CONTROLLING RESISTANCE SPOT-WELDING USING NEURAL-NETWORK AND FUZZY-LOGIC

A control scheme able to compensate for variations or errors during au tomatic resistance spot welding to produce consistently sound welds wa s developed and demonstrated through simulation. Fuzzy logic control ( FLC) was employed to overcome the lack of a precise mathematical model of the process. Electrode displacement, indicative of nugget growth, was used as the feedback signal to create appropriate actions to adjus t power delivered in real time. Control action is generated from a rul e based system constructed from experimental data for welds made under a wide variety of conditions. A neural network (NN) was constructed t o provide process input-output relationships and tune the fuzzy rules off line. The FLC system was evaluated using the NN to describe electr ode displacement as a function of the percentage maximum heat input an d welding time. Simulation showed the potential of applying this contr ol scheme to deal with the uncertainties of RSW in a typical automated production environment.