Stress concentrations and design for additive manufacturing: a design artefact approach to investigation

Abstract

The accelerated rate of product development and design complexities offered by Additive Manufacturing (AM) has allowed for innovation in the space industry. However, the surface roughness of parts poses a challenge, as it impacts performance and is tied to design choices. Design tools for traditional manufacturing methods fall short in AM contexts, prompting the need for alternative design processes. This work proposes an experimental approach to design for AM investigation using design artefacts to explore a process-structure-property-performance relationship.

Full text

DESIGN FOR ADDITIVE MANUFACTURING 1789
INTERNATIONAL DESIGN CONFERENCE – DESIGN 2024
https://doi.org/10.1017/pds.2024.181
Stress concentrations and design for additive manufacturing:
a design artefact approach to investigation
Didunoluwa Obilanade 1, , Owen Rahmat Peckham 2, Adam McClenaghan 2,
James Gopsill 2 and Peter Törlind 1
1 Luleå University of Technology, Sweden, 2 University of Bristol, United Kingdom
Didunoluwa.Obilanade@ltu.se
Abstract
The accelerated rate of product development and design complexities offered by Additive Manufacturing
(AM) has allowed for innovation in the space industry. However, the surface roughness of parts poses a
challenge, as it impacts performance and is tied to design choices. Design tools for traditional manufacturing
methods fall short in AM contexts, prompting the need for alternative design processes. This work proposes
an experimental approach to design for AM investigation using design artefacts to explore a process-structure-
property-performance relationship.
Keywords: additive manufacturing, design artefact, design for additive manufacturing,
surface roughness, prototyping
1. Introduction
Additive Manufacturing (AM) is increasingly becoming an economically and commercially viable
manufacturing method, typically involving fewer processes and resources than alternative subtractive
manufacturing methods (Gibson et al., 2020). The layer-by-layer process provides designers with unique
shape, hierarchical, functional, and material complexity capabilities, enabling new opportunities for
customisation and lowering manufacturing costs (Gibson et al., 2020). AM’s capabilities allow generative
design (GD) methods to create low-weight metal parts in the space industry (Samal et al., 2022).
Applications of GD and AM have demonstrated that space product development times can be reduced ten-
fold while also providing a three-fold improvement in structural performance (McClelland, 2022). With
AM, designers can use topology optimisation (TopOp) techniques to design and manufacture lightweight
brackets (Reiher and Koch, 2016). The University of Paderborn conducted a TopOp study for a biomimetic
shape for a reaction wheel bracket and found the AM TopOp structure reduced waste by 98%,
manufacturing time by 32%, and cost by 53% (Universität Paderborn, 2016). However, AM process factors
bring design challenges and uncertainties that must be considered in the early phases of product
development (Renjith et al., 2020; Thompson et al., 2016). These process factors affect design choices and
impact material characteristics such as part buildability, fatigue properties, and overall performance (Gradl
et al., 2023). A challenge in designing AM space components is the inherent roughness of the as-built
surface. This roughness is closely linked to a part’s design due to the layered manufacturing process, which
produces a staircase effect (Gradl et al., 2023). This staircase effect, in turn, acts as micro notches for stress
concentrations that impact fatigue performance (du Plessis and Beretta, 2020). Fatigue performance is
critical for satellite brackets as they must withstand the high forces experienced during launch.
Structural sensitivity indexes derived from empirical testing, such as stress concentration factors (SCF)
in traditional manufacturing, support designers in comprehending how geometric features influence
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1790 DESIGN FOR ADDITIVE MANUFACTURING
performance (Pilkey and Pilkey, 2007). SCFs provide easily understandable and applicable knowledge
that designers can apply when designing for subtractive manufacturing methods. When such knowledge
is integrated with computer-aided design (CAD) software tools, it provides analytics supporting
designers in assessing process–structure–property–performance relationships (PSPP). However, there
are significant challenges in understanding the PSPP relationships in Design for AM (DfAM) due to the
complexity and multitude of process parameters (Hashemi et al., 2022). Most CAD tools are not yet
well adapted for the PSPP representation of AM-designed structures, nor have the embedded heuristics
to suggest certain features (e.g., raft or chamfer) would be detrimental to performance (Nazir et al.,
2019). Consequently, designers require methods to acquire this design knowledge to help them
understand AM process capability and effectively manage their limitations.
