Adaptable multi-objective optimization framework: application to metal additive manufacturing

Abstract

The aim of this work is to introduce an adaptable framework for Multi-Objective Optimization (MOO) in Metal Additive Manufacturing (AM). The framework accommodates diverse design variables and objectives, enabling iterative updates via Bayesian optimization for continuous improvement. It employs space-filling design and Gaussian Process regression for high-fidelity surrogate models. A Sensitivity Analysis (SA) measures the input contributions. Multi-Objective Optimization (MOO) was performed using an evolutionary algorithm. Using literature data, the framework optimizes the surface roughness (SR) and porosity of the AM part by controlling the laser parameters. The GP model achieves cross-validation with an R² of 0.79, and with low relative mean errors. SA highlights the dominance of hatch distance in SR prediction and the balanced influence of laser speed and power on the porosity. This framework promises significant potential for the enhancement of AM technology.