Defect Detection and Optimization for Additive Manufacturing Powered by Artificial Intelligence: A Review

Additive Manufacturing (AM) has rapidly advanced in recent years, with various production methods being adopted across different industrial sectors. AM offers several advantages over traditional machining, including reduced material waste and the ability to create geometrically complex components. However, it also introduces significant variability in material properties and process conditions, which poses challenges that have hindered its full integration into industry. As a result, despite considerable research progress, the practical implementation of AM technologies has not yet fulfilled expectations. Critical barriers to the reliable industrialization of AM include defect detection and process optimization. To tackle these issues, researchers have explored real-time monitoring and predictive control methods using Artificial Intelligence (AI). AI-driven techniques show promise for improving defect identification and optimizing process parameters under dynamic conditions, ultimately enhancing part quality and manufacturing consistency. This review offers a comprehensive overview of recent advancements in AI-assisted defect detection and process optimization in AM, while also highlighting key challenges and research directions needed to effectively integrate AI with AM technologies.