Product Design Defect Detection and Automatic Repair Algorithm Based on CAD and Machine Vision

Producers need to strictly control the quality of their products when facing customer needs, ensuring the qualification rate of the products. The level of product design is not only related to the abilities of designers themselves, but also the role of design tools in product design cannot be underestimated. This article adopts a defect detection algorithm that combines computer-aided design (CAD) and machine vision and learns and identifies different types of product defects by training deep learning models. This algorithm utilizes the geometric information of products in CAD models and image processing methods in machine vision technology to detect and repair surface defects in product design automatically. The results show that it is significantly superior to the wavelet transform method in terms of detection accuracy, stability, and detail capture ability. Input the product design image to be tested into the model, and the model will output information on the type and severity of defects at each location. By combining deep learning and machine vision technology, this algorithm can more accurately detect and locate various types of product defects. © 2024 U-turn Press LLC,.