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Online quality inspection using Bayesian classification in powder-bed additive manufacturing from high-resolution visual camera images

Author

Listed:
  • Masoumeh Aminzadeh

    (Georgia Institute of Technology)

  • Thomas R. Kurfess

    (Georgia Institute of Technology)

Abstract

Despite their advances and numerous benefits, metal powder-bed additive manufacturing (AM) processes still suffer from the high chances of defect formation and a need for improved quality. This work develops an online monitoring system for quality of fusion and defect formation in every layer of the laser powder-bed fusion process using computer vision and Bayesian inference. An imaging setup is developed that for the first time allows capturing in-situ (during the build) images from every layer that visualize detailed layer defects and porosity. A database of camera images from every layer of AM parts made with various part quality was created that is the first visual labeled dataset from in-situ visual images of the powder-bed AM (also visualizing detailed layer features). The dataset is used in training-based classification to detect layers or sub-regions of the layer with low quality of fusion or defects. Features are carefully selected based on physical intuition into the process and extracted from the images of the various types of builds. A Bayesian classifier is developed and trained to classify the quality of the build that signifies the defective and unacceptable build layers or regions. The results can be used for quasi-real-time (layer-wise) process control, further process decisions, or corrective actions.

Suggested Citation

  • Masoumeh Aminzadeh & Thomas R. Kurfess, 2019. "Online quality inspection using Bayesian classification in powder-bed additive manufacturing from high-resolution visual camera images," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2505-2523, August.
    • Handle: RePEc:spr:joinma:v:30:y:2019:i:6:d:10.1007_s10845-018-1412-0
    DOI: 10.1007/s10845-018-1412-0
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    Citations

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    Cited by:

    1. 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.
    2. Jingchang Li & Qi Zhou & Xufeng Huang & Menglei Li & Longchao Cao, 2023. "In situ quality inspection with layer-wise visual images based on deep transfer learning during selective laser melting," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 853-867, February.
    3. Paromita Nath & Sankaran Mahadevan, 2023. "Probabilistic predictive control of porosity in laser powder bed fusion," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1085-1103, March.
    4. Yong Ren & Qian Wang, 2022. "Gaussian-process based modeling and optimal control of melt-pool geometry in laser powder bed fusion," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2239-2256, December.
    5. 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.
    6. Jingchang Li & Longchao Cao & Jiexiang Hu & Minhua Sheng & Qi Zhou & Peng Jin, 2022. "A prediction approach of SLM based on the ensemble of metamodels considering material efficiency, energy consumption, and tensile strength," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 687-702, March.
    7. Chunyang Xia & Zengxi Pan & Joseph Polden & Huijun Li & Yanling Xu & Shanben Chen, 2022. "Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1467-1482, June.
    8. Yilin Guo & Wen Feng Lu & Jerry Ying Hsi Fuh, 2021. "Semi-supervised deep learning based framework for assessing manufacturability of cellular structures in direct metal laser sintering process," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 347-359, February.
    9. Tamie Takeda Yokoyama & Satie Ledoux Takeda-Berger & Marco Aurélio Oliveira & Andre Hideto Futami & Luiz Veriano Oliveira Dalla Valentina & Enzo Morosini Frazzon, 2023. "Bayesian networks as a guide to value stream mapping for lean office implementation: a proposed framework," Operations Management Research, Springer, vol. 16(1), pages 49-79, March.
    10. Osama Aljarrah & Jun Li & Alfa Heryudono & Wenzhen Huang & Jing Bi, 2023. "Predicting part distortion field in additive manufacturing: a data-driven framework," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1975-1993, April.

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