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A novel NURBS surface approach to statistically monitor manufacturing processes with point cloud data

Author

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  • Lee J. Wells

    (Western Michigan University)

  • Romina Dastoorian

    (Western Michigan University)

  • Jaime A. Camelio

    (Virginia Tech)

Abstract

As sensor and measurement technologies advance, there is a continual need to adapt and develop new Statistical Process Control (SPC) techniques to effectively and efficiently take advantage of these new datasets. Currently high-density noncontact measurement technologies, such as 3D laser scanners, are being implemented in industry to rapidly collect point clouds consisting of millions of data points to represent a manufactured parts' surface. For their potential to be realized, SPC methods capable of handling these datasets need to be developed. This paper presents an approach for performing SPC using high-density point clouds. The proposed approach is based on transforming the high-dimensional point clouds into Non-Uniform Rational Basis Spline (NURBS) surfaces. The control parameters for these NURBS surfaces are then monitored using a surface monitoring technique. In this paper point clouds are simulated to determine the performance of the proposed approach under varying fault scenarios.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:32:y:2021:i:2:d:10.1007_s10845-020-01574-1
    DOI: 10.1007/s10845-020-01574-1
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    References listed on IDEAS

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    1. Mahmoud Mahmoud & J. P. Morgan & William Woodall, 2010. "The monitoring of simple linear regression profiles with two observations per sample," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(8), pages 1249-1263.
    2. 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.
    3. 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.
    4. Bersimis, Sotiris & Psarakis, Stelios & Panaretos, John, 2006. "Multivariate Statistical Process Control Charts: An Overview," MPRA Paper 6399, University Library of Munich, Germany.
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    Cited by:

    1. Kaishu Xia & Thorsten Wuest & Ramy Harik, 2023. "Automated manufacturability analysis in smart manufacturing systems: a signature mapping method for product-centered digital twins," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 3069-3090, October.
    2. Chen Zhao & Shichang Du & Jun Lv & Yafei Deng & Guilong Li, 2023. "A novel parallel classification network for classifying three-dimensional surface with point cloud data," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 515-527, February.
    3. Romina Dastoorian & Lee J. Wells, 2023. "A hybrid off-line/on-line quality control approach for real-time monitoring of high-density datasets," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 669-682, February.

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