Modeling of TIG welding process using conventional regression analysis and neural network-based approaches

Conventional regression analysis was carried out on some experimental data of a tungsten inert gas (TIG) welding process (obtained from published literature), to find its input–output relationships. One thousand training data for neural networks were created at random, by varying the input variables within their respective ranges and the responses were calculated for each combination of input variables by using the response equations obtained through the above conventional regression analysis. The performances of the conventional regression analysis approach, a back-propagation neural network (BPNN) and a genetic-neural system (GA-NN) were compared on some randomly generated test cases (experimental), which are different from the training cases. It is interesting to note that for the said test cases, the NN-based approaches could yield predictions that are more adaptive in nature compared to those of the more conventional regression analysis approach. It could be due to the fact that NN-based approaches are able to bring adaptability, which is missing in the conventional regression analysis. Moreover, GA-NN was found to perform better than the BPNN, in most of the test cases. A BPNN works based on the principle of a steepest descent method, whose solutions have the chance of being trapped at the local minima, whereas in GA-NN, the search for a minimum deviation in prediction, is carried out using a GA. However, their performance depends on the nature of the deviation function.