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%.
Highlights
Geometric measurement has been widely used in the production of parts, and the improvement of the level of geometric measurement is conducive to ensuring the quality of parts
With the increasing application of artificial neural network (ANN) models in the manufacturing field, geometric measurement based on ANN models has received more and more attention
This article mainly studies the optimization of the part manufacturing quality inspection path based on the model, so this article uses the BP neural network in the neural network model to perform deep learning optimization on the part quality inspection system, accelerates the efficiency of part recognition through the extraction of surface point collection information, and combines the basic principles of model checking and quality control principles to optimize the path
Summary
Geometric measurement has been widely used in the production of parts, and the improvement of the level of geometric measurement is conducive to ensuring the quality of parts. With the increasing application of artificial neural network (ANN) models in the manufacturing field, geometric measurement based on ANN models has received more and more attention. The study of geometric measurement based on the ANN model is conducive to the integration of design, measurement, and processing, thereby improving the efficiency of part processing, and lays a foundation for the development of measurement technology [1]. With the development of artificial neural networks, visual inspection technology is widely used in many fields, especially products having the advantages of noncontact, high precision, and high speed, defect detection, and product size inspection field [2]. The screw defects on the surface of the bolts and the dimensional inspection of the main parts of the valve are still used in visual form, and the inspection efficiency is improved through quality inspection or conventional inspection tools
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