Impact on Design When Introducing Additive Manufacturing in Space Applications

Borgue, Olivia; Panarotto, Massimo; Isaksson, Ola

doi:10.21278/idc.2018.0412

Cited by 4 publications

(6 citation statements)

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“…Lastly, AM constraints were introduced in the function tree, and the component was redesigned for AM. Details concerning this method can be found in work by Borgue et al [21].…”

Section: Methodsmentioning

confidence: 99%

“…The method is structured into six phases, from decomposition of the baseline design to manufacturing of the AM-optimized new design. The first three phases build on previous work by Borgue et al [21]. In this article, three more phases were included as presented in Figure 1.…”

Section: Proposed Dfam Methodsmentioning

confidence: 99%

“…While the opportunity-driven and manufacturing-driven approaches initially seem to be exclusive, they are often combined. Research suggests that, as knowledge about AM processes and constraints is limited and in constant evolution, modeling manufacturing constraints can support designers in efficiently managing and using that knowledge [21].…”

Section: Design For Am In Space Applicationsmentioning

confidence: 99%

“…In the proposed method, a distinction is made between manufacturing constraints (Cms) and functional constraints (Cfs). This is not part of the original EF-M theory but has proven beneficial in a redesign for an AM process [21]. Manufacturing constraints depend on the manufacturing process, such as minimum manufacturable wall thickness, while, in contrast, Cfs depend on FRs such as the maximum pressure a pipe needs to endure.…”

Section: Phase 1: Functional Decomposition and Modelingmentioning

confidence: 99%

“…Then, those FRs, DSs, and Cs were organized in a hierarchical function tree, as shown in Figure 3, where DSs were connected with other DSs through "iw" connections and Cs through "icb" connections. More details about this process can be found in previous work by Borgue et al [21].…”

Section: Functional Decompositionmentioning

confidence: 99%

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Constraint Replacement-Based Design for Additive Manufacturing of Satellite Components: Ensuring Design Manufacturability through Tailored Test Artefacts

et al. 2019

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Additive manufacturing (AM) is becoming increasingly attractive for aerospace companies due to the fact of its increased ability to allow design freedom and reduce weight. Despite these benefits, AM comes with manufacturing constraints that limit design freedom and reduce the possibility of achieving advanced geometries that can be produced in a cost-efficient manner. To exploit the design freedom offered by AM while ensuring product manufacturability, a model-based design for an additive manufacturing (DfAM) method is presented. The method is based on the premise that lessons learned from testing and prototyping activities can be systematically captured and organized to support early design activities. To enable this outcome, the DfAM method extends a representation often used in early design, a function–means model, with the introduction of a new model construct—manufacturing constraints (Cm). The method was applied to the redesign, manufacturing, and testing of a flow connector for satellite applications. The results of this application—as well as the reflections of industrial practitioners—point to the benefits of the DfAM method in establishing a systematic, cost-efficient way of challenging the general AM design guidelines found in the literature and a means to redefine and update manufacturing constraints for specific design problems.

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“…Lastly, AM constraints were introduced in the function tree, and the component was redesigned for AM. Details concerning this method can be found in work by Borgue et al [21].…”

Section: Methodsmentioning

confidence: 99%

Section: Proposed Dfam Methodsmentioning

confidence: 99%

Section: Design For Am In Space Applicationsmentioning

confidence: 99%

Section: Phase 1: Functional Decomposition and Modelingmentioning

confidence: 99%

Section: Functional Decompositionmentioning

confidence: 99%

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