Parametric feasibility studies can be conducted to investigate and provide knowledge on design-property
relationships (Jones et al., 2021; Zhou et al., 2021). However, in the early phases of product
development, with many ideas proposed, extensive studies would be required to understand the
feasibility and performance of different design concepts long before specifications are set. Prototyping
can provide an understanding of design-performance relationships for AM components (Thompson et
al., 2016). Ulrich and Eppinger (2012) classify prototypes along two dimensions: physical and
analytical. Physical prototypes are tangible representations of a design that can be tested and
experimented with. In contrast, an analytical prototype represents the design mathematically or visually
to analyse interesting aspects of the product, such as a Finite Element Analysis (FEA). Ulrich and
Eppinger (2012) further describe that prototypes are used for four purposes: learning, communication,
integration, and milestones. Lawrence (2003) suggests designers consider the ‘right-rapid-rough’
approach to prototyping to foster innovation when solving design problems. ‘Right’ implies designing
a prototype to address a specific question, i.e., targeting the prototype to the ‘right’ question a designer
wants answered rather than multiple. ‘Rapid’ means that a prototype should be quick to design, allowing
a designer to simulate and test a design challenge quickly. Finally, ‘rough’ suggests that a prototype
does not need to be pretty in describing the design uncertainty; it should be rough enough to provide
knowledge while allowing the designer to focus on the main design solution (Lawrence, 2003).
The contribution of this paper is the proposition of an experimental methodology to investigate design
uncertainties related to using GD for AM parts. The paper commences by providing a background on
practices and guidance that assist designers in gaining knowledge on PSPP relationships, highlighting a
design support process utilising design artefacts for knowledge generation (Section 2). Section 2 further
delves into an AM use case in the space industry where PSPP knowledge is limited, and design
uncertainties for buildability and the impact of roughness on stress concentration are identified.
Subsequently, the research methodology (Section 3) through the design support process, including the
design logic and the experimental design, is presented. Then, simulated results are given (Section 4).
The paper concludes with a discussion on the considerations for PSPP relationship investigations when
using design artefacts, followed by conclusions that outline future work (Sections 5 &6).
2. Background and related work
Guidelines for understanding PSPP relationships for AM are available through standards (ISO/ASTM,
2017, 2018). ISO/ASTM 52910 provides general design-performance support, whereas ISO/ASTM
TC261 (2018) suggests test artefact designs for investigating the geometric capability of AM systems.
Nevertheless, the standards and suggested artefacts fall short in providing support for complex
geometries, prompting researchers to modify the designs of standard test specimens to better investigate
design uncertainties. Benedetti et al. (2016) devised a test specimen design, slightly deviating from
typical push-pull axial fatigue specimens, to investigate the effects of surface roughness on the fatigue
limit of metal AM components. Their modification aimed to better capture the effects of roughness,
which they found were inadequately revealed with standard hourglass specimens.
Others have developed more PSPP-focused artefacts to investigate design-related performance factors
for AM. Zhou et al. (2021) found that fluid channels made through AM had higher friction factors than
expected from classical theory. To better understand this, they manufactured a series of small fluid
channels with varying diameters and build angles to characterise the fabrication quality and measure
friction factors. From their findings, they developed design guidelines and a model for predicting the
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DESIGN FOR ADDITIVE MANUFACTURING 1791
friction factors of AM-produced fluid channels that consider the fabrication quality. These activities can
be time-intensive; hence, the investment may be more worthwhile during the later design stages when
the specifications are further set.
