Laser Powder Bed Fusion of Defect-Free Aa2024 Using Bayesian Machine Learning

Abstract

Identifying optimal processing parameters remains a major challenge in additive manufacturing (AM), limiting its potential and broader industrial adoption. In this work, we present a Bayesian machine learning (ML) framework designed to efficiently determine optimal parameters for AM processes. We demonstrate its effectiveness through the successful processing of the AA2024 alloy into high-density components, known for its difficulty in processing, using laser powder bed fusion (PBF-LB/M). Our approach begins with Bayesian Optimization (BO) applied to an initial dataset containing only five processing parameter sets. Despite the limited data, the method accurately predicts conditions for producing crack-free components with a remarkably high density. We further extend the framework to perform bi-objective optimization, targeting both maximum build-up rate (BUR) and density. Experimental validation confirms that the framework can identify new parameter sets that significantly enhance BUR while maintaining high part quality. This work underscores the potential of BO strategies for accelerating optimal processing conditions discovery, especially for challenging materials and multi-objective scenarios.

Keywords: additive manufacturing, laser powder bed fusion, Bayesian optimization, Machine Learning, AA2024