A virtual-electric-field-based method for distributing sampling points on the free-form surfaces of parts. (20th July 2021) - Lu, Xincheng; Sheng, Buyun; Wang, Hui; Luo, Ruiping; Fu, Gaocai
Abstract: The sampling process used for on-machine inspections is one of the key factors affecting the efficiency and quality of such inspections. For high-precision contact measurements in particular, a good distribution of sampling points can reduce the time spent on the inspection process and improve the reliability of the test results. However, in practice, most production and test equipment can only provide simple measurement methods and insufficient test functions, resulting in reduced automation of the test process and poor-quality test results when testing complex part surfaces. In this paper, we propose a sampling point distribution method for on-machine inspection based on a virtual electric field, to solve the common problem of unreasonable inspection methods and the need to improve efficiency and quality when inspecting free-form surfaces during on-machine inspection. First, the discrete curvature of each node in the grid model is calculated according to Gauss–Bonnet's discrete curvature formula, which is used as the original data for the subsequent analysis process. The corresponding spatial search sphere is then constructed in the space where the model is located, and based on this, we propose a search method for extreme curvatures on the model surface. A virtual-electric-field model of the part surface is then constructed, and the searched curvature extremes are used to determine the positive charge of an electrostatic field on the model's surface. The sampling points are regarded as charged particles, and the repulsion equation between sampling points is given. We then analyze the motion law of the sampling points in the electronic potential field within the sampling region, and also give the step length of each motion iteration process. Finally, the adaptive distribution of the sampling points is experimentally verified using several mechanical models with complex surfaces. The results show that the sampling distribution method proposed in this paper outperforms some traditional or commonly used distribution methods in terms of fitting accuracy.