Prediction of the Weld Qualities Using Surface Appearance Image in Resistance Spot Welding

Abstract

The quality of the resistance spot weld is predicted qualitatively using information from the weld’s external apparent image. The predicting tool used for weld qualities was a convolution neural network (CNN) algorithm with excellent performance in pattern recognition. A heat trace image of the weld surface was used as information on the external apparent image of welds. The materials used in the experiment were advanced high strength steel (AHSS) with 980 MPa strength, and uncoated cold-rolled (CR) steel sheets and galvannealed (GA) steel sheets were used. The quantitatively predicted weld quality information contained tensile shear strength, nugget diameter, fracture mode of welds, and expulsion occurrence. The predicted performance of the verification step of the model determined through the learning process was as follows; the predicted error rate for tensile shear strength and nugget diameter were 2.2% and 2.6%, respectively. And the predicted accuracy on fracture mode and expulsion occurrence was 100%.