PREDICTION OF SPOT WELDING PARAMETERS USING FUZZY LOGIC CONTROLLING

The Resistance Spot Welding (RSW) represents one of the most important welding processes. The resistance spot welding quality depends on the process parameters like welding current, electrode force and welding time and their chosen levels. In this work, the experimental part is validated by the simulation part, where the last will be used later for predicting the results for new data with a very acceptable percentage of accuracy. This study presents an experimental work of the resistance spot welding for two similar sheets of Austenitic Stainless Steels (AISI 304) that are intended to be held together in one point by the pressure of the electrodes, with high magnitude of electrical current to be applied, where the resistance spot welding parameters (welding current and welding time) are changeable to show each of the parameter’s action on the welded material properties (The Maximum Shear Load that the metal can be subject to besides The Nugget Zone Diameter of the welded contact area). The experimental work in this study delivers genuine and important data that will be the basis for the Fuzzy Logic Controller (FLC), which will be set up then. The Artificial Intelligence (which is presented by the fuzzy logic controller) role is to predict the optimal welded material parameters for any given resistance spot welding parameters, and to discover the probability of expulsion, failure, or breaking in the welding process before it takes place or happens, where in this study, the FLC predicted the optimum value of the maximum shear load for RSW, which occurs at the welding time=20 cycle and the welding current=8 KA, while the estimated optimum value of the Nugget Diameter by FLC for RSW is found at welding time=20 cycle and welding current=8 KA. This prediction will save the metal parts and the electrodes of welding, besides saving the cost and the effor