Development and Testing of an Intelligent Controller for Optimization of Resistance spot Welding Process

Abstract

Resistance spot welding (RSW) is one of the most widely used welding processes for sheet metal joining, especially in the automotive industry. One of the challenges facing RSW is inconsistencies in quality of the weld. This challenge can be addressed by implementing an on-line weld quality assessment and control. In this study an on-line quality assessment and control model based on Learning Vector Quantization Neural Network (LVQ-NN) system and Adaptive Neuro-Fuzzy Inference System (ANFIS) is developed. The ANFIS model is realized for identifying the RSW dynamical system based on given input output data. It can be used to approximate nonlinear systems with minimum training data, quicker learning speed and higher precision. An indirect estimation of the weld quality employing an LVQ-NN type classifier was designed to provide a real time assessment of the weld quality. Experiments were conducted to establish the effects of various parameters, such as the welding time and the welding current on the quality of the weld produced. A set of important parameters was then selected as the input data to train the proposed LVQ-NN type classifier and ANFIS controller. Once the monitoring and control system had been trained, it was then tested to evaluate their validity in the RSW process. The results show that the classifier based on LVQ-NN was able to classify the weld quality into normal, cold and expulsion based on their corresponding dynamic resistance curves obtained as a function of welding time. The recognition rate was 82 percent for the test data. The proposed control algorithm based on ANFIS demonstrated robust performance reducing the number of expulsion welds by 30% xiii compared to the conventional controller, while increasing the number of normal welds by 31% in RSW process.