Dordlofva and Törlind (2020) observed the use of product-specific AM design artefacts (AMDA) by
engineers in the space industry to investigate and explore AM design uncertainties. Their observations
revealed that design artefacts can be used to inspire designers’ solutions for utilising AM potentials.
Inspired by the prototyping design process of the IDEO design consultancy (Hartmann, 2009), the
AMDA process, depicted in Figure 1 is a systematic approach to identify, explore, and reduce
uncertainties in AM design when inspiring, evolving, and validating solutions for AM design. The
learnings from design artefacts in the early phase can be used to drive design specifications, particularly
during the concept phase. In contrast, the design of artefacts, when used later in the development
process, when the product is more detailed, is driven by the product specification.
Figure 1. The design process with AMDAs (adapted from Dordlofva and Törlind (2020))
The AM of components by space industry companies has allowed for rapid cost-competitive innovation
in an industry where product development cycles can be slow and low-weight part designs are beneficial
(Sacco and Moon, 2019). AM production can hasten product development; however, the pace can be
hindered by a designer’s lack of DfAM understanding (Lindwall, 2023). Designers need to decide early
in the product development process if AM is feasible for their product idea. Thus, it is important to
understand PSPP relationships, such as buildability and surface roughness-induced stress concentrations
in complex geometries, when considering taking advantage of AM design opportunities (Nicoletto et al.,
2020). A lack of understanding brings design uncertainties for engineers exploring AM design solutions.
2.1. Directed Energy Deposition in the space industry
The AM process of Directed Energy Deposition (DED) melts material, either in powder or wire form,
using a heat source as it is deposited (Gibson et al., 2020). DED processes provide high material usage
efficiency in comparison to subtractive manufacturing methods, enabling the manufacture of metal
component designs with reduced weight, reduced part numbers and quicker production times (Thompson
et al., 2015). The rocket manufacturer Relativity Space used wire-DED to manufacture the fuselage of
their Terran 1 rocket, which was launched in 2023. They found that the DED process readily allowed for
incremental design changes, enabling them to reduce part numbers and optimise material usage faster than
traditional manufacturing processes (Relativity Space, 2023). DED processes often require finish
machining due to relatively poor part accuracy and higher surface roughness than other AM processes in
the as-built state (Gibson et al., 2020), which increases the cost and the time for production (Ding et al.,
2015). In an optimal manufacturing scenario, a part could be used in its as-built state. For instance, despite
the as-built rough surfaces of the Terran rocket tank accounting for roughly 5-10% of the mass, Relativity
Space does not remove the surface roughness as it causes no aerodynamic problems (Veritasium, 2021).
Another AM process used in the space industry is Laser Powder Bed Fusion (LPBF), which uses a laser
as a heat source and powdered metal material to create parts. LPBF is used for manufacturing high-
strength-to-weight satellite brackets (Samal et al., 2022). However, considering the increased rate of
Uncertainties
Inspire
Project
time
Evolve Validate
Identified
uncertainty
Print
Design
AMDA
Evaluate/
test (2)
(1)
(4)
(3)
AMDA-driven specifications Specification-driven AMDAs
Evolve
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1792 DESIGN FOR ADDITIVE MANUFACTURING
satellite launches and the associated demand for satellite bracket manufacturing, the higher deposition
rates offered by DED could be beneficial. Additionally, space manufacturers advertise using AM for a
more sustainable manufacturing process and product (Orbex, 2023). LPBF has a high environmental
impact due to the energy demand and argon usage, while the DED process, in contrast, can offer superior
production efficiency and reduced environmental impact per part, particularly in brackets manufacturing
(Min et al., 2019). Further, wire-DED offers better control for deposition efficiency than powder-DED
(Gibson et al., 2020; Thompson et al., 2015). The surface condition and geometrical accuracy of DED
parts tend to be worse than those from powder bed processes, and despite the potential benefits of
designing as-built DED, there is limited support available for designers regarding wire-DED DfAM
(Ding et al., 2015).
