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

Lee J. Wells (), Romina Dastoorian () and Jaime A. Camelio ()
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Lee J. Wells: Western Michigan University
Romina Dastoorian: Western Michigan University
Jaime A. Camelio: Virginia Tech

Journal of Intelligent Manufacturing, 2021, vol. 32, issue 2, No 1, 329-345

Abstract: 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.

Keywords: High-density measurements; Non-contact scanning systems; NURBS surfaces; Statistical process control; Surface monitoring (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10845-020-01574-1

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