted as the relevance of both
procedural/methodological aspects and the outcomes thereof when DfAM is approached. The clear
emergence of assigned definitions or clauses ascribable to opportunistic and restrictive DfAM from
both the affinity diagram and the outcomes of the PCA shows that the scope of both classes is well
internalised by educators in DfAM, irrespective of their awareness and their knowledge of the
terminology introduced by Laverne et al. (2015). It follows that, overall, the answers suggest that for
our sample of educators, DfAM is a process which implies design methods, tools and knowledge to
achieve a specific outcome by exploiting AM characteristics and mitigating its limitations. This is in
accordance with the published definitions of DfAM (e.g. BS EN ISO/ASTM 52910:2019), showing
that there is both a shared agreement of what DfAM means among our sample and between our
sample and the published definitions.
The fundamental alignment between what is generally claimed as being part of DfAM and the
perceived pertinence of meanings emerges as an outcome of the PCA too. Here, the main PC was a
balanced combination of an extensive list of meanings, including opportunistic and restrictive
interpretations, as well as other facets. Taken in isolation, this main PC is capable of explaining 40%
of data variability. This implies that our sample of respondents is attributed varying values on this PC.
This apparently strong result could indicate that the respondents had a different degree of confidence
and familiarity towards definitions included in the DfAM literature. However, some other terms
distinguishing educators understanding of DfAM emerged. While opportunistic and restrictive
nuances arose, other fundamental interpretations of DfAM are worth highlighting. Other
interpretations of the study outcomes include:
DfAM methods might be attributed to the capability of both expanding the design space enabled
by AM and supporting decision-making, especially when it comes to selecting manufacturing
DfAM methods might be featured by their varying level of abstraction.
DfAM ranges from actions to increase engineers and designers awareness of the potential
benefits of AM to practice-oriented tools targeting redesign and operating on 3D models.
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This study is inherently affected by some limitations. On the one hand, the respondents constitute a
large sample of convenience, but the generalizability of the results can be questioned, as the sample is
not sufficiently representative of the total engineering educator population. Moreover, the authors have
already underlined the reasons for targeting educators in a first instance, but their views could be
partially misaligned with the viewpoint of other stakeholders, such as researchers, industrialists,
officers of standards bodies, manufacturers of AM machines. While the respondents to our survey
possessed a range of confidence in DfAM, their confidence level was not been considered in this first
analysis. The authors future work will nevertheless investigate the views of a broader range of
stakeholders. It will be of particular interest to investigate if the distinction between opportunistic and
restrictive DfAM is known or intuitively understood also in the AM community at large and markedly
outside of the design field. As a further limitation, the processes followed to create categories of
definitions and interpret PCs involve subjectivity. Although we have followed best practice and
worked in an unbiased way, the analysis performed by different research groups could lead to different
outcomes. Acknowledging this, we offer to share our data with researchers willing to repeat the
analyses and/or to contribute to the formalization of the DfAM concept and its classifications.
Based on the above discussion of the results, we propose the following final conclusions in response to
the research objectives.
The overall interpretation of DfAM amongst a large international sample of expert educators is in
line with the literature and especially with the first attempts to establish standards, in that
outcomes, processes, opportunities and constraints are well recognized.
Neither opportunistic nor restrictive interpretations of AM dominated, despite some respondents
expressing a preference for the former or latter. The two classes could be isolated as independent
clusters in the PCA, a general knowledge of all DfAM facets appears prevalent among the
Other classification criteria, beyond opportunistic and restrictive DfAM methods were found.
Although these new classes are not necessarily overlooked within DfAM, our results reveal these
additional criteria and show that the opportunistic/restrictive distinction is insufficient to describe
the whole breadth of DfAM.
The last bullet point indicates the need for the for standardization the scientific formalization of the
field of DfAM without neglecting the breadth of DfAM. In this respect, while many researchers focus
on practical aspects and markedly on products, whose design and fabrication is enabled by
opportunistic DfAM and constrained by restrictive DfAM, the conceptual and intellectual endeavour
behind the development of DfAM (see some well-recognizable PCs) cannot be overlooked.
