Topology Optimization of 3D-printed joints under crash loads using Evolutionary Algorithms

In order to take full advantage of the enormous design freedom offered by Additive Manufacturing (AM) technologies, the use of Topology Optimization (TO) methods becomes essential. Although TO is well-established in many disciplines, the problems in vehicle crashworthiness pose severe difficulties for standard, gradient-based approaches, due to high noisiness, multi-modality, and discontinuous nature of the nonlinear simulation responses considered typically as objectives and constraints. In this article, we propose to use Evolutionary Algorithms (EAs) together with a suitable low-dimensional representation in an extended version of the Evolutionary Level Set Method (EA-LSM), able to address complex 3D crash TO problems. The method is used to optimize a 3D-printed metal joint in a hybrid S-rail structure under axial crash loading, inspired by novel frame design concepts in industry. The obtained results show that the method is capable of handling optimization problems with multiple constraints, including challenging acceleration responses, and yields significantly better solutions than the state-of-the-art methods. Finally, the robustness of the obtained designs is studied, demonstrating the ability of EA-LSM to find designs of low sensitivity w.r.t. small variations of the loading conditions, which is crucial from the perspective of industrial applications of the proposed method.