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Minimum zone torisity using minimum potential energy algorithms
|關鍵字:||最小環帶誤差;minimum zone error;形狀誤差;圓環;最小位能原理;虛功原理;form error;Torus;Torisity;minimum potential energy;principle of virtual work||出版社:||機械工程學系所||引用:|| Fan, K. C., Lee, J. C., “Analysis of minimum zone sphericity error using minimum potential energy theory,” Precision Engineering, Vol. 23, pp. 65-72, 1999.  Huang, P. H., Lee, J. C., “Minimum zone evaluation of conicity error using minimum potential energy algorithms,” Precision Engineering, Vol. 34, pp. 709-717, 2010.  ASME standard Y14.5M-1994, “National Standard on Dimensioning and Tolerancing,” The American Society of Mechanical Engineers, New York, 1994.  Lai, H. Y., Jywe, W. Y., Chen, C. K., “Precision modeling of form errors for cylindricity evaluation using genetic algorithms,” Precision Engineering, Vol. 24, pp. 310-319, 2000.  Zhang Ke, “Minimum Zone Evaluation of Sphericity Error Based on Ant Colony Algorithm,” IEEE International conference on Electronic Measurement and Instruments, pp. 535-538, 2007.  Aguirre-Cruz, J. A., and Raman, S., “Torus form inspection using coordinate sampling,” ASME J. Manufacturing Science and Engineering, Vol. 127, pp. 84-95, 2005.  Prakasvudhisarn, C., Kunnapapdeelert, S., “Torusity Tolerance Verification Using Swarm Intelligence,” Industrial Engineering and Management Systems, Vol. 6, pp. 94-105, 2007.  Yong Wang, Lin Li, Jun Ni, S. h. Huang, “Form Tolerance Evaluation of Toroidal Surfaces Using Particle Swarm Optimization,” ASME J. Manufacturing Science and Engineering, Vol. 131, pp. 1-9, 2009.  J. Kennedy, R. Eberhart, “Particle swarm optimization,” IEEE Conference Neural Networks, Vol. 4, pp. 1942-1948, 1995.  R. Storn, K. Price, “Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces,” Technical report, California: International Computer Science Institute, Berkeley, 1995.||摘要:||
In this paper, minimum zone evaluation of torus using minimum potential energy algorithms. Minimum zone error based on looking for minimum potential energy of the system. The virtual system consists of two coaxial, same center,and same distance form the center of the tube to the center of the torus. All data points are enclosed within two fictitious torus surface. When the system spring to promote the approximation of two torus surfaces in the process of contraction. Therefore, the virtual system towards the direction of minimum elastic potential energy. Finally, the system reaches stable state with minimum potential energy. Normal deviation between such two torus surfaces becomes minimum zone error.
This paper presented an adjustment form error strategy based on minimum potential energy algorithms. For solving the problem of high dimensions and large number of data points, experimental results will be obtained in a shorter computing time. The control experiment results evaluated by different method such as particle swarm optimization and differential evolution, indicate that effectiveness of proposed method.
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