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Enhancing the monitoring of 3D scanned manufactured parts through projections and spatiotemporal control charts

Author

Listed:
  • Ketai He

    (University of Science and Technology Beijing)

  • Min Zhang

    (Tianjin University)

  • Ling Zuo

    (Tianjin University)

  • Theyab Alhwiti

    (Auburn University)

  • Fadel M. Megahed

    (Auburn University)

Abstract

As measurement technologies evolve, our ability to detect, isolate and diagnose process faults on the shop-floor is rapidly changing. Three dimensional scanners provide the opportunity to capture the entire surface geometry of a manufactured part and allow for the detection of a wide variety of fault patterns that may not be captured by traditional measurement devices. Despite their advantages, their use in practice is limited due to the complexities associated with the analysis of 3D laser scan data (point clouds). Therefore, the objective of our work is to allow practitioners to fully utilize the inherent advantages of point clouds by providing a framework that can facilitate their analysis and visualization. More specifically, we transform point clouds into 2D images (without the loss of any spatial information) to benefit from the image analysis and monitoring techniques that are currently being implemented on the shop-floor. We provide numerical and experimental examples to illustrate and validate the advantages of our proposed method. Finally, we offer advice to practitioners and recommendations for future research.

Suggested Citation

  • Ketai He & Min Zhang & Ling Zuo & Theyab Alhwiti & Fadel M. Megahed, 2017. "Enhancing the monitoring of 3D scanned manufactured parts through projections and spatiotemporal control charts," Journal of Intelligent Manufacturing, Springer, vol. 28(4), pages 899-911, April.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:4:d:10.1007_s10845-014-1025-1
    DOI: 10.1007/s10845-014-1025-1
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    References listed on IDEAS

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    1. Du, Shichang & Lv, Jun, 2013. "Minimal Euclidean distance chart based on support vector regression for monitoring mean shifts of auto-correlated processes," International Journal of Production Economics, Elsevier, vol. 141(1), pages 377-387.
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    Cited by:

    1. Lee J. Wells & Romina Dastoorian & Jaime A. Camelio, 2021. "A novel NURBS surface approach to statistically monitor manufacturing processes with point cloud data," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 329-345, February.
    2. Ketai He & Qian Zhang & Yili Hong, 2019. "Profile monitoring based quality control method for fused deposition modeling process," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 947-958, February.

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