Machine learning in laser powder bed fusion: a data-centric review of structural prediction and process optimization

Abstract

Significant advances have been made in the field of Additive Manufacturing (AM) across various manufacturing processes. Laser Powder Bed Fusion (LPBF) is one of the most widely adopted metal AM processes in the industry, which employs energy sources such as lasers to melt powder materials. While the LPBF process offers numerous advantages, such as the ability to produce complex geometries and multiple parts simultaneously, it also presents challenges in the form of defects such as porosity, residual stresses, and cracks that need to be addressed. Conducting experiments to identify and eliminate these defects can be prohibitively expensive, motivating the development of alternative predictive approaches. Numerical modeling and simulation techniques provide a cost-effective alternative to experimental approaches, enabling analyses ranging from thermal histories and microstructural evolution to mechanical property prediction and defect identification in fabricated components. However, such simulations can also be computationally intensive. In recent years, Machine Learning (ML) and Artificial Intelligence (AI) algorithms have emerged as viable alternative tools to accelerate process understanding, defect prediction, and parameter optimization. This review provides a comprehensive overview of ML-based approaches applied to LPBF, emphasizing the importance of understanding the underlying physical phenomena that significantly influence data collection, preprocessing, and feature engineering strategies essential for effective model training and validation. It offers a structured framework for researchers seeking to leverage ML methods to enhance predictive accuracy and computational efficiency, particularly through simulation-driven data generation for model development.