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Advancing Additive Manufacturing Through Machine Learning Techniques: A State-of-the-Art Review

Author

Listed:
  • Shaoping Xiao

    (Department of Mechanical Engineering, Iowa Technology Institute, The University of Iowa, 3131 Seamans Center, Iowa City, IA 52242, USA)

  • Junchao Li

    (Department of Mechanical Engineering, Iowa Technology Institute, The University of Iowa, 3131 Seamans Center, Iowa City, IA 52242, USA)

  • Zhaoan Wang

    (Department of Mechanical Engineering, Iowa Technology Institute, The University of Iowa, 3131 Seamans Center, Iowa City, IA 52242, USA)

  • Yingbin Chen

    (Department of Mechanical Engineering, Iowa Technology Institute, The University of Iowa, 3131 Seamans Center, Iowa City, IA 52242, USA)

  • Soheyla Tofighi

    (Department of Mechanical Engineering, Iowa Technology Institute, The University of Iowa, 3131 Seamans Center, Iowa City, IA 52242, USA)

Abstract

In the fourth industrial revolution, artificial intelligence 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. These advanced ML techniques have recently gained significant attention for 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.

Suggested Citation

  • Shaoping Xiao & Junchao Li & Zhaoan Wang & Yingbin Chen & Soheyla Tofighi, 2024. "Advancing Additive Manufacturing Through Machine Learning Techniques: A State-of-the-Art Review," Future Internet, MDPI, vol. 16(11), pages 1-30, November.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:11:p:419-:d:1519628
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    References listed on IDEAS

    as
    1. Gopala K. Anumanchipalli & Josh Chartier & Edward F. Chang, 2019. "Speech synthesis from neural decoding of spoken sentences," Nature, Nature, vol. 568(7753), pages 493-498, April.
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