2.2. Stress concentration and DED design uncertainty
DfAM support is particularly useful when using GD techniques, as they can produce designs with
considerable geometrical changes, leading to several areas of high-stress concentrations (Benedetti et
al., 2021). Stress concentrations refer to localised areas of a structure that experience higher stress levels
than the average stress distribution across the body (Pilkey and Pilkey, 2007). Design tools like
Peterson’s elastic stress concentration factor (SCF) charts have supported designers for several years
(Pilkey and Pilkey, 2007). However, time-consuming FEA models are needed as parts get more
complicated, and a more accurate understanding of SCF is required (Shanmukha Prasad et al., 2020).
Stress concentration analysis for AM is particularly challenging due to the staircase effect creating
further stress concentration sites and points for crack initiation, impacting fatigue performance (Ding et
al., 2015; ISO/ASTM, 2017). Geometries like sharp radii have high stress concentrations. If designers
wish to lessen the concentrations, they could increase the radius size (Axsom, 2022). However, due to
the material deposition method of wire-DED, increasing the radius leads to a more significant staircase
effect, creating a rougher surface and impacting fatigue performance.
Additionally, as support structure is not used for DED components, there is uncertainty about the quality
and buildability of the unsupported radius. Hence, multiple design uncertainties exist regarding the
buildability of radii, the degree of roughness due to radius variation and the possible influence of
roughness on performance. Further, these design uncertainties are challenging to model using software
tools due to the uncertainty of the SCFs' accuracy for AM applications. In the early phases of product
ideation, when there is no fully defined specification for a product, design artefacts could allow for a
quick and rough investigation into these identified design uncertainties.
3. Design artefact process
GD and the possibility for light weighting and lower costs were identified as potential benefits from as-
built DED brackets. However, before time and effort are spent on generating designs, the design
uncertainties described have been identified regarding this idea. Dordlofva and Törlind (2020) presented
how engineers in the space industry used product-specific design artefacts to investigate design
uncertainties. Without appropriate simulation means for these uncertainties, design artefacts are
proposed as a low-investment option to generate rough design knowledge and investigate these
uncertainties. The following section describes the proposed artefact design and experimental
methodology to investigate and learn about the design uncertainties.
3.1. Artefact design
The first step of the AMDA process is identifying uncertainties. In this case, it is the capability of the
machine to manufacture the radii, how geometrically accurate the radii are, and the degree of surface
roughness and impact on fatigue performance. The second step is designing the artefact, where key
considerations are on which uncertainties will be represented and how. In this case, the artefact is
designed to enable all described uncertainties to be sequentially investigated due to the knock-on effect
of the design variation. A designer must consider their initial specifications as constraints when
designing an artefact to ensure a feasible investigation. Constraints in the ideation stage include the AM
machine, i.e. build volume and available materials. Additionally, the design is constrained by the desired
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DESIGN FOR ADDITIVE MANUFACTURING 1793
equipment as the artefact size and dimensions should consider the fixtures and mounting options of the
test equipment. Similarly, if measurements are required, i.e. radius and surface roughness
measurements, the measurement equipment further constrains the artefact design. For this investigation,
the artefact’s design is based on similar research using design artefacts to investigate a PSPP relationship
by Obilanade et al. (2022). In that work, a design artefact and experiment were proposed by engineers
investigating the performance of a specific radius related to a design uncertainty of an AM rocket engine
turbine manifold. The variations to that artefact design are based on the constraint considerations
described. A diagram of the artefact is presented in Figure 2, and the dimensions are provided in Table
1. The artefact is designed with an open internal lozenge shape design to allow for the investigation into
the impact of varying an unsupported radius (R1) to reduce stress concentrations. The internal radius of
the lozenge shape (R2) will be constant for all artefacts, and the angle between the radii is 65° as
specified by the design guidelines of the AM machine.
