Min-Liang Huang1, Jr-syu Yang1, Jheng-yu Wu This email address is being protected from spambots. You need JavaScript enabled to view it.1, Shih-Hsing Chang This email address is being protected from spambots. You need JavaScript enabled to view it.1

1Department of Mechanical and Electro-Mechanical Engineering, Tamkang University, No.151, Yingzhuan Rd., Tamsui Dist., 25137, New Taipei City, Taiwan (R.O.C.)


Received: October 30, 2019
Accepted: April 4, 2020
Publication Date: June 1, 2020

Download Citation: ||https://doi.org/10.6180/jase.202006_23(2).0021  


The dimensional accuracy of the workpiece will exceed the tolerance, therefore, to predict how many workpieces have been cut, the turning tool must be replaced is the important issue in machining field. To deal well with the normally distributed random error existed in the wear value prediction of CNC turning tools, this paper introduces the ν-Support Vector Regression (ν-GSVR) model with the Gaussian loss function to the prediction field of short-term wear value. A new hybrid evolutionary algorithm (namely CCGA) is established to search the appropriate parameters of the ν-GSVR, coupling the Chaos Map, Cloud model and Genetic Algorithm. Consequently, a new forecasting approach for the short-term wear value prediction of CNC turning tools, combining ν-GSVR model and CCGA algorithm, is proposed. The forecasting process considers the wear value prediction of CNC turning tools during the first few time intervals, the turning tool wear value for the spindle revolution, cutting depth and feed rate. It is used to verify the forecasting performance of the proposed model. The experiment indicates that the model yield more accurate results than the compared models in forecasting the short-term wear value on the turning tools. In this way, we can figure out how many turning tools to prepare for similar workpieces, which can reduce the stock of turning tools, and reduce the labor costs on quality inspection of workpieces during this period.

Keywords: Wear Value Prediction; CNC Turning Tools; Support Vector Machine; Support Vector Regression; Gaussian loss function; Genetic Algorithm; Chaos Map; Cloud model.



