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A systematic literature review on recent trends of machine learning applications in additive manufacturing

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  • Md Doulotuzzaman Xames

    (Military Institute of Science and Technology)

  • Fariha Kabir Torsha

    (University of Houston)

  • Ferdous Sarwar

    (Bangladesh University of Engineering and Technology)

Abstract

Additive manufacturing (AM) offers the advantage of producing complex parts more efficiently and in a lesser production cycle time as compared to conventional subtractive manufacturing processes. It also provides higher flexibility for diverse applications by facilitating the use of a variety of materials and different processing technologies. With the exceptional growth of computing capability, researchers are extensively using machine learning (ML) techniques to control the performance of every phase of AM processes, such as design, process parameters modeling, process monitoring and control, quality inspection, and validation. Also, ML methods have made it possible to develop cybermanufacturing for AM systems and thus revolutionized Industry 4.0. This paper presents the state-of-the-art applications of ML in solving numerous problems related to AM processes. We give an overview of the research trends in this domain through a systematic literature review of relevant journal articles and conference papers. We summarize recent development and existing challenges to point out the direction of future research scope. This paper can provide AM researchers and practitioners with the latest information consequential for further development.

Suggested Citation

  • Md Doulotuzzaman Xames & Fariha Kabir Torsha & Ferdous Sarwar, 2023. "A systematic literature review on recent trends of machine learning applications in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2529-2555, August.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:6:d:10.1007_s10845-022-01957-6
    DOI: 10.1007/s10845-022-01957-6
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    References listed on IDEAS

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    1. Ohyung Kwon & Hyung Giun Kim & Min Ji Ham & Wonrae Kim & Gun-Hee Kim & Jae-Hyung Cho & Nam Il Kim & Kangil Kim, 2020. "A deep neural network for classification of melt-pool images in metal additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 375-386, February.
    2. William Mycroft & Mordechai Katzman & Samuel Tammas-Williams & Everth Hernandez-Nava & George Panoutsos & Iain Todd & Visakan Kadirkamanathan, 2020. "A data-driven approach for predicting printability in metal additive manufacturing processes," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1769-1781, October.
    3. Carlos Gonzalez-Val & Adrian Pallas & Veronica Panadeiro & Alvaro Rodriguez, 2020. "A convolutional approach to quality monitoring for laser manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 789-795, March.
    4. Sheng Yang & Thomas Page & Ying Zhang & Yaoyao Fiona Zhao, 2020. "Towards an automated decision support system for the identification of additive manufacturing part candidates," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1917-1933, December.
    5. Yong Fang & Jian Gao & Cheng Huang & Hua Peng & Runpu Wu, 2019. "Self Multi-Head Attention-based Convolutional Neural Networks for fake news detection," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-13, September.
    6. Vivek Mahato & Muhannad Ahmed Obeidi & Dermot Brabazon & Pádraig Cunningham, 2022. "Detecting voids in 3D printing using melt pool time series data," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 845-852, March.
    7. Mingtao Wu & Zhengyi Song & Young B. Moon, 2019. "Detecting cyber-physical attacks in CyberManufacturing systems with machine learning methods," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1111-1123, March.
    8. Aniruddha Gaikwad & Reza Yavari & Mohammad Montazeri & Kevin Cole & Linkan Bian & Prahalada Rao, 2020. "Toward the digital twin of additive manufacturing: Integrating thermal simulations, sensing, and analytics to detect process faults," IISE Transactions, Taylor & Francis Journals, vol. 52(11), pages 1204-1217, November.
    9. Xiang Li & Xiaodong Jia & Qibo Yang & Jay Lee, 2020. "Quality analysis in metal additive manufacturing with deep learning," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 2003-2017, December.
    10. Omid Davtalab & Ali Kazemian & Xiao Yuan & Behrokh Khoshnevis, 2022. "Automated inspection in robotic additive manufacturing using deep learning for layer deformation detection," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 771-784, March.
    11. Mohammad Montazeri & Abdalla R. Nassar & Alexander J. Dunbar & Prahalada Rao, 2020. "In-process monitoring of porosity in additive manufacturing using optical emission spectroscopy," IISE Transactions, Taylor & Francis Journals, vol. 52(5), pages 500-515, May.
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    Cited by:

    1. Christian Neunzig & Dennis Möllensiep & Bernd Kuhlenkötter & Matthias Möller, 2024. "ML Pro: digital assistance system for interactive machine learning in production," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3479-3499, October.

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