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Automated inspection in robotic additive manufacturing using deep learning for layer deformation detection

Author

Listed:
  • Omid Davtalab

    (University of Southern California
    Contour Crafting Corporation)

  • Ali Kazemian

    (Louisiana State University)

  • Xiao Yuan

    (Contour Crafting Corporation
    University of Southern California)

  • Behrokh Khoshnevis

    (Contour Crafting Corporation
    University of Southern California)

Abstract

In this paper, an automated layer defect detection system for construction 3D printing is proposed. Initially, a step-by-step procedure is implemented to develop a deep convolutional neural network that receives images as input and is able to distinguish concrete layers from other surrounding objects through semantic pixel-wise segmentation. Using data augmentation techniques, 1M labeled images are generated and used to train and test the CNN model. Then, a defect detection module is developed which is able to detect deformations in the printed concrete layers extracted from the images using the CNN model. The evaluation results based on metrics such as accuracy, F1 score, and miss rate verify the acceptable performance of the developed system.

Suggested Citation

  • Omid Davtalab & Ali Kazemian & Xiao Yuan & Behrokh Khoshnevis, 2022. "Automated inspection in robotic additive manufacturing using deep learning for layer deformation detection," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 771-784, March.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:3:d:10.1007_s10845-020-01684-w
    DOI: 10.1007/s10845-020-01684-w
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    References listed on IDEAS

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    1. Olatomiwa Badmos & Andreas Kopp & Timo Bernthaler & Gerhard Schneider, 2020. "Image-based defect detection in lithium-ion battery electrode using convolutional neural networks," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 885-897, April.
    2. Hui Lin & Bin Li & Xinggang Wang & Yufeng Shu & Shuanglong Niu, 2019. "Automated defect inspection of LED chip using deep convolutional neural network," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2525-2534, August.
    3. 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.
    4. Andrej Tibaut & Danijel Rebolj & Matjaž Nekrep Perc, 2016. "Interoperability requirements for automated manufacturing systems in construction," Journal of Intelligent Manufacturing, Springer, vol. 27(1), pages 251-262, February.
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    Cited by:

    1. Tan Kai Noel Quah & Yi Wei Daniel Tay & Jian Hui Lim & Ming Jen Tan & Teck Neng Wong & King Ho Holden Li, 2023. "Concrete 3D Printing: Process Parameters for Process Control, Monitoring and Diagnosis in Automation and Construction," Mathematics, MDPI, vol. 11(6), pages 1-34, March.
    2. 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.

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