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Towards real-time in-situ monitoring of hot-spot defects in L-PBF: a new classification-based method for fast video-imaging data analysis

Author

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  • Matteo Bugatti

    (Politecnico di Milano)

  • Bianca Maria Colosimo

    (Politecnico di Milano)

Abstract

The increasing interest towards additive manufacturing (AM) is pushing the industry to provide new solutions to improve process stability. Monitoring is a key tool for this purpose but the typical AM fast process dynamics and the high data flow required to accurately describe the process are pushing the limits of standard statistical process monitoring (SPM) techniques. The adoption of novel smart data extraction and analysis methods are fundamental to monitor the process with the required accuracy while keeping the computational effort to a reasonable level for real-time application. In this work, a new framework for the detection of defects in metal additive manufacturing processes via in-situ high-speed cameras is presented: a new data extraction method is developed to efficiently extract only the relevant information from the regions of interest identified in the high-speed imaging data stream and to reduce the dimensionality of the anomaly detection task performed by three competitor machine learning classification methods. The defect detection performance and computational speed of this approach is carefully evaluated through computer simulations and experimental studies, and directly compared with the performance and computational speed of other existing methods applied on the same reference dataset. The results show that the proposed method is capable of quickly detecting the occurrence of defects while keeping the high computational speed that would be required to implement this new process monitoring approach for real-time defect detection.

Suggested Citation

  • Matteo Bugatti & Bianca Maria Colosimo, 2022. "Towards real-time in-situ monitoring of hot-spot defects in L-PBF: a new classification-based method for fast video-imaging data analysis," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 293-309, January.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:1:d:10.1007_s10845-021-01787-y
    DOI: 10.1007/s10845-021-01787-y
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    References listed on IDEAS

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    1. Yun Bai & Zhenzhong Sun & Bo Zeng & Jianyu Long & Lin Li & José Valente Oliveira & Chuan Li, 2019. "A comparison of dimension reduction techniques for support vector machine modeling of multi-parameter manufacturing quality prediction," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2245-2256, June.
    2. Zhen He & Ling Zuo & Min Zhang & Fadel M. Megahed, 2016. "An image-based multivariate generalized likelihood ratio control chart for detecting and diagnosing multiple faults in manufactured products," International Journal of Production Research, Taylor & Francis Journals, vol. 54(6), pages 1771-1784, March.
    3. 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.
    4. 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.
    5. Mahesh Mani & Brandon M. Lane & M. Alkan Donmez & Shaw C. Feng & Shawn P. Moylan, 2017. "A review on measurement science needs for real-time control of additive manufacturing metal powder bed fusion processes," International Journal of Production Research, Taylor & Francis Journals, vol. 55(5), pages 1400-1418, March.
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    Cited by:

    1. 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.

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