Artificial neural networks for quality control by ultrasonic testing in resistance spot welding

An artificial neural network is proposed to solve problems in the interpretation of ultrasonic oscillograms obtained by the pulse echo method. The artificial neural network classifies resistance spot welds in several quality levels through their respective ultrasonic oscillograms. The inputs of the artificial neural network are vectors obtained from each ultrasonic oscillogram with the help of a MATLAB® program. The training of the artificial neural network uses supervised learning mechanism and therefore each input has the respective desired output (target). There are four targets, one for each considered quality level. The available data set is randomly split into a training subset (to update weight values) and a validation subset (to guard against overfitting by means of cross validation). The number of neurons in the hidden layers is selected considering the overfitting phenomenon. This research work has the aim of contributing to the automation of quality control processes in resistance spot welding.