Research on electrical contact performance based on machine vision

The electrical connection serves as a vital and abundant link in power, electronic equipment, and systems, with the electrical contact acting as its core component. In practical working conditions, fretting wear occurs during the usage of electrical contacts, leading to surface destruction and a decline in their performance. Determining the degree of wear on electrical contacts is crucial for assessing their failure in engineering applications. This study focuses on conducting fretting wear tests on copper material under different cycles for electrical contacts while utilizing machine vision algorithms to detect the morphological characteristics of wear marks. Gray threshold segmentation is applied to extract texture features from wear marks after various oxidation conditions. Pseudocolorization techniques are employed to process extracted morphologies, followed by calculating their characteristic areas. Finally, combining these results with contact resistance curves allows for judging the electrical conductivity of the electrical contact under different cycles.