Parameter optimization of Milling Operation Using Teaching-Learning-Based Optimization and Artificial Neural Network

Satypal T. Warghat, T. R. Deshmukh

Abstract


In milling operation, cutting parameter optimization greatly affects the production time, cost, profit rate, and the quality of the final products. The current work investigated an approach to the determination of the optimum machining parameters to produce good surface finish and high material removal rate in the plain milling of AISI 1020 Mild Steel using an experimental approach, TLBO and ANN. The mathematical modeling is prepared for prediction of output parameters. For the optimization, teacher-learning-based optimization (TLBO) technique is used. Firstly TLBO is used to optimize machining parameters on the basis of surface roughness and material removal rate separately and then simultaneously for both output parameters. Artificial Neural Network (ANN) is used to validate the results based on mathematical modeling.  Using both optimization techniques, it is observed that the results are agrees well with experimental and ANN. Using ANN, it is observed that the predicted results and desired results are found very close.


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MAYFEB Journal of Mechanical Engineering
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