Consequently, more studies are needed to validate the existence of DfAM categories and classification
criteria. The expected final outcome will be the formalization of methods and concepts belonging to
DfAM classes, articulated according to the opportunistic or restrictive distinction, and, if further
evidence is found, the additional criteria that have been elicited in this paper.
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... Laverne et al. (2015), who coined the two terms, indicated that opportunistic DfAM is more useful in early divergent design phases, while restrictive DfAM is a reference for later convergent design phases. This aspect is shared by many authors, as recently put forward by Berni et al. (2021). Possibly, different meanings might be attributed to the terms based on educators' and students' backgrounds, e.g. ...
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Additive Manufacturing (AM) has become an established discipline in both research and education. However, to achieve its full potential AM requires a step-change in design thinking, which makes Design for AM (DfAM) education and training crucial. This paper reports results from the first attempt to investigate the uptake of DfAM in higher education. This research required the development and administration of an articulated online survey, in which educators worldwide who teach AM and DfAM have participated. The results show that DfAM is taught in a considerable number of courses. However, the survey revealed that DfAM is seldom recognised as a distinct course or topic and the relevance attributed and proportion of teaching dedicated to DfAM within wider AM is typically marginal. DfAM is being mostly taught in North America and Europe and is also typically taught in institutions that are research active in AM or specifically DfAM, suggesting the subject has not yet reached maturity or diffusion into mainstream design and engineering curricula. It was interesting to find that currently, the contents of courses do not differ significantly between engineering and design programmes. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
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Due to increasing global competition, the demand for more individualization and constantly changing requirements, the importance of time to market and a high degree of flexibility in product development is increasing. New manufacturing technologies as well as improved methodical approaches in product development can be used for this purpose. The additive manufacturing industry has developed strongly in recent years and the possibilities of using this technology for final components are constantly increasing. At the same time, there are many efforts that contribute to identifying and implementing the potential of this manufacturing technology during product development. Other approaches, such as agile product development from software industry, are applied at the organizational level and also try to tackle the aforementioned challenges. This contribution therefore presents an approach using agile product development methods to develop components for additive manufacturing. The potentials of additive manufacturing are specifically used (e.g. tool-free manufacturing) to enable the principles of agile development also for physical products. The Design Pattern Matrix as an opportunistic approach is applied to systematically link the potentials of additive manufacturing with the functions of the product. Therefore, design elements are conducted from additive manufacturing case studies and provided to design engineers during the development. Furthermore, already known methods of design for additive manufacturing are classified into the procedure of the agile framework scrum.
The capabilities of additive manufacturing (AM) enable designers to generate and build creative solutions beyond the limitations of traditional manufacturing. However, designers must also accommodate AM limitations to minimize build failures. Several researchers have proposed design tools and educational interventions for integrating design for AM (DfAM) in engineering design. However, there is a need to investigate the effect of DfAM training on industry professionals’ use of these techniques and its subsequent effects on the creativity of their designs. In this paper, we present a workshop-based study in which industry professionals were sequentially introduced to opportunistic and restrictive DfAM. Participants were also given a DfAM task, with short idea generation sessions conducted between each content lecture. The participants’ designs and their DfAM and creative self-efficacies were compared from before to after receiving DfAM training. The results show that DfAM training successfully increased participants’ restrictive DfAM self-efficacy; however, no changes were seen in their opportunistic DfAM or creative self-efficacies. Further, the results show an increase in the uniqueness and overall creativity of the participants’ designs, but no significant changes were seen in the initially high usefulness of the designs. These findings suggest that DfAM training presents an opportunity to encourage creative idea generation.
