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Machine learning and deep learning based predictive quality in manufacturing: a systematic review

Author

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  • Hasan Tercan

    (University of Wuppertal)

  • Tobias Meisen

    (University of Wuppertal)

Abstract

With the ongoing digitization of the manufacturing industry and the ability to bring together data from manufacturing processes and quality measurements, there is enormous potential to use machine learning and deep learning techniques for quality assurance. In this context, predictive quality enables manufacturing companies to make data-driven estimations about the product quality based on process data. In the current state of research, numerous approaches to predictive quality exist in a wide variety of use cases and domains. Their applications range from quality predictions during production using sensor data to automated quality inspection in the field based on measurement data. However, there is currently a lack of an overall view of where predictive quality research stands as a whole, what approaches are currently being investigated, and what challenges currently exist. This paper addresses these issues by conducting a comprehensive and systematic review of scientific publications between 2012 and 2021 dealing with predictive quality in manufacturing. The publications are categorized according to the manufacturing processes they address as well as the data bases and machine learning models they use. In this process, key insights into the scope of this field are collected along with gaps and similarities in the solution approaches. Finally, open challenges for predictive quality are derived from the results and an outlook on future research directions to solve them is provided.

Suggested Citation

  • Hasan Tercan & Tobias Meisen, 2022. "Machine learning and deep learning based predictive quality in manufacturing: a systematic review," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 1879-1905, October.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:7:d:10.1007_s10845-022-01963-8
    DOI: 10.1007/s10845-022-01963-8
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    References listed on IDEAS

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    Cited by:

    1. Zhen Zhang & Zenan Yang & Chenchong Wang & Wei Xu, 2024. "Accelerating ultrashort pulse laser micromachining process comprehensive optimization using a machine learning cycle design strategy integrated with a physical model," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 449-465, January.
    2. 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.
    3. Shugui Wang & Yunxian Cui & Yuxin Song & Chenggang Ding & Wanyu Ding & Junwei Yin, 2024. "A novel surface temperature sensor and random forest-based welding quality prediction model," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3291-3314, October.
    4. Thomas Heitz & Ning He & Addi Ait-Mlouk & Daniel Bachrathy & Ni Chen & Guolong Zhao & Liang Li, 2025. "Investigation on eXtreme Gradient Boosting for cutting force prediction in milling," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 285-301, January.
    5. Zhe Li & Kexin Liu & Xudong Wang & Xiaofang Yuan & He Xie & Yaonan Wang, 2025. "A signal-to-image fault classification method based on multi-sensor data for robotic grinding monitoring," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 537-550, January.
    6. Sergey Butsykin & Anton Gordynets & Alexey Kiselev & Mikhail Slobodyan, 2023. "Evaluation of the reliability of resistance spot welding control via on-line monitoring of dynamic resistance," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 3109-3129, October.
    7. Indrawan Nugrahanto & Hariyanto Gunawan & Hsing-Yu Chen, 2024. "Innovative Approaches to Sustainable Computer Numeric Control Machining: A Machine Learning Perspective on Energy Efficiency," Sustainability, MDPI, vol. 16(9), pages 1-22, April.

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