Detection of Hot Cracking for 6000 Series Aluminum Alloys using a Multi-Sensor Based Deep Learning Model

Abstract

This study developed a monitoring technology using a multi-sensor based deep learning model to diagnose hot cracking in aluminum alloy laser welding. Hot cracking that occurs during the laser welding process of aluminum alloys is difficult to diagnose accurately with a single sensor signal, necessitating multi-sensor based process monitoring technology. To monitor these hot cracks, laser-induced plasma, acoustic, and elastic wave signals were simultaneously measured using a spectrometer, non-contact acoustic sensor, and contact acoustic sensor during the overlap laser welding process of 6000 series aluminum alloys. The welded specimens were classified into normal and cracked specimens through bead analysis, and features related to hot cracking were extracted from each sensor signal to utilize the measured multi-sensor signals for monitoring. The extracted features from each signal were used as inputs for a Deep Neural Network (DNN) model capable of learning complex nonlinear relationships, and the hyperparameters of the DNN model were optimized using a genetic algorithm. The DNN model trained with multi-sensor data diagnosed hot cracking with an accuracy of 93.75%.