Meta-Heuristic Algorithms-Tuned Elman vs. Jordan Recurrent Neural Networks for Modeling of Electron Beam Welding Process

A boost in the preference of high energy beam, such as electron beam, laser beam etc. has led to the requirement of its automation through accurate input–output modelling. Modeling of electron beam welding is conducted in the present study through Elman and Jordan recurrent neural networks (RNNs), both having a single feed-back loop, to meet the said requirement. The RNNs are trained using some nature-inspired optimization tools, namely cuckoo search, firefly, flower pollination, and crow search utilizing input–output welding data, obtained from a computational fluid dynamics-based heat transfer and fluid flow welding model. RNN predictions are validated through real experiments. Thus, the effect of change in the position of the feed-back loop on the accuracy of prediction of RNNs is investigated. In addition, a few popular statistical tests have been used to evaluate the performances of the RNNs tuned by various optimization algorithms, where flower pollination-tuned Jordan RNN is observed to yield the best results.