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Soft computing techniques for modelling and multi-objective optimization of magnetic field assisted powder mixed EDM process

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Abstract

The present work emphasizes on artificial neural network (ANN) and genetic algorithm (GA) for modelling and optimization of Magnetic Field Assisted powder mixed EDM (PMEDM) process. Fabricated Magnetic Field Assisted PMEDM setup was utilized for experimentation to machine Aluminium 6061 alloy by aluminium powder agglomerated in EDM oil. Peak current (IP), spark on duration (SON), spark off duration (SOFF), magnetic field (MF) and powder concentration (PC) are considered as machining parameters, and material removal rate (MRR), tool wear rate (TWR), surface roughness (SR), recast layer thickness (RLT) and overcut (OC) as machining performances. Mathematical models for predicting the responses namely MRR, TWR, SR, RLT and OC have been developed using feed-forward backpropagation ANN. The influence of machining parameters on MRR, TWR, SR, RLT and OC has been studied on developed ANN model. Further ANN models are interfaced with GA for multi-objective optimization to find out the optimum machining parameters for maximizing MRR, and minimizing TWR, SR, RLT and OC. ANN model developed provides better prediction on the responses for 2 hidden layers with 6 and 4 neurons in each hidden layer. The absolute error between the predicted and experimental results at optimized level observed is less than 5%. Machined surface at optimum machining parameters revealed the presence of smaller craters, voids, micro-cracks and molten particles.

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Acknowledgements

The authors are thankful to the Material Science and Engineering department, IIT Kanpur, Kanpur for providing SEM facility and Advanced Machine Tool Lab for providing EDM for accomplishing this work. The authors also gratefully acknowledge TEQIP- II & III, MNNIT Allahabad for the financial support provided to carry out this research.

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  1. Department of Mechanical Engineering, Motilal Nehru National Institute of Technology Allahabad, Prayagraj, Uttar Pradesh, 11004, India

    Arun Kumar Rouniyar & Pragya Shandilya

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  1. Arun Kumar Rouniyar
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Correspondence to Arun Kumar Rouniyar.

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Rouniyar, A.K., Shandilya, P. Soft computing techniques for modelling and multi-objective optimization of magnetic field assisted powder mixed EDM process. Neural Comput & Applic 34, 18993–19014 (2022). https://doi.org/10.1007/s00521-022-07498-6

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  • DOI: https://doi.org/10.1007/s00521-022-07498-6

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