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ANN modelling and Elitist teaching learning approach for multi-objective optimization of \(\upmu \)-EDM

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Abstract

Fabrication of micro-holes has been carried out in Inconel 718 using micro electrical discharge machining operation. Artificial neural network modelling has been carried out to predict Material Removal Rate, Overcut effect and Recast Layer thickness. The training, testing and validation data sets were collected by conducting experiments. It is observed that ANN is a powerful prediction tool. It provides agreeable results when experimental and predicted data are compared. Further optimization of the process variables has been carried out using different meta heuristic approaches like Elitist Teaching learning based optimization, Multi-Objective Differential Evolution and Multi-Objective Optimization using an Artificial Bee Colony algorithm. The comparisons are carried out to improve the accuracy of the model on the basis of Pareto front solutions.

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  1. Department of Mechanical Engineering, National Institute of Technology, Rourkela, Odisha, India

    Kalipada Maity & Himanshu Mishra

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  1. Kalipada Maity
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  2. Himanshu Mishra
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Correspondence to Kalipada Maity.

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Maity, K., Mishra, H. ANN modelling and Elitist teaching learning approach for multi-objective optimization of \(\upmu \)-EDM. J Intell Manuf 29, 1599–1616 (2018). https://doi.org/10.1007/s10845-016-1193-2

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