Nature‑Inspired Optimization Algorithm‑Tuned Feed‑Forward and Recurrent Neural Networks Using CFD‑Based Phenomenological Model‑Generated Data to Model the EBW Process

摘要

To automate the electron beam welding process, the identification of its contributing parameters is a must, for which it isrequired to establish the input–output correlations in both forward and reverse directions as accurately as possible. In thepresent investigation, both feed-forward and recurrent neural networks are developed for the said purposes, which have beentrained using the welding data collected from an existing computational fluid dynamics (CFD)-based phenomenologicalmodel with the help of some natured-inspired optimization tools like cuckoo search, firefly, flower pollination, crow searchalgorithms, particle swarm optimization, covariance adaptation evolution strategy and spider monkey optimization, separately.The results of the trained networks have been validated using some real experimental data. The novelty of this study lieswith the applications of these newly developed nature-inspired optimization algorithms to tune the neural networks usingthe CFD-based phenomenological model-generated welding data. In addition, the performances of these neural networkstuned using the said nature-inspired optimization algorithms have been compared through some statistical tests. In general,flower pollination-tuned recurrent neural network is found to provide the best predictions.