Abstract

Surface point cloud data from three-dimensional optical scanners provide rich information about the surface geometry of scanned parts and potential variation in the surfaces from part-to-part. It is challenging, however, to make full use of these data for statistical process control purposes to identify sources of variation that manifest in a more complex nonparametric manner than variation in some prespecified set of geometric features of each part. We develop a framework for identifying nonparametric variation patterns that uses dissimilarity representation of the data and dissimilarity-based manifold learning, which helps discover a low-dimensional implicit manifold parameterization of the variation. Visualizing how the parts change as the manifold parameters are varied helps build an understanding of the physical characteristic of the variation. We also discuss using the nominal surface of parts when it is accessible to improve the computational expense and visualization aspects of the framework. Our approaches clearly reveal the nature of the variation patterns in a real cylindrical-part machining example and a simulated square head bolt example.

摘要

来自 3D 光学扫描仪的表面点云数据提供了有关扫描部件的表面几何形状和部件之间表面潜在变化的丰富信息。然而，为了统计过程控制的目的，充分利用这些数据来识别变异源具有挑战性，这些变异源以比每个零件的某些预先指定的几何特征集的变异更复杂的非参数方式表现出来。我们开发了一个用于识别非参数变化模式的框架，该框架使用数据的不同表示和基于不同的流形学习，这有助于发现变化的低维隐式流形参数化。可视化零件如何随着歧管参数的变化而变化，有助于了解变化的物理特性。我们还讨论了在可以访问时使用零件的标称表面来提高框架的计算成本和可视化方面。我们的方法清楚地揭示了真实圆柱零件加工示例和模拟方头螺栓示例中变化模式的性质。