Additive manufacturing (AM), widely known as 3D printing, has transformed traditional manufacturing by enabling the production of complex geometries, reducing material waste, and supporting rapid, on-demand fabrication. However, conventional AM processes continue to face limitations such as defect formation, inconsistent part quality, and time-consuming design iterations. The integration of artificial intelligence (AI) into AM referred to as AI-enhanced additive manufacturing (AI-AM) offers a promising solution to these challenges. This review explores the convergence of AI and AM, focusing on how machine learning, computer vision, and generative design improve process reliability, adaptability, and efficiency. By synthesizing current research, it highlights how AI enables real-time monitoring, automated defect detection, and adaptive control of printing parameters, leading to reduced failure rates and higher-quality outputs. Furthermore, AI-driven generative design optimizes part geometry and material usage, allowing for the creation of intricate, performance-optimized components. Case studies from aerospace, automotive, and biomedical sectors demonstrate the tangible benefits and limitations of AI-AM systems. Findings indicate that AI integration enhances every stage of the AM workflow from design to fabrication paving the way for more intelligent, autonomous, and efficient production pipelines. As AI continues to evolve, its synergy with AM is poised to redefine the future of manufacturing, offering unprecedented capabilities in producing customized, functionally complex parts with superior precision and performance.
A Mon, study studied this question.