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%.
Highlights
The ability to predict or evaluate the quality of resistance spot welding (RSW) in real-time using nondestructive methods is key to the automation of automotive assembly processes
Muhammad et al [2] proposed the models to predict the growth of nugget diameter and heat-affected zone (HAZ), which consisted of welding current, welding time and hold time as variables using the surface response analysis method in RSW of mild steel
The purpose of this study is to suggest how to predict the weld quality of GPa-grade steel, where the quality of welds (TSS, nugget diameter, and fracture mode) varies significantly despite slight changes in the process setting conditions
Summary
The ability to predict or evaluate the quality of resistance spot welding (RSW) in real-time using nondestructive methods is key to the automation of automotive assembly processes. Since the quality evaluation method requires time and manpower, many studies have been conducted to predict the quality nondestructively. Hao et al [1] studied statistical models on nugget diameter, weld strength, and expulsion according to process variable conditions using multiple linear regression analysis. Muhammad et al [2] proposed the models to predict the growth of nugget diameter and heat-affected zone (HAZ), which consisted of welding current, welding time and hold time as variables using the surface response analysis method in RSW of mild steel. Darwish et al [3]
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