Design for Additive Manufacturing Using a Master Model Approach

Abstract

The introduction of Additive Manufacturing opens up possibilities for creating lighter, better and customized products. However, to take advantage of the possibilities of Additive Manufacturing, the design engineer is challenged. In this paper, a general design process for the creation of complex products is proposed and evaluated. The proposed method aims to aid a design process in which Topology Optimization (TO) is used for concept development, and the result is then interpreted into a Master Model (MM) supporting design evaluations during detailed design. At the same time as the MM is created, information regarding manufacturing is saved in a database. This makes it possible to automatically generate and export models for manufacturing or CAE analyses. A tool that uses Knowledge-Based Engineering (KBE) to realize the presented methodology has been developed. The tool is specialized for the creation of structural components that connect to other components in an assembly. A case study, part of an aircraft door, has been used for evaluation of the tool. The study shows that the repetitive work when interpreting the topology-optimized design could be reduced. The result comes in the form of a parametric CAD model which allows fast changes and the coupled database enables the export of models for various purposes.

Industrial evaluation of design heuristics for additive manufacturing

“…This desire to see more examples is a sentiment echoed by the novice designers of the previous DHAM user studies (Blösch 2020). As this feedback is coming from both novices and experts, some more examples could be added, but maximum one or two per heuristic, because if too many examples are added it becomes a different type of design support method, e.g., a design catalogue, like Weiss et al (2015), instead of heuristics. Another option would be to include access to a database similar to that of Bin Maidin (2011) that connects each of the DHAM to the AM artefacts used to derive it, so as to give the users another way to access more examples if desired, but keep the number of immediate examples low.…”

“…Table 1 lists these 18 characteristics and provides references to the supporting literature. Although much additional research in this area has been published since the survey was conducted in 2019 (e.g., Perez et al 2019;Prabhu et al 2019;Schumacher et al 2019;Valjak & Bojčetić 2019;Watschke et al 2019;Wiberg, Persson, & Ölvander 2019;Dordlofva & Törlind 2020;Prabhu et al 2020b;Valjak et al 2020), it was conciously decided not to update Table 1, because of the results' direct connection to the development of the questionnaire used to evaluate the method (see Sections 3-3.3).…”

“…Early design phase DfAM often has a strong opportunistic DfAM component (information about the unique capabilities of AM; Gibson et al 2015) as opportunistic design support assists designers in expanding their AM design horizons, something that is key in the early design phases (Ulrich & Eppinger 2008). However, some early-phase DfAM methods also include restrictive DfAM information (information about the restrictions of the AM process; e.g., Laverne et al 2015; Weiss, Binz, & Roth 2015; Kumke et al 2017; Watschke et al 2017; Perez 2018) to help users make the transition from the early design phases into the later phases of the design process (e.g., detailed design) without having to use or learn an additional method. The usefulness of these different types of design support in the early phases has not been studied in detail, and there are varying opinions on the inclusion of restrictive DfAM in early-phase DfAM methods (see Table 1).…”

Additive manufacturing in fluid power with novel application to hydraulic pump design

“…During the system design step, concept generation and ideation methods were used to identify the boundaries between the individual components and different potential overall designs was identified. During the part design step a design automation framework presented in (Wiberg et al, 2023) was utilised. This framework allows easy creation of parametric flow and structural features which can be adopted to specific needs and allows automatic creation of simulation models.…”