Research on Intelligent Detection of Surface Defects in Irregular Parts Based on Machine Vision

Due to the complex geometric shape, low contrast, and imaging interference of surface defects in irregular parts, traditional detection methods suffer from accuracy fluctuations and high misjudgment rates. This article addresses the systemic challenges of defect detection in irregular parts. By quantitatively analyzing the coupling mechanism between optical parameters, algorithm parameters, and hardware performance, a multivariate collaborative optimization criterion is proposed to build an intelligent detection platform for surface size accuracy. This platform can achieve functions such as distinguishing, calibrating, reconstructing dimensions, and error analysis and compensation of part features. Experiments have shown that the combination of multiple angle light sources can reduce the misjudgment rate of pits to below 5%; Further introduce response surface methodology and dynamic weight allocation strategy, establish a joint optimization space for optical parameters and algorithms, and address the limitations of traditional linear models. Verified by the precision component production line, the system's misjudgment rate has decreased from 8.6% to 1.8%, and the detection efficiency has increased by 40%. This study provides a theoretical and practical closed-loop solution for the development of a multimodal quality inspection system for irregular parts, promoting the development of high-precision quality inspection technology in intelligent manufacturing scenarios.