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

• Published in: Finite elements in analysis and design Vol. 244; p. 104282
• Main Authors: Wang, Jingyi, Papadopoulos, Panayiotis
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2025
• Subjects: Additive manufacturing; Bayesian optimization; Finite element method; Gradient-descent optimization; Method of local variations; Sensitivity; Finite element method; Gradient-descent optimization; Sensitivity; Additive manufacturing; Bayesian optimization; Method of local variations
• ISSN: 0168-874X
• DOI: 10.1016/j.finel.2024.104282

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. •Fully-coupled thermomechanical modeling of additive manufacturing.•Optimization of process parameters enabled by finite element analysis.•Gradient-based and gradient-free approaches for optimization of process parameters.•Shape optimization for printed parts subject to cooling.•Simulation of two-dimensional printing involving curved domains.