Affiliation:
1. Singapore Centre for 3D Printing Nanyang Technological University 50 Nanyang Avenue Singapore 639798 Singapore
2. School of Mechanical and Aerospace Engineering Nanyang Technological University 50 Nanyang Ave Singapore 639798 Singapore
3. Singapore Institute of Manufacturing Technology (SIMTech) Agency for Science Technology and Research (A*STAR) 5 CleanTech Loop #01‐01 Singapore 636732 Singapore
Abstract
AbstractIn recent years, there has been 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. This review 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. It is envisioned that the integration of ML into AM processes will significantly enhance 3D printing capabilities across diverse AM‐related research areas.
Funder
National Research Foundation
Agency for Science, Technology and Research
Cited by
20 articles.
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