Machine learning-assisted process-structure-property correlation in laser metal additive manufacturing: a critical review

Abstract Artificial intelligence provides novel perspectives for laser metal additive manufacturing (LMAM), enhancing precision, efficiency, and structural and process optimization. Machine learning-assisted process–structure–property correlation in additive manufacturing (ML-PSP-AM) presents an effective pathway for structural innovation and performance optimization, leveraging automation and intelligence to address the growing processing demands across industries. This review differs from the existing literature by presenting a multi-scale, PSP-centered analysis of ML applications in LMAM, integrating discussions that span from processing-driven macro-scale formation to meso/micro-scale defect prediction and microstructure–property relationships. By evaluating state-of-the-art ML applications across various AM stages, we identify current limitations, propose targeted strategies, and outline opportunities to improve accuracy, minimize defects, and enhance mechanical properties such as strength and fatigue life. The advancement of ML-assisted AM should focus on breakthroughs from “0 to 1” in application and innovations from “1 to ∞” in algorithms. The realization of ML-PSP-AM represents a transformative yet disruptive integration of manufacturing engineering, artificial intelligence, and materials science, driving significant progress in modern manufacturing technologies.