A review on in-situ monitoring of the temperature field in metal-based laser additive manufacturing

Abstract Metal additive manufacturing (MAM) presents unparalleled opportunities for fabricating complex and high-performance components. While achieving consistent part quality and process repeatability remains challenging. The temperature field is one of the dominant factors influencing the evolution of microstructure, distribution of residual stress, and mechanical properties during MAM. Therefore, it is significant to monitor and control the temperature field. In this review, the influences of the temperature field on the microstructure, residual stress, and mechanical performance are overviewed. The coupling mechanisms between thermal behavior and defect formation are explored. Secondly, a detailed review of the current state-of-the-art in-situ process monitoring techniques for the temperature field is provided. These techniques are evaluated for their capabilities and limitations in detecting defects. Thirdly, the application of machine learning (ML) algorithms in temperature monitoring and defect prediction based on thermal information during the MAM process is summarized. Finally, the advantages and current challenges—such as multiple sensors data fusion, physics-informed modeling, and ML models—are also discussed. This paper aims to provide a comprehensive overview of the precise and efficient monitoring of temperature field in MAM and equip researchers and industry professionals with a holistic understanding of the current capabilities, limitations, and future directions of in-situ process monitoring of temperature field during MAM.