Utilizing Generative Design for Additive Manufacturing

Ntintakis, Ioannis; Stavroulakis, Georgios Ε.; Sfakianakis, Georgios E.; Fiotodimitrakis, Nikolaos

doi:10.1007/978-981-16-7787-8_78

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…• Assess the cost-effectiveness of a component in relation to the manufacturing process (Kampker et al, 2019;Kazmer et al, 2023) • Identify the potential of AM (Blosch-Paidosh and Shea, 2021;Brennan et al, 2021; Lachmayer and Lippert, 2020) • Identifying & specifying problems related to AM (Renjith et al, 2020;Toguem et al, 2020) • Provide inspiration through existing components (Bender and Gericke, 2021) • Create system architecture/product architecture to identify potentials and contradictions (Ganter et al, 2021;Valjak and Bojčetić, 2023) • Adopting functions and shapes from nature (Lachmayer and Lippert, 2020) • Create ideas and solve problems with the Contradiction Matrix (Gross et al, 2018;Mazlan et al, 2022) • Optimising design for AM-processes (e.g. fused filament fabrication (Djokikj and Kandikjan, 2022), laser powder bed fusion (Herzog et al, 2022), laser metal deposition (Ewald and Schlattmann, 2018)) • Numerical generation of multiple solutions for defined constraints (Ntintakis et al, 2022) • Facilitate the selection of suitable software for the application (Nieto and Sánchez, 2021) • Support selection of print material (Lachmayer and Lippert, 2020) • Support selection of printing process (Lachmayer and Lippert, 2020) • Provide checklists adapted to the design phases (Kumke, 2018). A selection of intended effects through the use of AM and claims given by the design support must be assessed in terms of application and outcome.…”

Section: Output Validationmentioning

confidence: 99%

A proposal for guiding the selection of suitable DfAM support based on experiential knowledge

Schmitt,

Siewert,

Gericke

2024

Proc. Des. Soc.

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Unlocking additive manufacturing's (AM) potential requires designer expertise. Design for additive manufacturing (DfAM) addresses this need but faces barriers, such as uncertainty in scope of integration, design support selection, result validation or time investment for incorporating design support. This paper proposes a framework aligning SCRUM (agile framework) to aid designers in overcoming those barriers. The goal is to pave the way for a better exchange between academia and industry and fostering iterative development of DfAM support tailored to designer needs.

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Section: Output Validationmentioning

confidence: 99%

A proposal for guiding the selection of suitable DfAM support based on experiential knowledge

Schmitt,

Siewert,

Gericke

2024

Proc. Des. Soc.

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“…In their 2022 work 'Utilizing Generative Design for Additive Manufacturing', Ntintakis et al re-designed three pneumatic cylinder mounting brackets using Fusion 360 [3], concluding that the GD tool enabled a roughly 60% weight reduction. Regardless of detailed documentation of the setup process, little explanation is given for some of the choices made, leaving doubt that the setup used would result in a theoretical minimum mass for the component.…”

Section: Variability In Gdmentioning

confidence: 99%

“…As new technologies such as Generative Design (GD) and Additive Manufacturing (AM) are posed as potential revolutions in mechanical engineering, it is important to evaluate the potential of using AM to produce GD parts and whether current approaches can consistently deliver the heavily optimized components that are promised. Whilst previous work has rigorously evaluated GD for AM, little consideration has been given to the sensitivity of the overall process to both the chosen design process and external factors [1][2][3]. This study aims to highlight the large range of possible performances when using GD for AM so that further research can follow to increase the reliability of the process, raising the technology readiness level.…”

Section: Introductionmentioning

confidence: 99%

Investigating and Characterizing the Systemic Variability When Using Generative Design for Additive Manufacturing

Peckham,

Elverum,

Hicks

et al. 2024

Applied Sciences

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This paper demonstrates the unpredictability of outcomes that result from compounding variabilities when using generative design (GD) coupled with additive manufacturing (AM). AM technologies offer the greatest design freedom and hence are most able to leverage the full capability of generative design (GD) tools and thus maximize potential improvements, such as weight, waste and cost reduction, strength, and part consolidation. Implicit in all studies reported in the literature is the fundamental assumption that the use of GD, irrespective of user experience or approach followed, yields high-performing and/or comparable design outputs. This work demonstrates the contrary and shows that achieving high performance with GD tools requires careful consideration of study setup and initial conditions. It is further shown that, when coupled with the inherent variability of AM parts, the potential variation in the performance of the design output can be significant, with poorer designs achieving only a fraction of that of higher-performing designs. This investigation shows how AM by Material Extrusion (MEX), which is used to manufacture components with polylactic acid (PLA), varies through different design pathways, bridging MEX and GD. Through a practical study across nine independently generated designs, the breadth of performance—due to initial GD conditions and MEX part strength unpredictability—is shown to reach 592%. This result suggest that current GD tools, including their underlying workflows and algorithms, are not sufficiently understood for users to be able to generate consistent solutions for an input case. Further, the study purports that training and consideration on GD setup are necessary to apply GD toolsets to achieve high-performing designs, particularly when applied in the context of MEX.

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“…The higher freedom of design of AM enables and empowers the great potential of generative design (GD) and topology optimization (TO) to manufacture lighter and improved mechanically performing structures [11,12]. GD does not use an initial shape but, through an iterative process, searches for the optimal manner of growing the structure by applying a series of geometric and mechanical constraints.…”

Section: Introductionmentioning

confidence: 99%

3D Printing of Low-Filled Basalt PA12 and PP Filaments for Automotive Components

Lupone,

Tirillò,

Sarasini

et al. 2023

J. Compos. Sci.

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Fused Deposition Modeling (FDM) enables many advantages compared to traditional manufacturing techniques, but the lower mechanical performance due to the higher porosity still hinders its industrial spread in key sectors like the automotive industry. PP and PA12 filaments filled with low amounts of basalt fibers were produced in the present work to improve the poor mechanical properties inherited from the additive manufacturing technique. For both matrices, the introduction of 5 wt.% of basalt fibers allows us to achieve stiffness values comparable to injection molding ones without modifying the final weight of the manufactured components. The increased filament density compared with the neat polymers, upon the introduction of basalt fibers, is counterbalanced by the intrinsic porosity of the manufacturing technique. In particular, the final components are characterized by a 0.88 g/cm3 density for PP and 1.01 g/cm3 for PA12 basalt-filled composites, which are comparable to the 0.91 g/cm3 and 1.01 g/cm3, respectively, of the related neat matrix used in injection molding. Some efforts are still needed to fill the gap of 15–28% for PP and of 26.5% for PA12 in tensile strength compared to injection-molded counterparts, but the improvement of the fiber/matrix interface by fiber surface modification or coupling agent employment could be a feasible solution.

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Utilizing Generative Design for Additive Manufacturing

Cited by 6 publications

References 14 publications

A proposal for guiding the selection of suitable DfAM support based on experiential knowledge

A proposal for guiding the selection of suitable DfAM support based on experiential knowledge

Investigating and Characterizing the Systemic Variability When Using Generative Design for Additive Manufacturing

3D Printing of Low-Filled Basalt PA12 and PP Filaments for Automotive Components

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