Automated Inspection of Micro Laser Spot Weld Quality Using Optical Sensing and Neural Network Techniques

This paper presents an approach to the automated inspection of laser spot welding processes using optical sensing and neural network techniques. An optical sensor is used to derive signals covering a spectrum ranging from visible to infrared bands. A set of features extracted from the signals is fed into a neural network to classify the quality of welds. A series of experiments was carried out using a pulsed Nd:YAG laser and a common SMD (surface mounted devices) as a test component. The results obtained show that this approach can be used to inspect the laser welding quality for the microelectronics industry