Due to its excellent processability, thermal conductivity and high corrosion resistance, copper tubes applied to heat exchangers are being joined through brazing to increase heat exchange efficiency. In order to improve performance, the issue of joint quality of copper tubes, a major member of heat exchangers, is emerging, so research is needed to obtain excellent joint quality of brazing joints that may be damaged. In this study, the quality change of joints according to process variables was studied through induction heating brazing experiments using high frequency. The depth of penetration, which indicates the quality of the junction, was measured, and the center position of the high-frequency electrode and the height of the electrode, which change the location of the heat source applied to the junction, were selected as process variables. Lastly, the thermal image data obtained between the brazing experiments were obtained and the joint quality according to the temperature gradient of the joint was analyzed.

The use of heat exchangers in various applications such as chemical, air conditioning systems, fuel processing, and power industries is increasing. In order to improve the performance of the heat exchanger, the problem of bonding quality of the copper tube, which is a major member, is emerging. However, since the copper tube is in the form of a pipe, it is difficult to identify internal defects with external factors. In this study, a thermal imaging camera was used to develop and verify an algorithm for detecting defects in the brazing part, and in the process, the brazing performance characteristics were analyzed according to the electrode position, and finally, a learning model was developed and performance evaluation was performed. It was confirmed that the method of supplying heat to the base material and melting the filler metal through the heat transfer effect is more effective than supplying heat input to the filler metal in the bonding process of copper tubes through high-frequency induction heating brazing. Thermal image data was used to develop a defect discrimination model, and 80% of training data and 20% of test data were selected, and a neural network-based single-layer copper tube brazing defect discrimination model was developed through k-Flod cross-validation., the prediction accuracy of 95.2% was confirmed as a result of the error matrix analysis.