Conference Paper
The capabilities of additive manufacturing (AM) processes present designers with creative freedoms beyond the limitations of traditional manufacturing processes. However, to successfully leverage AM, designers must balance their creativity against the limitations inherent in these processes to ensure their designs can be feasibly manufactured. To employ AM effectively, designers must learn about and apply opportunistic and restrictive design for AM (DfAM) techniques at appropriate stages of the design process. While researchers have demonstrated the effect of the order of presentation of content on learning and retrieval, there is a need to explore this effect within DfAM education. In this paper, we explore this gap through an experimental study involving 195 undergraduate engineering students. Specifically, we compare two variations in DfAM education: (1) opportunistic DfAM followed by restrictive DfAM, and (2) restrictive DfAM followed by opportunistic DfAM, against only opportunistic DFAM and only restrictive DfAM training. The variations in DfAM education are compared through differences in participants’ DfAM self-efficacy, their self-reported DfAM use, and the creativity of their design outcomes. The results show that only students trained in opportunistic DfAM, with or without restrictive DfAM, present a significant increase in their opportunistic DfAM self-efficacy. However, all students trained in DfAM – opportunistic, restrictive, or both – demonstrated an increase in their restrictive DfAM self-efficacy. Further, we see that teaching restrictive DfAM first followed by opportunistic DfAM results in the generation of ideas with greater creativity – a novel research finding. These results highlight the need for educators to account for the effects of the order of presenting content to students, especially when educating students about DfAM.
Design for additive manufacturing (DFAM) provides design freedom for creating complex geometries and guides designers to ensure the manufacturability of parts fabricated using additive manufacturing (AM) processes. However, there is a lack of formalized DFAM knowledge that provides information on how to design parts and how to plan AM processes for achieving target goals. Furthermore, the wide variety of AM processes, materials, and machines creates challenges in determining manufacturability constraints. Therefore, this study presents a DFAM ontology using the web ontology language (OWL) to semantically model DFAM knowledge and retrieve that knowledge. The goal of the proposed DFAM ontology is to provide a structure for information on part design, AM processes, and AM capability to represent design rules. Furthermore, the manufacturing feature concept is introduced to indicate design features that are considerably constrained by given AM processes. After developing the DFAM ontology, queries based on design rules are represented to explicitly retrieve DFAM knowledge and analyze manufacturability using Semantic Query-enhanced Web Rule Language (SQWRL). The SQWRL rules enable effective reasoning to evaluate design features against manufacturing constraints. The usefulness of the DFAM ontology is demonstrated in a case study where design features of a bracket are selected as manufacturing features based on a rule development process. This study contributes to developing a reusable and upgradable knowledge base that can be used to perform manufacturing analysis.
Conference Paper
Additive Manufacturing (AM) is a novel process that enables the manufacturing of complex geometries through layer-by-layer deposition of material. AM processes provide a stark contrast to traditional, subtractive manufacturing processes, which has resulted in the emergence of design for additive manufacturing (DfAM) to capitalize on AM’s capabilities. In order to support the increasing use of AM in engineering, it is important to shift from the traditional design for manufacturing and assembly mindset, towards integrating DfAM. To facilitate this, DfAM must be included in the engineering design curriculum in a manner that has the highest impact. While previous research has systematically organized DfAM concepts into process capability-based (opportunistic) and limitation-based (restrictive) considerations, limited research has been conducted on the impact of teaching DfAM on the student’s design process. This study investigates this interaction by comparing two DfAM educational interventions conducted at different points in the academic semester. The two versions are compared by evaluating the students’ perceived utility, change in self-efficacy, and the use of DfAM concepts in design. The results show that introducing DfAM early in the semester when students have little previous experience in AM resulted in the largest gains in students perceiving utility in learning about DfAM concepts and DfAM self-efficacy gains. Further, we see that this increase relates to greater application of opportunistic DfAM concepts in student design ideas in a DfAM challenge. However, no difference was seen in the application of restrictive DfAM concepts between the two interventions. These results can be used to guide the design and implementation of DfAM education.
