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A deep learning solution for real-time quality assessment and control in additive manufacturing using point cloud data

Author

Listed:
  • Javid Akhavan

    (Stevens Institute of Technology)

  • Jiaqi Lyu

    (Stevens Institute of Technology)

  • Souran Manoochehri

    (Stevens Institute of Technology)

Abstract

This work presents an in-situ quality assessment and improvement technique using point cloud and AI for data processing and smart decision making in Additive Manufacturing (AM) fabrication to improve the quality and accuracy of fabricated artifacts. The top surface point-cloud containing top surface geometry and quality information is pre-processed and passed to an improved deep Hybrid Convolutional Auto-Encoder decoder (HCAE) model used to statistically describe the artifact's quality. The HCAE’s output is comprised of 9 × 9 segments, each including four channels with the segment's probability to contain one of four labels, Under-printed, Normally-printed, Over-printed, or Empty region. This data structure plays a significant role in command generation for fabrication process optimization. The HCAE’s accuracy and repeatability were measured by a multi-label multi-output metric developed in this study. The HCAE’s results are used to perform a real-time process adjustment by manipulating the future layer's fabrication through the G-code modification. By adjusting the machine's print speed and feed-rate, the controller exploits the subsequent layer’s deposition, grid-by-grid. The algorithm is then tested with two defective process plans: severe under-extrusion and over-extrusion conditions. Both test artifacts' quality advanced significantly and converged to an acceptable state by four iterations.

Suggested Citation

  • Javid Akhavan & Jiaqi Lyu & Souran Manoochehri, 2024. "A deep learning solution for real-time quality assessment and control in additive manufacturing using point cloud data," Journal of Intelligent Manufacturing, Springer, vol. 35(3), pages 1389-1406, March.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:3:d:10.1007_s10845-023-02121-4
    DOI: 10.1007/s10845-023-02121-4
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    References listed on IDEAS

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    1. Sebastian Larsen & Paul A. Hooper, 2022. "Deep semi-supervised learning of dynamics for anomaly detection in laser powder bed fusion," Journal of Intelligent Manufacturing, Springer, vol. 33(2), pages 457-471, February.
    2. 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.
    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. Hasan Tercan & Tobias Meisen, 2022. "Machine learning and deep learning based predictive quality in manufacturing: a systematic review," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 1879-1905, October.
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