Metal Additive Manufacturing and Molten Pool Dynamic Characterization Monitoring: Advances in Machine Learning for Directed Energy Deposition - Wentao He; Lida Zhu; Can Liu; Hongxiao Jiang (Metals)
Directed energy deposition (DED) has progressively emerged as a highly promising technology for the rapid, cost-effective, and high-performance fabrication of hard-to-process metal components with shorter production cycles. Recognized as one of the most widely utilized metal additive manufacturing (AM) techniques, DED has found extensive applications in critical industrial sectors such as aerospace and aviation. Despite its potential, challenges such as inconsistent part quality and low process repeatability continue to restrict its broader adoption. The core issue underlying these challenges is the complex, dynamic nature of the DED process, which involves the coupling of multiple physical fields. Within this context, the molten pool plays a pivotal role, serving as a key carrier that encapsulates abundant process characteristic information. The dynamic characteristics of the molten pool are intrinsically linked to the final part quality and the repeatability of the process. Consequently, integrating machine learning (ML) methodologies into the monitoring framework can offer robust data-driven support for enhancing both product quality and process consistency. This paper provides a comprehensive review of the research advancements and prospective trends in the dynamic monitoring and control of molten pool characteristics within DED processes underpinned by machine learning techniques. The review is structured around five key areas: an overview and fundamental principles of DED technology, methods for process information sensing during part monitoring, approaches for dynamically monitoring molten pool characteristics, the primary challenges currently faced in intelligent monitoring systems, and the potential future directions for further research and development. Through this detailed examination, the paper aims to shed light on the pivotal role of intelligent monitoring systems in advancing DED technology, ultimately contributing to more reliable and repeatable additive manufacturing processes.