Figure 2. Example diagram of the artefact design
Table 1. Artefact geometries (angles between radius base and roof are 65°)
Artefact type
ID
Diagonal width d,
[mm]
Artefact width w,
(mm)
Thickness t,
(mm)
Length l,
(mm)
R1
(mm)
R2
(mm)
A
45
6
8
120
1
4
B
45
6
8
120
3
4
C
45
6
8
120
5
4
D
45
6
8
120
7
4
E
45
6
8
120
9
4
The artefact is designed to investigate the surface roughness and mechanical performance at five radii:
1 mm, 3 mm, 5 mm, 7 mm, and 9 mm. All artefacts will be built with 5 mm additional height as a
support structure at the base to allow for cutting from the base plate and the stack. Five of each artefact
geometry shall be manufactured: one for maximum tensile testing, three for cyclic fatigue loading and
an additional as a spare.
3.2. Experimental design
The artefacts will be manufactured using a Meltio M450 wire-DED machine, and the material used will
be stainless steel with a wrought tensile strength of 550 MPa (Meltio, 2024). Stainless steel is
investigated as it is favourable for making satellite brackets (Samal et al., 2022). Several process-
dependent factors, such as the wire diameter, tool path, feed rate, laser power, infill pattern and feed
orientation, can bring uncertainties for the performance of the geometry and mechanical properties of
wire-DED parts (Ding et al., 2015; Rismalia et al., 2019). Design artefact investigations are conducted
as a controlled study to focus on specific elements of design uncertainty by limiting the parameter space.
In this investigation, the uncertainty is radii variation and its knock-on impact on buildability, surface
roughness and performance. Hence, only one design variable, an overhanging radius, shall be varied,
allowing for a focused investigation into DED build capability, geometric accuracy, and performance
FRONT
d
R1
R2
t
w
Build direction
SIDE
l
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1794 DESIGN FOR ADDITIVE MANUFACTURING
through evaluation and testing. Upon building completion, all artefact radii will be measured to
investigate the geometric accuracy of the radius geometry. The material thickness between the radius
and the top of the artefact will also be measured. Ensuring that the t value is similar for all artefacts is
important for a fair performance assessment. The loading experiment will investigate for the PSPP
relationship. Two tests are proposed to gain insight into how the performance of the artefact is affected
by the design variation. Both tests will be conducted on a 25kN Instron 8872 tensile testing machine,
and the artefacts will be gripped by 12.5 mm wide hydraulic jaws and loaded as shown in Figure 3, fixed
at one end and the load applied at the other. A cut shall be performed at the opposite radius to the radius
under investigation to focus on the R1 radius and to allow flexion for the cyclic fatigue loading.
Figure 3. Diagram of artefact and rationale for testing (red line indicating cut point)
The tests will focus on a fracture failure mode implemented by a stopping condition on the Instron when
the separation of the upper and lower jaws reaches an Fmax= 4kN. A tensile test shall be conducted to
investigate the maximum tensile load each artefact can hold before fracture. This test will be performed
for each artefact variation to inform if 4 kN seems appropriate for the investigation and will provide a
simple control against which the artefacts can be compared. The artefact and investigation design are
purposely simplistic to quickly obtain design and process knowledge.
The second test aims to gauge the effect of varying radii and resulting surface roughness upon the
artefacts’ fatigue fracture resistance. The test will follow the procedure of the ASTM standard E466 for
“Conducting Force Controlled Constant Amplitude Axial Fatigue Tests of Metallic Materials” due to
its widespread use and repeatability (ASTM, 2002). An FEA tensile test was run using Abaqus for each
artefact variation. Once all tests are completed, plots of the maximum load against the simulated
maximum load from the FEA analysis shall be created. A second plot will be created, plotting the
average number of cycles for the cyclic fatigue loading at the various radii.
