Understanding weld modelling processes using a combination of trained neural networks

In this paper, a set of neural networks has been trained for weld modelling processes with different architecture and training parameters. The set of neural networks is trained using actual weld data available in the literature. The performance of each neural network in this set is defined by two performance measures of interest, namely training error and generalization error. Instead of using one of the best networks from this set of trained networks, a method of combining the outputs of all the network from the set is proposed and is called the combined output (or output of the combined network). It is shown that the performance measures of interest obtained using this combined output is better than the performance measures of interest obtained by all the individual neural networks in the set.