Research progress and challenges of intelligent in situ monitoring for laser additive manufacturing processes based on machine learning

Laser Powder Bed Fusion (LPBF) and Laser Directed Energy Deposition (LDED) additive manufacturing technologies, as core techniques in high-end manufacturing, enable the production of complex parts and hold broad application prospects in aerospace, high-end equipment, and other fields. However, issues such as process parameter fluctuations and material property variations leading to defects have become stumbling blocks hindering the advancement of additive manufacturing toward mass production. In recent years, the rapid advancement of machine learning has shifted the research focus of LDED and LPBF from “process parameter optimization” to “AI-driven online quality control.” Integrating advanced sensing technologies has also become particularly crucial. This review outlines the progress and current status of intelligent monitoring research in LPBF and LDED additive manufacturing across five dimensions: process information sensing, internal/external defect assessment based on in-situ and ex-situ measurements, performance quality evaluation, process parameter optimization, and quality control. It highlights the challenges both technologies face in achieving mass production and outlines prospects.