Multidisciplinary design optimization in design for additive manufacturing

A general MDO framework for simultaneously product-material-process design problems in DfAM and Pareto sets comparison for different optimization strategies. Design for additive manufacturing (AM) involves decision making in various design domains, including product design, material selection, and process planning. In practice, engineers typically adopt a sequential design process to optimize these design domains in consecutive order. However, coupling factors, e.g. shared variables, related constraints, and conflicting objectives, are not sufficiently considered within the sequential design process, resulting in an inefficient workflow and suboptimal design solutions. To address the above issues, this paper proposes a multidisciplinary design optimization framework to simultaneously optimize different domains, which enables rapid exploration and complete exploitation of the AM design space under complex constraints. More specifically, the proposed framework is based on the concurrent optimization method, which coordinates the optimization of different design domains by allowing an automated exchange of design information. Also, the framework utilizes the surrogate modeling approach to approximate high-fidelity simulations for facilitating the iterative process. The effectiveness of the proposed framework is validated with two examples, a plate with a hole design and a hook design, which involve multiple design objectives from both process and structure domains, i.e. the print time, print area, strain energy, and maximum von Mises stress.

Automated summary

This paper proposes a multidisciplinary design optimization framework for simultaneously optimizing product design, material selection, and process planning in additive manufacturing. The framework utilizes concurrent optimization and surrogate modeling methods to enable rapid exploration and full utilization of the design space in AM.