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This review focuses on the use of machine learning to optimize material selection and processing parameters in laser-based metal additive manufacturing.

July 4, 2026Open Access

Machine learning-aided material selection, parameter optimization, and process monitoring in laser powder bed fusion and laser directed energy deposition with powder feed

Key Points

This review focuses on the use of machine learning to optimize material selection and processing parameters in laser-based metal additive manufacturing.
Comprehensive overview of machine learning applications in laser powder bed fusion and directed energy deposition.
Analysis of material selection, process parameter optimization, and in situ monitoring.
Discussion of ML models' characteristics, limitations, and data requirements for effective implementation.
Machine learning uncovers complex processing-structure-property relationships amid high-dimensional datasets.
Enhanced material selection strategies result in better tailored materials for additive manufacturing processes.
Improvements in monitoring techniques using ML lead to better quality assurance and process control in metal AM.

Abstract

Laser-based metal additive manufacturing (AM) technologies, such as laser powder bed fusion and laser-based directed energy deposition, have been broadly adopted in a wide range of industries, including the aerospace, automotive, biomedical, and energy sectors. These technologies require cross-scale coordination spanning six orders of magnitude, involving complex interactions between laser parameters, feedstock properties, and thermal-fluid dynamics. The resulting high-dimensional datasets span materials, processing parameters, laser-metal interaction, and final part properties, posing significant challenges to understanding intricate processing-structure-property (P-S-P) relationships. Recent advances in machine learning (ML), with its capacity to uncover hidden patterns and correlations from large, high-dimensional datasets, present a transformative approach to accelerate discovery and optimization in metal AM. In this review, we provide a comprehensive overview of recent advances and challenges in the application of various ML methods in two major laser-based metal AM processes: laser powder bed fusion for metal and laser directed energy deposition with powder feed. We examine the application of ML in three main aspects: (1) material selection, including AM-tailored materials development and metal powder selection; (2) optimization of processing parameters through the development of process maps and optimization strategies; and (3) in situ monitoring and quality assurance in AM processes using sensor-based systems, including acoustic emission, visible light, infrared, synchrotron X-ray, and multi-sensor approaches. Moreover, we examine the characteristics, limitations, and data requirements of various ML models in each of these domains, guiding model selection and experimental design. By adopting an ML-centric perspective, this review aims to inform future research directions, support the development of more autonomous metal AM systems, and accelerate the optimization of laser-based metal AM.

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