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A deep neural network for classification of melt-pool images in metal additive manufacturing

Author

Listed:
  • Ohyung Kwon

    (Korea Institute of Industrial Technology)

  • Hyung Giun Kim

    (Korea Institute of Industrial Technology)

  • Min Ji Ham

    (Korea Institute of Industrial Technology)

  • Wonrae Kim

    (Korea Institute of Industrial Technology)

  • Gun-Hee Kim

    (Korea Institute of Industrial Technology)

  • Jae-Hyung Cho

    (WINFORSYS)

  • Nam Il Kim

    (WINFORSYS)

  • Kangil Kim

    (Konkuk University)

Abstract

By applying a deep neural network to selective laser melting, we studied a classification model of melt-pool images with respect to 6 laser power labels. Laser power influenced to form pores or cracks determining the part quality and was positively-linearly dependent to the density of the part. Using the neural network of which the number of nodes is dropped with increasing the layer number achieved satisfactory inference when melt-pool images had blurred edges. The proposed neural network showed the classification failure rate under 1.1% for 13,200 test images and was more effective to monitor melt-pool images because it simultaneously handled various shapes, comparing with a simple calculation such as the sum of pixel intensity in melt-pool images. The classification model could be utilized to infer the location to cause the unexpected alteration of microstructures or separate the defective products non-destructively.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:2:d:10.1007_s10845-018-1451-6
    DOI: 10.1007/s10845-018-1451-6
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    References listed on IDEAS

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    2. Kuo-Ming Tsai & Hao-Jhih Luo, 2017. "An inverse model for injection molding of optical lens using artificial neural network coupled with genetic algorithm," Journal of Intelligent Manufacturing, Springer, vol. 28(2), pages 473-487, February.
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    6. Doriana M. D’Addona & A. M. M. Sharif Ullah & D. Matarazzo, 2017. "Tool-wear prediction and pattern-recognition using artificial neural network and DNA-based computing," Journal of Intelligent Manufacturing, Springer, vol. 28(6), pages 1285-1301, August.
    7. Hamed Pashazadeh & Yousof Gheisari & Mohsen Hamedi, 2016. "Statistical modeling and optimization of resistance spot welding process parameters using neural networks and multi-objective genetic algorithm," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 549-559, June.
    8. Yicha Zhang & Alain Bernard & Ramy Harik & K. P. Karunakaran, 2017. "Build orientation optimization for multi-part production in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 28(6), pages 1393-1407, August.
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    Cited by:

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    4. Hong Seok Park & Dinh Son Nguyen & Thai Le-Hong & Xuan Tran, 2022. "Machine learning-based optimization of process parameters in selective laser melting for biomedical applications," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1843-1858, August.
    5. 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.
    6. Zhangyue Shi & Abdullah Al Mamun & Chen Kan & Wenmeng Tian & Chenang Liu, 2023. "An LSTM-autoencoder based online side channel monitoring approach for cyber-physical attack detection in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1815-1831, April.
    7. Angel-Iván García-Moreno, 2022. "A fast method for monitoring molten pool in infrared image streams using gravitational superpixels," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1779-1794, August.
    8. Jia Liu & Jiafeng Ye & Daniel Silva Izquierdo & Aleksandr Vinel & Nima Shamsaei & Shuai Shao, 2023. "A review of machine learning techniques for process and performance optimization in laser beam powder bed fusion additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3249-3275, December.
    9. 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.
    10. Zhenxing Cheng & Hu Wang & Gui-Rong Liu, 2021. "Deep convolutional neural network aided optimization for cold spray 3D simulation based on molecular dynamics," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1009-1023, April.
    11. Feiyang Li & Nian Cai & Xueliang Deng & Jiahao Li & Jianfa Lin & Han Wang, 2022. "Serial number inspection for ceramic membranes via an end-to-end photometric-induced convolutional neural network framework," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1373-1392, June.
    12. Hyunseop Park & Hyunwoong Ko & Yung-tsun Tina Lee & Shaw Feng & Paul Witherell & Hyunbo Cho, 2023. "Collaborative knowledge management to identify data analytics opportunities in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 541-564, February.
    13. 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.
    14. Runquan Xiao & Yanling Xu & Zhen Hou & Chao Chen & Shanben Chen, 2022. "An automatic calibration algorithm for laser vision sensor in robotic autonomous welding system," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1419-1432, June.
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