Quality monitoring of spot welding with advanced signal processing and data-driven techniques

As resistance spot welding is a crucial and widely used metal joining technique nowadays, a cheap and highly accurate online quality monitoring scheme is strongly demanded in industry. In this paper, a novel framework for welding quality examination is proposed by using advanced signal processing and artificial intelligence techniques. Our proposed framework consists of two objective schemes, a general welding quality classification scheme and a more detailed and advanced welding quality estimation scheme. To achieve a fast, convenient and cheap monitoring strategy, only the easily obtained electrical signals are monitored for data acquisition. For the welding quality classification, a self-organizing map under a windowed feature extraction is applied. Results of the spot welding experiment from a portable welding machine show that the classification accuracy can be 92.9%. For welding quality estimation, variation of the welding time and detailed aspects of welding quality are introduced. In particular, a modified recurrent neural network is utilized for the size estimation of the heat affected zone, while a novel self-organizing map type classifier is used to detect the expulsion condition along with its occurrence time. During the spot welding experiments, the average error percentage of the heat affected zone size estimation is around 6.7% and the accuracy on expulsion detection reaches 93.3%.