Impact of Initialization Strategies on Multi-Objective Bayesian Optimization in Discrete PBF-LB/M Process Development: A Case Study on AZ31 Magnesium Alloy

Multi-objective Bayesian Optimization (MOBO) has become a promising strategy for accelerating process development in Laser Powder Bed Fusion of Metals (PBF-LB/M), where experimental evaluations are costly, and design spaces are high-dimensional. This study investigates how different initialization strategies affect MOBO performance in a discrete, machine-limited parameter space during the fabrication of AZ31 magnesium alloy. Three approaches to constructing the initial experimental dataset—Latin Hypercube Sampling (V1), balanced-marginal selection (V2), and prior fractional-factorial sampling (V3)—were compared using two state-of-the-art MOBO algorithms, DGEMO and TSEMO, implemented within the AutoOED platform. A total of 180 samples were produced and evaluated with respect to two conflicting objectives: maximizing relative density and build rate. The evolution of the Pareto front and hypervolume metrics shows that the structure of the initial dataset strongly governs subsequent optimization efficiency. Variant V3 yielded the highest hypervolumes for both algorithms, whereas Variant V2 produced the most uniform Pareto approximation, highlighting a trade-off between global coverage and structural distribution. TSEMO demonstrated faster early convergence, whereas DGEMO maintained broader exploration of the design space. Analysis of duplicate experimental points revealed that discretization and batch selection can considerably limit the effective search diversity, contributing to an early saturation of hypervolume gains. The results indicate that, in constrained PBF-LB/M design spaces, MOBO primarily serves to validate and refine a well-designed initial dataset rather than to discover dramatically new optima. The presented workflow highlights how initialization, parameter discretization, and sampling diversity shape the practical efficiency of MOBO for additive manufacturing process optimization.