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

#### 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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Chakravathy PS, Babu NR (1999), “A new approach for selection of optimal process parameters in abrasive water jet cutting”, Material Manufacturing Process 14(4), pp.581–600.

Crepinsek, Matej, Shih-Hsi Liu, and Luka Mernik (2012), “ A note on teaching–learning-based optimization algorithm, Information Sciences 212”, pp.79-93.

Dede, T.(2014), “Application of teaching-learning-based-optimization algorithm for the discrete optimization of truss structures”, KSCE Journal of Civil Engineering, 18(6), pp.1759-1767.

Kumar, M.S. and Gayathri, G.V. (2015), “A Short Survey on Teaching Learning Based Optimization. In Emerging ICT for Bridging the Future”-Proceedings of the 49th Annual Convention of the Computer Society of India CSI, Volume 2, pp. 173-182.

Karayel, D.(2009), “Prediction and control of surface roughness in CNC lathe using artificial neural network”, Journal of materials processing technology, 209(7), pp.3125-3137.

Kovac, P., Rodic, D., Pucovsky, V., Savkovic, B. and Gostimirovic, M.( 2013), “Application of fuzzy logic and regression analysis for modeling surface roughness in face milling,” Journal of Intelligent manufacturing, 24(4), pp.755-762.

Mandal, B. and Roy, P.K. (2013), “Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization”, International Journal of Electrical Power & Energy Systems, 53, pp.123-134.

Niknam, Taher, Faranak Golestaneh, and Mokhtar Sha Sadeghi (2012), “Multi objective Teaching–Learning-Based Optimization for Dynamic Economic Emission Dispatch”, IEEE Systems Journal 6.2 (2012), pp. 341-352.

Omidvar, M., Fard, R.K., Sohrabpoor, H. and Teimouri, R., (2015), “Selection of laser bending process parameters for maximal deformation angle through neural network and teaching–learning-based optimization algorithm”, Soft Computing, 19(3), pp.609-620.

Pawar P. J. and R. Venkata Rao. (2013), “Parameter optimization of machining processes using teaching–learning-based optimization algorithm”, The International Journal of Advanced Manufacturing Technology, 67.5-8, pp. 995-1006.

Pontes, F.J., Ferreira, J.R., Silva, M.B., Paiva, A.P. and Balestrassi (2010), “Artificial neural networks for machining processes surface roughness modeling”, The International Journal of Advanced Manufacturing Technology, 49(9-12), pp.879-902.

R. Venkata Rao, V. D. Kalyankar (2013), Engineering Applications of Artificial Intelligence, 26, pp.524-531.

Rao RV, Savsani VJ, Vakharia DP (2011), “Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems”, Computer-Aided Design 43, pp.303–315.

Rao RV, Savsani VJ, Vakharia DP (2012), “Teaching–learning-based optimization: an optimization method for continuous non-linear large scale problems”, Information Science 183(1), pp.1–15.

Rao, Ravipudi V., Vimal J. Savsani, and D. P. Vakharia (2011), “Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems”, Computer-Aided Design, Volume 43, pp.303-315.

Rao, Ravipudi Venkata, and Vivek D. Kalyankar (2012), “Parameters optimization of continuous casting process using teaching-learning-based optimization algorithm”, International Conference on Swarm, Evolutionary, and Memetic Computing, Springer Berlin Heidelberg.

Rao, R.V. and Kalyankar, V.D., (2013), “Multi-pass turning process parameter optimization using teaching–learning-based optimization algorithm”, Scientia Iranica, 20(3), pp.967-974.

Rao, R.V. and More, K.C., (2015), “Optimal design of the heat pipe using TLBO (teaching–learning-based optimization) algorithm”, Energy, 80, pp.535-544.

Rao, R.V. and Kalyankar, V.D., (2013), “Parameter optimization of modern machining processes using teaching–learning-based optimization algorithm”, Engineering Applications of Artificial Intelligence, 26, pp.524-531.

Satapathy, Suresh Chandra, Anima Naik, and K. Parvathi (2012), “ Teaching learning based optimization for neural networks learning enhancement”. International Conference on Swarm, Evolutionary, and Memetic Computing. Springer Berlin Heidelberg.

Satapathy, S.C., Naik, A. and Parvathi, K., (2013). “A teaching learning based optimization based on orthogonal design for solving global optimization problems”. Springer Plus, 2(1), pp.1-12.

Togan, Vedat(2012), “Design of planar steel frames using teaching–learning based optimization”, Engineering Structures 34 pp. 225-232.

Waghmare, Gajanan(2013), “Comments on A note on teaching–learning-based optimization algorithm”, Information Sciences 229, pp. 159-169.

Wang, L., Zou, F., Hei, X., Yang, D., Chen, D. and Jiang, Q.,(2014), “An improved teaching–learning-based optimization with neighborhood search for applications of ANN”. Neurocomputing, 143, pp.231-247.

Yildiz, A.R., (2013), “Optimization of multi-pass turning operations using hybrid teaching learning-based approach”, The International Journal of Advanced Manufacturing Technology, 66(9-12), pp.1319-1326.

Yu, K., Wang, X. and Wang, Z., (2014), “An improved teaching-learning-based optimization algorithm for numerical and engineering optimization problems”, Journal of Intelligent Manufacturing, pp.1-13.

Uzlu, E., Kankal, M., Akpınar, A. and Dede, T., (2014), “Estimates of energy consumption in Turkey using neural networks with the teaching–learning-based optimization algorithm”. Energy, 75, pp.295-303.

Zain, A.M., Haron, H. and Sharif, S., (2010), “Prediction of surface roughness in the end milling machining using Artificial Neural Network”. Expert Systems with Applications, 37(2), pp.1755-1768.

Zhai, Zhibo, Shujuan Li, and Yong Liu (2015), “Parameter Determination of Milling Process Using a Novel Teaching-Learning-Based Optimization Algorithm”, Mathematical Problems in Engineering, pp.1-15.

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