AI-Driven Metal Additive Manufacturing: A Critical Review of Techniques, Challenges, and Emerging Opportunities
DOI:
https://doi.org/10.37255/jme.v21i2pp064-076Keywords:
Metal AM; AI-driven AM; PSP intelligence; Defect analytics; Physics-informed ML; Digital twinsAbstract
Metal additive manufacturing (MAM) technology offers significant opportunities for the production of complex metal components, including greater design freedom and improved material utilization. However, issues such as process instability, defect formation, complex microstructure, and poor repeatability pose significant challenges to the realization of industrial applications of the MAM technology. In recent years, the implementation of AI and ML methods has been considered as one of the possible ways to address these problems. The goal of this paper is to provide a comprehensive overview of recent developments in metal additive manufacturing technology enabled by artificial intelligence methods, including process parameter optimization, defect detection, microstructure prediction, and material qualification. Developments in deep learning, Bayesian optimization, physics-guided machine learning, and related areas will be considered in detail, along with their potential contributions to advancing process-structure-property relationships. Also, the emergence of new trends in autonomous manufacturing systems, generative design, digital twins, and others will be discussed to demonstrate the transition from conventional trial-and-error approaches to AI-driven, autonomous manufacturing systems. Moreover, the issues with current methodologies will be addressed, including the need for reliable, explainable, and robust AI techniques for implementing MAM technology. Additionally, directions for addressing issues with the MAM technology will be provided.
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