Résumé
Additive manufacturing (AM) framework enables the customization of geometries regardless of the number of parts to manufacture. Hence, the right product can be provided to the right customer. However, traditional optimization methods, used to improve performance and mass, are usually non-parametric. Therefore, the process flow shall be repeated even if all parts in a library display similar functions and overall geometry. As such, the relation between development time and the number of parts to design is linear. Hence, one cannot afford to generate large libraries using these approaches.
In this paper, we propose an alternative solution for structural design, inspired by Knowledge Based Engineering. First, topology optimization is used to emulate a prior knowledge of optimal geometry. As this result is nonparametric, it is approximated using low complexity elements such as shells and beams. Then, batch optimization is run to model the optimal geometry in function of the load case. Finally, the ready to print part is reconstructed using traditional parametric CAD software.
We benchmarked our methodology with the Design for AM (DfAM) of a library of chassis components. Thanks to the selective complexity reduction, the computation time is reduced significantly compared to traditional approaches. Furthermore, the parametrization of the part and the modeling of its behavior rationalize the design of families of parts. Indeed, most activities are only executed once.
In this manner, our methodology uncouples the development time from the number of load cases allowing to design for AM entire libraries of similar part.