An intelligent control system for resistance spot welding using a neural network and fuzzy logic

An intelligent control system based on fuzzy logic able to compensate for variations and errors during automatic resistance spot welding (RSW) and produce consistent sound welds was developed. A fuzzy logic control (FLC) scheme was employed to overcome the lack of a precise mathematical model of the process. Electrode displacement, indicative of weld nugget growth, was used as the feedback signal to create appropriate actions to adjust power delivered to welds in real time. Control action is generated from a rule-based system constructed from experimental data for welds made under a wide variety of conditions. A neural network (NN) was constructed to provide process input-output relationships and tune the fuzzy rules off line. The FLC system was evaluated using the NN to describe electrode displacement as a function of percentage maximum heat input and welding time. Results showed the suitability of applying this control scheme to deal with the uncertainties of RSW in a typical automated production environment