Phenomenological model-based study on electron beam welding process, and input-output modeling using neural networks trained by back-propagation algorithm, genetic algorithms, particle swarm optimization algorithm and bat algorithm

摘要

High power density welding technologies are widely used nowadays in various fields of engineering. However, a computationally efficient and quick predictive tool to select the operating parameters in order to achieve the specified weld attribute is conspicuously missing in the literature. In the present study, a computationally efficient inverse model has been developed using artificial neural networks (ANNs). These ANNs have been trained with the outputs of physics-based phenomenological model using back-propagation (BP) algorithm, genetic algorithm (GA), particle swarm optimization (PSO) algorithm and bat algorithm (BA) separately to develop both the forward and reverse models. Unlike data driven ANN model, such approach is unique and yet based on science. Power, welding speed, beam radius and power distribution factor have been considered as input process parameters, and four weld attributes, such as length of the pool, depth of penetration of the pool, half-width of the pool and cooling time are treated as the responses. The predicted outputs of both the forward and reverse models are found to be in good agreement with the experimental results. The novelty of this study lies with the development and testing of five neural network-based approaches for carrying out both forward and reverse mappings of the electron beam welding process.