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.

随着传感器和测量技术的进步，不断需要适应和开发新的统计过程控制（SPC）技术，以有效地利用这些新数据集。当前，诸如3D激光扫描仪之类的高密度非接触式测量技术正在工业中实施，以快速收集由数百万个数据点组成的点云，以代表制造零件的表面。为了实现其潜力，需要开发能够处理这些数据集的SPC方法。本文提出了一种使用高密度点云执行SPC的方法。所提出的方法是基于将高维点云转换为非均匀有理基础样条（NURBS）曲面。然后使用表面监视技术监视这些NURBS表面的控制参数。在本文中，通过模拟点云来确定所提出方法在各种故障情况下的性能。