Xiao, S.; Li, J.; Wang, Z.; Chen, Y.; Tofighi, S. Advancing Additive Manufacturing Through Machine Learning Techniques: A State-of-the-Art Review. Preprints2024, 2024092411. https://doi.org/10.20944/preprints202409.2411.v1
APA Style
Xiao, S., Li, J., Wang, Z., Chen, Y., & Tofighi, S. (2024). Advancing Additive Manufacturing Through Machine Learning Techniques: A State-of-the-Art Review. Preprints. https://doi.org/10.20944/preprints202409.2411.v1
Chicago/Turabian Style
Xiao, S., Yingbin Chen and Soheyla Tofighi. 2024 "Advancing Additive Manufacturing Through Machine Learning Techniques: A State-of-the-Art Review" Preprints. https://doi.org/10.20944/preprints202409.2411.v1
Abstract
In the fourth industrial revolution, artificial intelligence (AI) and machine learning (ML) have increasingly been applied to manufacturing, particularly additive manufacturing (AM), to enhance processes and production. This study provides a comprehensive review of the state-of-the-art achievements in this domain, highlighting not only the widely discussed supervised learning but also the emerging applications of semi-supervised learning and reinforcement learning (RL). These advanced ML techniques have recently garnered significant attention due to their potential to further optimize and automate AM processes. The review aims to offer insights into various ML technologies employed in current research projects and to promote the diverse applications of ML in AM. By exploring the latest advancements and trends, this study seeks to foster a deeper understanding of ML’s transformative role in AM, paving the way for future innovations and improvements in manufacturing practices.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
