Finite element analysis-enabled optimization of process parameters in additive manufacturing

A design optimization framework is proposed for process parameters in additive manufacturing. A finite element approximation of the coupled thermomechanical model is used to simulate the fused deposition of heated material and compute the objective function for each analysis. Both gradient-based and gradient-free optimization methods are developed. The gradient- based approach, which results in a balance law-constrained optimization problem, requires sensitivities computed from the fully discretized finite element model. These sensitivities are derived and subsequently applied to a projected gradient-descent algorithm. For the gradient- free approach, two distinct algorithms are proposed: a search algorithm based on local variations and a Bayesian optimization algorithm using a Gaussian process. Two design optimization examples are considered in order to illustrate the effectiveness of these approaches and explore the range of their usefulness.