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Quality analysis in metal additive manufacturing with deep learning

Author

Listed:
  • Xiang Li

    (University of Cincinnati
    Northeastern University)

  • Xiaodong Jia

    (University of Cincinnati)

  • Qibo Yang

    (University of Cincinnati)

  • Jay Lee

    (University of Cincinnati)

Abstract

As a promising modern technology, additive manufacturing (AM) has been receiving increasing research and industrial attention in the recent years. With its rapid development, the importance of quality monitoring in AM process has been recognized, which significantly affects the property of the manufactured parts. Since the conventional hand-crafted features for quality identification are generally costly, time-consuming and sensitive to noises, the intelligent data-driven automatic process monitoring methods are becoming more and more popular at present. This paper proposes a deep learning-based quality identification method for metal AM process. To alleviate the requirement for large amounts of high-quality labeled training data by most existing data-driven methods, an identification consistency-based approach is proposed to better explore the semi-supervised training data. The proposed method is able to achieve promising performance using limited supervised samples with low quality, such as noisy and blurred images. Experiments on a real-world metal AM dataset are implemented to validate the effectiveness of the proposed method, which offers a promising tool for real industrial applications.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:8:d:10.1007_s10845-020-01549-2
    DOI: 10.1007/s10845-020-01549-2
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    References listed on IDEAS

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    1. Li, Xiang & Zhang, Wei & Ding, Qian, 2019. "Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 208-218.
    2. Yang Hu & Piero Baraldi & Francesco Di Maio & Enrico Zio, 2017. "A Systematic Semi-Supervised Self-adaptable Fault Diagnostics approach in an evolving environment," Post-Print hal-01652242, HAL.
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    Cited by:

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    3. Ying Zhang & Mutahar Safdar & Jiarui Xie & Jinghao Li & Manuel Sage & Yaoyao Fiona Zhao, 2023. "A systematic review on data of additive manufacturing for machine learning applications: the data quality, type, preprocessing, and management," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3305-3340, December.
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    5. Bianca Maria Colosimo & Luca Pagani & Marco Grasso, 2024. "Modeling spatial point processes in video-imaging via Ripley’s K-function: an application to spatter analysis in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 429-447, January.
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    9. Sinan Uguz & Osman Ipek, 2022. "Prediction of the parameters affecting the performance of compact heat exchangers with an innovative design using machine learning techniques," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1393-1417, June.
    10. Mohamed Ismail & Noha A. Mostafa & Ahmed El-assal, 2022. "Quality monitoring in multistage manufacturing systems by using machine learning techniques," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2471-2486, December.
    11. Jinping Liu & Jie Wang & Xianfeng Liu & Tianyu Ma & Zhaohui Tang, 2022. "MWRSPCA: online fault monitoring based on moving window recursive sparse principal component analysis," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1255-1271, June.
    12. Yunhan Kim & Taekyum Kim & Byeng D. Youn & Sung-Hoon Ahn, 2022. "Machining quality monitoring (MQM) in laser-assisted micro-milling of glass using cutting force signals: an image-based deep transfer learning," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1813-1828, August.
    13. Chun Fai Lui & Ahmed Maged & Min Xie, 2024. "A novel image feature based self-supervised learning model for effective quality inspection in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3543-3558, October.
    14. 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.
    15. Michael D. T. McDonnell & Daniel Arnaldo & Etienne Pelletier & James A. Grant-Jacob & Matthew Praeger & Dimitris Karnakis & Robert W. Eason & Ben Mills, 2021. "Machine learning for multi-dimensional optimisation and predictive visualisation of laser machining," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1471-1483, June.
    16. Salomé Sanchez & Divish Rengasamy & Christopher J. Hyde & Grazziela P. Figueredo & Benjamin Rothwell, 2021. "Machine learning to determine the main factors affecting creep rates in laser powder bed fusion," Journal of Intelligent Manufacturing, Springer, vol. 32(8), pages 2353-2373, December.
    17. Hasan Tercan & Philipp Deibert & Tobias Meisen, 2022. "Continual learning of neural networks for quality prediction in production using memory aware synapses and weight transfer," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 283-292, January.
    18. Minyoung Lee & Joohyoung Jeon & Hongchul Lee, 2022. "Explainable AI for domain experts: a post Hoc analysis of deep learning for defect classification of TFT–LCD panels," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1747-1759, August.
    19. 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.

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