Table 2. ASTM E466 load-controlled fatigue test standard characteristics
Test method
Load controlled fatigue
Test temperature
Room Temperature
Test environment
Air
Waveform
Sinusoidal
Frequency
10 Hz
Rσ-ratio (ratio of maximum: minimum loading)
0.1
Load range, run-out
500-50,000 cycles
Failure criterion
Fracture
4. FEA simulation results
The FEA modelling used Young’s modulus of 200 GPa and Poisson's ratio of 0.27. A visualisation of
an FEA result is presented in Figure 4, and the analysis results for the Max. Mises stress are presented
and plotted in Figure 5. The model had fixed constraints on one end, matching the shape as in Figure 3,
and a surface traction load on the opposite end of 2.285 MPa, resulting in a 4kN load across the entire
F
Fixed
Clamps - 35 x 12.5 mm
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DESIGN FOR ADDITIVE MANUFACTURING 1795
surface. The investigation anticipates an increase in surface roughness with the radius of the artefacts,
affecting the mechanical properties. The evaluation will involve comparing the maximum tensile load
of the artefacts at the various radii for similarity with the FEA’s maximum von Mises stress plot.
Figure 4. Visualisation of FEA results for artefact E (R1 = 9 mm) (stress is given in MPa)
Figure 5. FEA results for Max. von Mises stress of the varying radii
Figure 5 indicates decreasing stresses with increasing radius, suggesting improved mechanical
performance. A saturation in the stress values can be seen in Figure 5. SCF graphs derived from
empirical testing of subtractive parts show that SCFs do not increase linearly with size (Pilkey and
Pilkey, 2007). It can be assumed that the SCF of the radius also does not increase linearly as the radius
decreases. The results of the artefact testing are expected to reveal higher stresses for a given tensile
load than FEA predicts, as the FEA does not account for surface roughness. This expectation will be
investigated by comparing the FEA-calculated stresses to the stress-strain curve produced during tensile
testing. Hence, testing through design artefacts will provide a designer insight into this influence, if it
impacts as expected, and, if so, how design variation may affect performance.
5. Discussion
This work proposes an experimental methodology for investigating design uncertainty in DED for GD
AM components using a series of design artefacts. The proposed artefact design and experiment aim to
provide an understanding of the DED process capability and the possible impact of as-built surface
roughness on fatigue performance. They are addressing potential uncertainties related to the effect of
increasing the radius for stress concentration reduction and improved mechanical performance. A
satellite bracket designer anticipates forces during operation; however, uncertainties arise in as-built
AM structure behaviour at complex geometries due to unknown surface conditions. Variations in radius
geometry can be implemented to improve performance and would impact performance in non-AM parts,
however, traditional design supports like SCFs and CAD programs are available to help analyse these
impacts, as demonstrated with the FEA analysis. With the many other variables in AM processes, such
as residual stresses and material composition due to process parameter choice, modelling becomes
computationally intensive and time-consuming in the early phase. Additionally, SCF tables are not
directly applicable to AM parts. If any build parameters are changed, an SCF table produced for an AM
process would be difficult to generalise or inapplicable. Necessitating the need for design support
processes like the AMDA process to investigate PSPP relationships. Post-processing via processes like
Max. Mises
stress [MPa]
Radius size
[mm] 380.11 258.63 2285 216.57 210.69
0
50
100
150
200
250
300
350
400
1 3 5 7 9
Max. Mises Stress [MPa]
Artefact R1 radius [mm]
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1796 DESIGN FOR ADDITIVE MANUFACTURING
milling can be done to improve the surface condition. However, the additional cost of milling, reduction
in product development speed, and overall performance improvement are also unknown to the designer
in the early phase. These types of artefacts could provide an understanding of the post-processing
necessity for the selected AM process, i.e., the feasibility of a design to meet requirements without post-
processing.
