A Novel, Nelder-Mead Optimization Approach, based on Neuro-regression modeling for the Energy Efficiency Parameters of End Milling Process

Global crises are increasing day by day due to the rapid depletion of energy supplies around the planet. One of the goals of engineering is to prevent this situation by developing innovative solutions to this rapid energy consumption that has disappeared in the world. A solution could be to reduce the energy consumption of the machines that are used during production. In this study, a new design technique based on the neuro-regression approach and non-linear regression modeling was offered as an alternative to Taguchi design to reduce energy consumption. Thus, a cutting parameter optimization model was created to examine the effects of the constraint conditions on energy consumption. The cutting power, the surface roughness of the part, and tool life were handled as objective functions(constraint conditions). First of all, the multiple non-linear regression modeling was created using design variables in end milling . These design variables were determined as spindle rotational speed, feed rate power, radial cut depth, axial cut depth, and cutting speed. Then, objective functions were brought to the proper minimum optimal levels due to this optimization modeling. As a result of the optimization model built with design variables, accurate modeling was achieved in this work by studying several optimization models utilized to optimize the minimum objective functions, which play a significant role in reducing energy consumption in end milling. After the optimization, the maximum value was found as 110.791. At the end of the study, some options of direct search method to maximize and minimize results were applied.

Keywords:

End milling, energy efficiency optimization,

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[1] P. Albertellia, A. Kesharia, and A. Matta, “Energy oriented multi cutting parameter optimization in face milling.” Journal of Cleaner Production, vol.137, pp. 1602-1618, 2016
[2] L. Zhoua, J. Lia, F. Lia, G. Mendisb, and J. W. Sutherlandb, “Optimization parameters for energy efficiency in end milling,” PROCEDIA CIRP, vol. 69, pp. 312-317, Copenhagen, Denmark, 2018.
[3] R. Kumar, P. S. Bilga, and S. Singh, "Multi-objective optimization using different methods of assigning weights to energy consumption responses, surface roughness and material removal rate during rough turning operation,” Journal of Cleaner Production, vol.164, pp.45-57, 2017
[4] B. A. Khidhir, and B. Mohamed, "Analyzing the effect of cutting parameters on surface roughness and tool wear when machining nickel-based Hastelloy – 276." Materials Science and Engineering Conference Series,vol.17, pp. 012043, 2011.
[5] J. Ribeiro, H. Lopes, L. Queijo, and D. Figueiredo, "Optimization of cutting parameters to minimize the surface roughness in the end milling process using the Taguchi method." Periodica Polytechnica. Engineering. Mechanical Engineering; Budapest, vol. 61, pp. 30-35, 2017.
[6] S. Velchev, I. Kolev, K. Ivanov, and S. Gechevski, “Empirical models for specific energy consumption and optimization of cutting parameters for minimizing energy consumption during turning,” J. Clean. Prod, vol. 80, pp. 139-149, 2014.
[7] C. C. Negrete, "Optimization of cutting parameters for minimizing energy consumption in turning of AISI 6061 T6 using Taguchi methodology and ANOVA," Journal of Cleaner Production, vol. 53, pp.195-203, 2013.
[8] P. T. Mativenga, and M. F. Rajemi, “Calculation of optimum cutting parameters based on minimum energy footprint,” CIRP Ann Manuf Technol, vol. 60, pp. 149-152, 2011.
[9] M. F. Rajemi, P. T. Mativenga, and A. Aramcharoen, “Sustainable machining : selection of optimum turning conditions based on minimum energy considerations,” J Clean Prod, vol. 18, pp, 1059-1065, 2010.
[10] I. Asilturk, S. Neseli, and M.A. Ince, "Optimisation of parameters affecting surface roughness of Co28Cr6Mo medical material during CNC lathe machining by using the Taguchi and RSM methods," Measurement, vol. 78, pp. 120-128, 2016.
[11] R. K. Bhushan, “Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites,” Journal of Cleaner Production, vol. 39, pp. 242-254, 2013.
[12] C. Li, Q. Xiao, Y. Tang, and L. Li, “A method integrating Taguchi, RSM and MOPSO to CNC machining parameters optimization for energy saving,” J. Clean. Prod., vol. 135, pp. 263-275, 2016.
[13] A. J. Kulkarni, K. Tai, and A. Abraham, “Probability collectives: a distributed multi-agent system approach for optimization,” In: Intelligent Systems Reference Library, pp. 86, 2015.
[14] I. Polatoğlu, L. Aydın, B.C. Nevruz and S. Ozer, “A Novel Approach for the Optimal Design of a Biosensor”. Analytical Letters, pp.1-18, 2020.
[15] I. Polatoglu, and L. Aydin, “A new design strategy with stochastic optimization on the preparation of magnetite cross-linked tyrosinase aggregates (MCLTA),” Process Biochemistry, vol. 99, pp. 131–138, 2020.