Laser Detection of Surface Quality of Electrical Contacts Based on Ensemble Learning

The quality of silver-based contacts directly affects the overall performance and service life of electrical connectors. There are many factors affecting the surface quality: production process, equipment accuracy, transportation, vibration. In the assembly process of electrical connection equipment, it is necessary to first screen out contacts with unqualified surface quality. But the traditional testing process requires the participation of professional workers, which is difficult to meet the demand for high-speed and efficient testing under the trend of output expansion. Fortunately, in recent years, the research of machine learning has created the possibility of online identification and classification of fine surface defects of silver-based contacts. In this paper, based on machine learning, laser ranging, automatic classification and other methods for surface quality detection, contact automatic detection and sorting. The results show that the detection time can be greatly reduced with the help of computer, and the detection data can be integrated to provide data support for the subsequent life estimation of electrical connectors.