  1. [1] CHANG Shih-Hsing, Research of Expert System is applied in Automated Lathe Machining. The first automatic control application technology seminar of the Republic of China, March 6, 1993.
  2. SHANG Ning,QIN Minggui,WANG Yaqin. A BP Neural Network Method for Short-term Traffic Flow Forecasting on Cr6ossroads[J]. Computer Applications and Software,2006, 23(2): 32-33.
  3. [2] HUANG Darong, SONG Jun, WANG Dacheng, CAO Jianqiu, LI Wei. Forecasting Model of Traffic Flow Based on ARMA and Wavelet Transform[J]. Computer Engineering and Applications, 2006, 6(36): 191-194.
  4. [3]HAN Chao,SONG Su,WANG Chenghong. A Real-time Short-term Traffic Flow Adaptive Forecasting Method Based on ARIMA Model[J]. JOURNAL OF SYSTEM SIMULATION, 2004, 7(16):1530-1534.
  5. [4] WANG YI-BING, PAPAGEORGIOUM, MESSMER A. Real-time freeway traffic state estimation based on extended Kalman filter: A general approach [J]. Transportation Research, 2007, 41(2): 167-181.
  6. [5] Lan Hong li. SHORT-TERM TRAFFIC FLOW PREDICTION FOR HIGHWAY TUNNEL BASED ON FUZZY CLUSTERING ANALYSISP[J]. Computer Applications and Software, 2010, 27(1): 151-153.
  7. [6] HU Dan, XIAO Jian, CHE Chang. Lifting wavelet support vector machine for traffic flow prediction[J]. Application Research of Computers, 2007, 24(8): 276-278.
  8. [7] XIE Hong, LIU ZHONGong Hua. Short-term traffic flow prediction based on embedding phase-space and blind signal separation [C].Proceedings of the 2008 IEEE Conference on Cybernetics and IntelligentSystems. [S.L. ]: IEEE Press, 2008: 760-764.
  9. [8] ShangNing,QinMinggui,WangYaqin,CuiZhongfa,CuiYan,ZhuYangyong. A BP NEURAL NETWORK METHOD FOR SHORT-TERM TRAFFIC FLOW FORECASTING ON CROSSROADS[J] ComputerApplications and Software,2006,23(2):33-35.
  10. [9] YE Yan,LU Zhi-lin. Neural network short-term traffic flow forecasting model based on particle swarm optimization[J].Computer Engineering and Design, 2009,30(18):4296-4299.
  11. [10] LIU Hanli, ZHOU Chenghu, ZHU Axing, LI Lin. Multi-Population Genetic Neural Network Model for Short-term Traffic Flow Predictionat Intersections[J]. Acta Geodaetica et Cartographica Sinica, 2009,38(4):364-368.
  12. [11] CAO J R, CAI A N. A robust shot transition detection method based on support vector machine in compressed domain[J].Pattern Recognition Letters, 2007, 28(12):1534-1540.
  13. [12] ZHU Sheng-xue, ZHOU Jun, BAO Xu. Short-term Traffic Forecast Based on WD and SVM [J]. University of Science and Technology of Suzhou (Engineering and Technology), 2007, 20(3):80-85.
  14. [13] Ren Qi-liang,XIE Xiaosong,PENG Qiyuan. GSVMR Model on Short-term Forecasting of City Road Traffic Volume[J]. Journal of Highway and Transportation Research and Development, 2008,2(52):135-138.
  15. [14] Qi Wu. A hybrid-forecasting model based on Gaussian support vector machine and chaotic particle swarm optimization[J]. Expert Systems with Applications, 2010, 37:2388–2394.
  16. [15] YAN H S, XU D. An approach to estimating product design time based on fuzzy-support vector machine [J]. IEEE Transactions on Neural Networks, 2007, 18(3):721-731.
  17. [16] SUN Yuefeng, ZHANG Shenhong, Wang Xiao-ling, MEI Chuanshu. Multi-objective optimization of regional water resources based on mixed genetic algorithm[J]. 2009, 29(1):139-142.
  18. [17] Abdelhadi, B., Benoudjit, A., & NaitSaid, N. (2003). Self-adaptive genetic algorithms based characterization of structured model parameters. In: Proceedings of the 35th southeastern symposium on system theory, Virginia (pp. 181–185).
  19. [18] Min, S. H., Lee, J., & Han, I. (2006). Hybrid genetic algorithms and support vector machines for bankruptcy prediction. Expert Systems with Applications, 31(3), 652–660.
  20. [19] SCH LKOPF B, SMOLA A J, WILLIAMSON R C, etal. New support vector algorithms [J].Neural Computation, 2000,12(5):1207-1245.
  21. [20] WU Qi, YAN Hongsen. Forecasting method based on support vector machine with Gaussian loss function [J]. Computer Integrated Manufacturing Systems, 2009, 15(2), 306–310.
  22. [21] YAN H S, XU D. An approach to estimating product design time based on fuzzy ν-support vector machine [J]. IEEE Transactions on Neural Networks, 2007, 18(3):721-731.
  23. [22] ZHANG Zong fei. Novel Improved Quantum Genetic Algorithm [J]. Computer Engineering, 2010, 36(6):181-183.
  24. [23] CAO Dao you, CHENG Jia xing. A Genetic Algorithm Based on Modified Selection Operator and Crossover Operator [J]. COMPUTER TECHNOLOGY AND DEVELOPMENT, 2010, 20(2):45-48.
  25. [24] ZHANG Jig Zhao, JIANG Tao. Improved adaptive genetic algorithm[J]. Computer Engineering and Applications, 2010,46(11):53-55.
  26. [25] Choi C, Lee J J. Chaotic local search algorithm[J]. Artificial Life and Robotics,1998,2(1):41-47.
  28. [27] GONG Dun-wei, ZHU Mei-Qiang, GUO Xi-jin, Li Ming. Genetic algorithm based on chaotic mutation to deal with premature convergence[J]. Control and Decision, 2003,18(6): 686 -689.
  29. [28] WANG Fang, DAI Yong-shou, WANG Shao-shui. Modified chaos-genetic algorithm.Computer Engineering and Applications[J]. Computer Engineering and Applications, 2010,46(6).
  30. [29] Chen Guan-rong, Mao Yao-bin, Chui C K.A symmetric image encryption scheme based on 3D chaotic cat maps[J].Chaos, Solutions and Fractals, 2004, 21(3):749-761. No in Bib
  31. [30] Lu Q Z, Shen G L, Yu R Q. A chaotic approach to maintain the population diversity of genetic algorithm in network training [J]. Computational Biology and Chemistry, 2003, 27(3):363.
  32. [31] Liu B, Wang L, Jin Y H, et al. Improved particle swarm optimization combined with chaos [J]. Chaos Solutions and Fractals, 2005, 25(5):1261.
  33. [32] LI Dey,i MENG Haijun, SHI Xueme.i Membership clouds and membership cloud generators[J]. Journal of Computer Research and Development, 1995, 32(6): 15-2.
  35. [34] LIU Changyu, LIDey, i DU Y,i et a. Some statistical analysis of the normal cloud model [J]. Information and Control, 2005,4(2): 236-239.
  36. [35] Hsu, C. C., Wang, K. S., & Chang, S. H., Bayesian decision theory for support vector machines: Imbalance measurement and feature optimization, Expert Systems With Applications, Vol. 38, no. 5, pp. 4698-4704, May 2011.
  37. [36] Hsu, C. C., Wang,. K. S., Chung, H. T., & Chang, S. H., A Study of visual behavior of multidimensional scaling for kernel perceptron algorithm, Neural Computing and Applications, Vol. 26, no. 3, pp. 679-691, 2015.
  38. [37] Taylor, J.W. Short-term load forecasting with exponentially weighted methods. IEEE Trans. Power Syst. 2012, 27, 458–464.