Quantum Genetic Optimization of Spindle Speed Ratio in Numerical Control Lathe with Automatic Transmission

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Abstract:

Determination of spindle rotate speed of lathe is an important issue when machine tools with changeable spindle gear ratio cutting metal parts, especially in heavy load cutting. Genetic optimization is used to automate design in engineering application for minimizing volume or weight. A quantum genetic optimization method was applied to calculate rotate speed and ratio of spindle transmission for gain maximum motor efficiency in numerical control lathe. Quantum bit code way was applied to simplify calculation and improve compatibility. Rotate angle strategy gate were introduced to Quantum gate for realizing evolution operation. The results were compared with a trial and error procedure usually applied. Quantum genetic arithmetic produced quite well results promptly supplying economic cutting parameters of numerical control (NC) lathe.

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1077-1081

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December 2012

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] S.Y. Yang, L.C. Jiao and H. Xing: Quantum Evolution Algorithm. Engineering Mathematics. Vol. 23 (2006), pp.235-246.

Google Scholar

[2] D.T. Qin, Z.K. Xing and J.H. Wang: Optimization Design of System Parameters of the Gear Transmission of Wind Turbine Based on Dynamics and Reliability. Chinese Journal of Mechanical Engineering. Vol. 44 (2008), pp.24-31.

DOI: 10.3901/jme.2008.07.024

Google Scholar

[3] M. Hirafuji, S. Hagan: A Global Optimization Algorithm Based on the Process of Evolution in Complex Biological Systems. Computers and Electronics in Agriculture. Vol. 29 (2000), pp.125-134.

DOI: 10.1016/s0168-1699(00)00140-x

Google Scholar

[4] P.C. Li, K.P. Song and F.H. Shang: Double Chains Quantum Genetic Algorithm with Application to Neuro-fuzzy Controller Design. Advances in Engineering Software. Vol. 42 (2011), pp.875-886.

DOI: 10.1016/j.advengsoft.2011.06.006

Google Scholar

[5] T.C. Lu and J.C. Juang: Quantum-inspired Space Search Algorithm (QSSA) for Global Numerical Optimization. Applied Mathematics and Computation. Vol. 218 (2011), pp.2516-2532.

DOI: 10.1016/j.amc.2011.07.067

Google Scholar

[6] K.H. Han, J.H. Kim: Quantum-inspired Evolutionary Algorithm for a Class of Combinatorial Optimization. IEEE Transit Evolution Compute. Vol. 6 (2002), pp.580-593.

DOI: 10.1109/tevc.2002.804320

Google Scholar

[7] Y. Wang, X.Y. Feng and Y.X. Huang: A Novel Quantum Swarm Evolutionary Algorithm and its Applications. Neurocomputing. Vol. 70 (2007), pp.633-640.

DOI: 10.1016/j.neucom.2006.10.001

Google Scholar