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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

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Abstract

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.

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Acknowledgements

The first author gratefully acknowledges the financial support of the Ministry of Human Resource Development (MHRD), Government of India, for carrying out this research.

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Authors and Affiliations

  1. Indian Institute of Technology Kharagpur, Kharagpur, India

    Debasish Das & Abhishek Rudra Pal

  2. Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India

    Dilip Kumar Pratihar

  3. Department of Metallurgical and Materials Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India

    Gour Gopal Roy

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  1. Debasish Das
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  2. Dilip Kumar Pratihar
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  4. Abhishek Rudra Pal
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Corresponding author

Correspondence to Dilip Kumar Pratihar.

Appendix

Appendix

Table 2 Material properties used in the phenomenological model [4, 7, 18, 54]
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Table 3 Results of the test scenarios for case one
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Table 4 Results of the test scenarios for case two
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Das, D., Pratihar, D.K., Roy, G.G. et al. 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. Appl Intell 48, 2698–2718 (2018). https://doi.org/10.1007/s10489-017-1101-2

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