Model-Based Part Manufacturing Quality Inspection Path Planning

Abstract

This article mainly studies the path planning of part manufacturing quality inspection based on models. Therefore, this paper optimizes the inspection path planning by combining the deep learning of the BP neural network in the neural network model, then improves the recognition efficiency of parts with various shapes through the collection of surface point information, and then combines the basic principles of model inspection and quality control principles to improve the accuracy of quality inspection. In order to better design this optimal path, this paper also designs welding basic formation parameter experiments and robustness analysis experiments to verify the influencing factors of the welding process and the specific results of image processing; this paper also designed the part outer diameter quality inspection test analysis to verify the accuracy and coverage of model-based part manufacturing quality inspection. The results obtained through the collection of experiments are finally compared with the traditional part quality inspection path; the experimental results show that compared with the traditional part quality inspection path, the new part quality inspection path can improve the accuracy rate of 5%-17%, the coverage rate of 9%-20%, and efficiency of 3%-17%.