Fuzzy Logic-Based Techniques for Modeling the Correlation between the Weld Bead Dimension and the Process Parameters in MIG Welding

Fuzzy logic-based techniques have been developed to model input-output relationships of metal inert gas (MIG) welding process. Both conventional and hierarchical fuzzy logic controllers (FLCs) of Mamdani type have been developed, and their performances are compared. The conventional FLC suffers from the curse of dimensionality for handling a large number of variables, and a hierarchical FLC was proposed earlier to tackle this problem. However, in that study, both the structure and knowledge base of the FLC were not optimized simultaneously, which has been attempted here. Simultaneous optimization of the structure and knowledge base is a difficult task, and to solve it, a genetic algorithm (GA) will have to deal with the strings having varied lengths. A new scheme has been proposed here to tackle the problem related to crossover of two parents with unequal lengths. It is interesting to observe that the conventional FLC yields the best accuracy in predictions, whereas the hierarchical FLC can be computationally faster than others but at the cost of accuracy. Moreover, there is no improvement of interpretability by introducing a hierarchical fuzzy system. Thus, there exists a trade-off between the accuracy obtained in predictions and computational complexity of various FLCs. 1. Introduction Arc welding process plays a vital role in manufacturing. Despite the widespread use of arc welding for joining the metals, total automation of the process is yet to be achieved, and it is so due to the fact that the physics of the problem is not fully understood and quantified. Metal inert gas (MIG) welding is one of the most commonly used arc welding processes, due to its low initial cost and high productivity. To have the better knowledge and control of MIG welding process, it is necessary to determine its input-output relationships. MIG welding is a complex process involving multiple variables. The quality of weld bead is decided by its mechanical properties, which are dependent on its both metallurgical properties and bead geometry, which, in turn, depend on a number of input process parameters, such as welding speed, voltage, wire feed rate, gas flow rate, nozzle-plate distance, torch angle, surface tension of the molten metal, and electromagnetic force. Several approaches had been developed by various researchers to predict bead geometry in welding. Those approaches include theoretical studies, statistical regression analysis, and soft computing-based approaches. Rosenthal [1] studied temperature distributions in an infinite plate, due to a moving point heat