Using design artefacts could help explore the design options beyond the information available in
standard guides, published design rules and CAD systems while allowing designers to understand the
functional impact of a creative decision. Using the AMDA process in the early phase prompts designers
to detail considerations for AM usage. When a design uncertainty is identified, the designer must
consider the influencing factors to that uncertainty in case further uncertainties can be identified. In this
case, using GD and as-built DED brings uncertainties related to the machine's capability to produce
geometries and the surface roughness on the fatigue performance. During artefact design, designers
articulate initial assumptions to define uncertainty representation, considering practical constraints.
Early decisions on build parameters are considered, and an understanding of the likelihood of build
success is revealed during the print stage. Finally, in the evaluate/test activity, as discussed by Dordlofva
and Törlind (2020), the early specifications are considered, and design knowledge output. Figure 6,
illustrates a model of the artefact design process, describing the presented work, future work, and key
considerations during the stages of the prescribed AMDA process.
Figure 6. Model of the described design process
Considering the right-rapid-rough approach to prototyping, the choice of testing procedure can be
adapted to allow for a rough understanding of PSPP relationships rather than following a standard or
guide if it does not have information regarding the variations due to the chosen process.
The experiment proposed will investigate the possible benefit of design artefacts to generate general
design knowledge faster than more specific parametric feasibility studies. In AM, many factors need
consideration for their impact on the mechanical properties of a component. The design of this
experiment purposefully focuses on the potential impact of as-built surface condition through
comparison to a non-AM process FEA simulation. Using artefacts like the design in this work and
varying process factors like tool path, infill pattern, and feed orientation instead of the radius could allow
for investigations into other types of uncertainties. The results of this type of prototyping method may
not directly apply to the eventual design solution, as the ‘roughness’ of the representation of the
uncertainty in the artefact design and the experimental setup limits the result's applicability. However,
with the lack of standardised PSPP support, focused rough prototyping provides a method of considering
design uncertainties and building knowledge in the early phase.
6. Conclusions
In proposing the use of DED for a GD AM component and the use of and design of a design artefact for
design investigation, the following considerations and points have been discussed in this paper:
• There are challenges in understanding PSPP relationships in DfAM due to the complexity of AM
processes. E.g., unknown surface conditions in complex geometries create uncertainties in the
behaviour of as-built AM structures, making traditional design supports less directly applicable.
AM inspired design idea
Generative design space bracket
As built DED
Lightweight and low cost
(1) Identified uncertainties
Machine capability
Geometric adherence
Surface roughness
Fatigue performance
(2) Design Artefact
Uncertainty representation
Machine Constraints
Testing equipment constraints
Measurement equipment constraints
(3) Print
Build parameters
Material selection
Build success
(4) Evaluate/Test
Test parameters
Measurement tools
Simulation comparison
Design knowledge output
Buildable radii
Geometric quality
Fatigue performance
AMDA process (--- future work)
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DESIGN FOR ADDITIVE MANUFACTURING 1797
• The process of creating design artefacts may be used to explore and consider design uncertainties
in the early phases of product development due to considering the known constraints of the machine,
testing capability and measurement tools.
• AM modelling complexities, e.g., residual stresses, are computationally intensive; hence, artefacts
as an adaptive prototyping approach may be beneficial for design investigations.
• Rough prototyping may provide practical utility and offer useful early-phase insights, but
representation constraints limit their direct application to the final design.
An analysis of the variety of ways designers investigate PSPP uncertainties during the AM product
development process should be conducted to provide a comparison against the use of design artefacts.
Future work will focus on printing and testing the artefacts following the procedure presented in this
paper. Further, microstructural analysis of the artefacts could also be conducted to investigate other
properties affecting performance.
Acknowledgement
The authors acknowledge the financial support from The LTU Graduate School of Space Technology,
The EU regional growth project RIT (Space for Innovation and Growth) and GKN Aerospace.
Acknowledgement also to the University of Bristol Design and Manufacturing Futures lab.
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