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Inspecting Method for Defective Casting Products with Convolutional Neural Network (CNN)

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International Journal of Precision Engineering and Manufacturing-Green Technology Aims and scope Submit manuscript

Abstract

It is essential to conduct the quality control for gauranteeing sound products after finishing conventional manufacturing processes. Vision-based inpection system has been extensively applied to various industries linked with concept of the smart factory since it does not only enhance the inspecting accuracy, but also decrease the cost for the human inspection, substantially. This paper mainly concerns the development of the inspecting system for the casting products with supported by the convolutional neural network, which makes it possible to detect various types of defects such as blow hole, chipping, crack, and wash automatically. To obtain high accuracy in inspecting system, it does not only require sub-partitioning of the original images, but also apply multiple labeling according to the order of the sub-images and the existence of the defects. Performance of the proposed inspecting algorithm has been validated with the 400 casting products, in which it exhibits substantially high accuracy more than 98%, experimentally.

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT) (No. 2019R1A2C4070160). This work was also supported by the “Human Resource Program in Energy Technology” of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), granted by the Ministry of Trade, Industry & Energy (No. 20174010201310).

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Authors and Affiliations

  1. Department of Mechanical Design Engineering, Hanyang University, 222, Wangsimni-ro, Seongdonggu, Seoul, 04763, Republic of Korea

    Thong Phi Nguyen & Seungho Choi

  2. Department of Mechanical Engineering, Korea National University of Transportation, 50 Daehak-ro, Chungju, Chungcheongbuk-do, 27469, Republic of Korea

    Sung-Jun Park

  3. School of Materials Science and Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu, 41566, Republic of Korea

    Sung Hyuk Park

  4. Department of Mechanical Engineering, Hanyang University, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan-si, Gyeonggi-do, 15588, Republic of Korea

    Jonghun Yoon

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  1. Thong Phi Nguyen
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Correspondence to Jonghun Yoon.

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Nguyen, T.P., Choi, S., Park, SJ. et al. Inspecting Method for Defective Casting Products with Convolutional Neural Network (CNN). Int. J. of Precis. Eng. and Manuf.-Green Tech. 8, 583–594 (2021). https://doi.org/10.1007/s40684-020-00197-4

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  • DOI: https://doi.org/10.1007/s40684-020-00197-4

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