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

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.

Automated summary

With the advancement of sensor and measurement technologies, there is a growing need to adapt and develop new Statistical Process Control (SPC) techniques to effectively utilize new datasets. This paper proposes an approach for performing SPC using high-density point clouds, transforming them into NURBS surfaces and monitoring control parameters using a surface monitoring technique. Simulation results are used to evaluate the proposed approach under varying fault scenarios.