A Multi-Sensor Data Fusion System for Laser Welding Process Monitoring

Abstract

Most existing laser welding process monitoring (LWPM) technologies focus on detecting postprocess defects. However, in sheet metal laser welding applications such as welding of electronic consumer products during mass production, in-process defect detection is more important. In this paper, a compact LWPM system using multi-sensor data fusion to detect in-process defects has been built. This system can collect the time series of plasma intensity, light intensity and temperature data for feature analysis. To verify the system's effectiveness, a plasma-light-temperature dataset has been compiled, which consists of 5,836 samples of nine classes, including one positive class and eight negative classes of typical in-process defects. A multi-sensor data fusion network based on a convolution neural network for in-process defect detection, called IDDNet, has also been proposed. Experimental results have demonstrated that IDDNet can achieve better multi-classification results than the support vector machine, with an overall accuracy of 97.57%. In particular, considering this monitoring process as a binary classification problem, IDDNet can achieve a 99.42% accuracy. Moreover, IDDNet can reach an average speed of 0.79ms per sample on a single GTX 1080ti graphics card, which meets the real-time requirement for industrial production. The proposed LWPM system has been successfully verified in real applications of sheet metal laser welding. INDEX TERMS laser welding process monitoring, in-process defect detection, multi-sensor data fusion, convolution neural network

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“…In the existing researches, four kinds of sensors are mainly used to monitor the laser welding process, such as visual [14][15][16], acoustic [17][18][19], optical [20,21] and thermal [22,23] sensors. These sensors are used to capture the changes of characteristic quantities during the welding process [24][25][26][27] and establish a correlation between the changing trends of characteristic quantities and the final welding effect. In the actual production line, it is difficult to realize real-time visual testing because of the need to capture and process a large amount of image data.…”