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

Ketai He, Min Zhang, Ling Zuo, Theyab Alhwiti and Fadel M. Megahed ()
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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

Journal of Intelligent Manufacturing, 2017, vol. 28, issue 4, No 4, 899-911

Abstract: 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.

Keywords: High-density data; Noncontact scanning systems; Image monitoring; Map projection; Statistical process control (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10845-014-1025-1

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