Machine learning-assisted in-situ adaptive strategies for the control of defects and anomalies in metal additive manufacturing

作者：Dayalan Gunasegaram, Amanda S. Barnard, Manyalibo J. Matthews, Bradley Howell Jared, Amber Andreaco, Katharina Bartsch, Anthony B. Murphy · 发表于：Additive manufacturing · 年份：2024 · DOI：10.1016/j.addma.2024.104013 · 被引用次数：101 · 研究领域：Additive Manufacturing Materials and Processes、Welding Techniques and Residual Stresses、Industrial Vision Systems and Defect Detection

In metal additive manufacturing (AM), the material microstructure and part geometry are formed incrementally. Consequently, the resulting part could be defect- and anomaly-free if sufficient care is taken to deposit each layer under optimal process conditions. Conventional closed-loop control (CLC) engineering solutions which sought to achieve this were deterministic and rule-based, thus resulting in limited success in the stochastic environment experienced in the highly dynamic AM process. On the other hand, emerging machine learning (ML) based strategies are better suited to providing the robustness, scope, flexibility, and scalability required for process control in an uncertain environment. Offline ML models that help optimise AM process parameters before a build begins and online ML models that efficiently processed in-situ sensory data to detect and diagnose flaws in real-time (or near-real-time) have been developed. However, ML models that enable a process to take evasive or corrective actions in relation to flaws via on the fly decision-making are only emerging. These models must possess prognostic capabilities to provide context-sensitive recommendations for in-situ process control based on real-time diagnostics. In this article, we pinpoint the shortcomings in traditional CLC strategies, and provide a framework for defect and anomaly control through ML-assisted CLC in AM. We discuss flaws in terms of their causes, in-situ detectability, and controllability, and exam...