Additive manufacturing (AM) is currently one of the most promising industrial technologies that allow designers to operate with more degrees of freedom to create shapes without overthinking restrictive manufacturing constraints. Products must be conceived with the “AM on mind” to exploit AM potentialities. Design for AM (DfAM) methods and tools, such as topology optimization and generative design, are crucial for this aim. The present paper aims to understand how existing DfAM tools can effectively support the DfAM process. The study is based on the definition and application of a systematic evaluation protocol consisting of quantitative and qualitative metrics. The case studies involved four commercial DfAM tools tested on three mechanical components. Results confirmed that most of the tools lead to very similar solutions from the technical point of view since they are based on analogous optimization algorithms. The consideration of manufacturability constraints and the availability of advanced functionalities for geometry reconstruction after the optimization phase are relevant issues observed. Finally, regarding tools functionalities, notable differences have been registered.
Design for Additive Manufacturing Tools: Are They an Effective Support for Designers? / Marconi, M.; Zanini, A.; Favi, C.; Mandolini, M.. - (2023), pp. 980-992. (Intervento presentato al convegno International Joint Conference on Mechanics, Design Engineering and Advanced Manufacturing, JCM 2022 tenutosi a ita nel 2022) [10.1007/978-3-031-15928-2_86].
Design for Additive Manufacturing Tools: Are They an Effective Support for Designers?
Favi C.;
2023-01-01
Abstract
Additive manufacturing (AM) is currently one of the most promising industrial technologies that allow designers to operate with more degrees of freedom to create shapes without overthinking restrictive manufacturing constraints. Products must be conceived with the “AM on mind” to exploit AM potentialities. Design for AM (DfAM) methods and tools, such as topology optimization and generative design, are crucial for this aim. The present paper aims to understand how existing DfAM tools can effectively support the DfAM process. The study is based on the definition and application of a systematic evaluation protocol consisting of quantitative and qualitative metrics. The case studies involved four commercial DfAM tools tested on three mechanical components. Results confirmed that most of the tools lead to very similar solutions from the technical point of view since they are based on analogous optimization algorithms. The consideration of manufacturability constraints and the availability of advanced functionalities for geometry reconstruction after the optimization phase are relevant issues observed. Finally, regarding tools functionalities, notable differences have been registered.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
