Progress and Opportunities for Machine Learning in Materials and Processes of Additive Manufacturing

Abstract In recent years, there has been a widespread adoption of machine learning (ML) technologies to unravel intricate relationships among diverse parameters in various additive manufacturing (AM) techniques. These ML models excel at recognizing complex patterns from extensive, well‐curated datasets, thereby unveiling latent knowledge crucial for informed decision‐making during the AM process. The collaborative synergy between ML and AM holds the potential to revolutionize the design and production of AM‐printed parts. By leveraging the copious data generated in AM processes, ML algorithms can significantly enhance design optimization. This is achieved by employing forward problem analysis in tandem with iterative optimization techniques or generative artificial intelligence tools. The approach involves reverse‐engineering from desired outcomes to yield valuable insights, ultimately streamlining the AM design process. This review paper delves into the challenges and opportunities emerging at the intersection of these two dynamic fields. It provides a comprehensive analysis of the publication landscape for ML‐related research in the field of AM, explores common ML applications in AM research (such as quality control, process optimization, design optimization, microstructure analysis, and material formulation) and concludes by presenting an outlook that underscores the utilization of advanced ML models, the development of emerging sensors, and ML applications in emerging AM‐related fields. Notably, ML has garnered increased attention in AM due to its superior performance across various AM‐related applications. We envision that the integration of ML into AM processes will significantly enhance 3D printing capabilities across diverse AM‐related research areas. This article is protected by copyright. All rights reserved