Conference Paper
Design for manufacturing provides engineers with a structure for accommodating the limitations of traditional manufacturing processes. However, little emphasis is typically given to the capabilities of processes that enable novel design geometries, which are often a point of focus when designing products to be made with additive manufacturing (AM) technologies. In addition, limited research has been conducted to understand how knowledge of both the capabilities (i.e., opportunistic) and limitations (i.e., restrictive aspects) of AM affects design outcomes. This study aims to address this gap by investigating the effect of no, restrictive, and both, opportunistic and restrictive (dual) design for additive manufacturing (DfAM) education on engineering students’ creative process. Based on the componential model of creativity [1], these effects were measured through changes in (1) motivation and interest in AM, (2) DfAM self-efficacy, and (3) the emphasis given to DfAM in the design process. These metrics were chosen as they represent the cognitive components of ‘task-motivation’ and ‘domain relevant skills’, which in turn influence the learning and usage of domain knowledge in creative production. The results of the study show that while the short (45 minute) DfAM intervention did not significantly change student motivation and interest towards AM, students showed high levels of motivation and interest towards AM, before the intervention. Teaching students different aspects of DfAM also resulted in an increase in their self-efficacy in the respective topics. However, despite showing a greater increase in self-efficacy in their respective areas of training, the students did not show differences in the emphasis they gave to these DfAM concepts, in the design process. Further, students from all three education groups showed higher use of restrictive concepts, in comparison to opportunistic DfAM.
Additive manufacturing (AM) has unique capabilities when compared to traditional manufacturing, such as shape, hierarchical, functional, and material complexity, a fact that has fascinated those in research, industry, and the media for the last decade. Consequently, designers would like to know how they can incorporate AM's special capabilities into their designs, but are often at a loss as how to do so. Design for Additive Manufacturing (DfAM) methods are currently in development but the vast majority of existing methods are not tailored to the needs and knowledge of designers in the early stages of the design process. Therefore, we propose a set of process-independent design heuristics for AM aimed at transferring the high-level knowledge necessary for reasoning about functions, configurations, and parts to designers. Twenty-nine design heuristics for AM are derived from 275 AM artifacts. An experiment is designed to test their efficacy in the context of a re-design scenario with novice designers. The heuristics are found to positively influence the designs generated by the novice designers and are found to be more effective at communicating DfAM concepts in the early phases of re-design than a lecture on DfAM alone. Future research is planned to validate the impact with expert designers and in original design scenarios.
Conference Paper
Additive manufacturing (AM) provides engineers with nearly unlimited design freedom, but how much do they take advantage of that freedom? The objective is to understand what factors influence a designer’s creativity and performance in Design for Additive Manufacturing (DFAM). Inspired by the popular Marshmallow Challenge, this exploratory study proposes a framework in which participants apply their DFAM skills in sketching, CAD modeling, 3D-Printing, and a part testing task. Risk attitudes are assessed through the Engineering Domain-Specific Risk-Taking (E-DOSPERT) scale, and prior experiences are captured by a self-report skills survey. Multiple regression analysis found that the average novelty of the participant’s ideas, engineering degree program, and risk seeking preference were statistically significant when predicting the performance of their ideas in AM. This study provides a common framework for AM educators to assess students’ understanding and creativity in DFAM, while also identifying student risk attitudes when conducting an engineering design task.
Conference Paper
Additive manufacturing (AM) is gaining popularity in industrial applications including new product development, functional parts, and tooling. However, due to the differences in AM technologies, processes, and process implementations, functional and geometrical characteristics of manufactured parts can vary dramatically. Planning, especially selecting the appropriate AM process and material requirements can be rather involved. Manufacturability using AM processes has been well studied; however, gaps exist in the design process when catering to the needs of manufacturability. Designers today are challenged with a lack of understanding of AM capabilities, process-related constraints, and their effects on the final product. Challenges are compounded by the ambiguity of where design for AM ends and process planning begins. These ambiguities can be addressed through design principles and corresponding design rules for additively manufacturing parts. The purpose of this paper is to categorically present relevant and reported efforts in design and process planning with design rules in AM. The overarching goal of the review is to offer insights to extract and categorize fundamental principles for derivative rules for different AM processes. Identifying such fundamental requirements could potentially lead to breakthroughs in design and process planning.