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Optimization method of machining parameters based on intelligent algorithm

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Abstract

The processing parameters have a particularly significant impact on the quality and efficiency of processing. Selecting the correct processing parameters can greatly improve the processing performance of the machine tool. To this end, by improving the chromosome structure and genetic operators of the GA algorithm, a new GA-BP neural network algorithm is proposed and combined BP neural network method for adaptive crossover and mutation probability optimization. Then, through comparison experiments. After selecting a certain type of CNC EDM machine, find its standard process parameter table and select 50 groups of data as preparation. 30 groups of data are randomly sampled from the inside to serve as training sample data, and the remaining 20 groups serve as performance test samples. Experimental results show that the prediction accuracy of the new algorithm is higher than that of the conventional algorithm, pulse width or peak current. The new prediction results are often closer to the true value, and the prediction accuracy is higher, which can better meet the processing requirements.

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References

  1. Michal Šajgalík, A., Martikáň, A., Czán, et al.: Computing of the theoretical value of roughness parameter Rz when turning with helical cutting edge. Technol. Eng. 13(2), 9–10 (2017)

    Google Scholar 

  2. Oussama Zerti, M.A., Yallese, R., Khettabi, et al.: Design optimization for minimum technological parameters when dry turning of AISI D3 steel using Taguchi method. Int. J. Adv. Manuf. Technol. 89(5–8), 1–20 (2017)

    Google Scholar 

  3. Antoshchenkova, I., Bykadorov, I.: Monopolistic competition model: the influence of technological progress on equilibrium and social optimality. Autom. Remote Control 78(2), 3–31 (2014)

    MathSciNet  MATH  Google Scholar 

  4. Sniders, A., Laizans, A., Komass, T.: Invariant control of the technological plants to compensate an impact of main disturbances preemptively. Latvian J. Phys. Techn. Sci. 53(3), 2–23 (2016)

    Google Scholar 

  5. Servili, M., Esposto, S., Taticchi, A., et al.: The effect of diverse agricultural and technological factors on olive oil quality and yield. Acta Hort. 1057(1057), 603–618 (2014)

    Article  Google Scholar 

  6. Yin, Y., Shengzhi, L.I., Kang, Y., et al.: Influence of technological parameters on lamination defect of large diameter heavy wall P92 seamless steel pipe elongated by 2-roll rotary rolling process. Mater. Sci. Technol. 22(6), 123–128 (2014)

    Google Scholar 

  7. Zhang, X., Liu, Q., Huang, W., et al.: Investigation of non-sinusoidal oscillation waveform function and technological parameters for continuous casting mold. Iron Steel 49(8), 42–47 (2014)

    Google Scholar 

  8. Fan, C., Xing, Q., Fu, Q., et al.: A hybrid intelligent algorithm by combining particle swarm optimization with variable neighborhood search for solving nonlinear bilevel programming problems. Syst. Eng. Theory Pract. 35(2), 473–480 (2015)

    Google Scholar 

  9. Son, C.: Intelligent rule-based sequence planning algorithm with fuzzy optimization for robot manipulation tasks in partially dynamic environments. Inf. Sci. 342(3), 209–221 (2015)

    Google Scholar 

  10. Chen, L., Cao, D., Liu, Y.: A new intelligent jigsaw puzzle algorithm base on mixed similarity and symbol matrix. Int. J. Pattern Recogn. Artif. Intell. 32(2), 31–35 (2017)

    MathSciNet  Google Scholar 

  11. Kaur, M., Kaur, G., Singh, D.: Optimization using Bio-inspired algorithm. Intell. Waterdrop Algorithm 106(13), 36–41 (2014)

    Google Scholar 

  12. Ding, T., Zhu, X.: Application research of heliostats field scheduling with intelligent algorithm. Acta Energ. Solaris Sinica 35(11), 214–219 (2014)

    Google Scholar 

  13. Martinez, D., Valdez, F.: An improved intelligent water drop algorithm to solve optimization problems. Stud. Comput. Intell. 61(5), 233–239 (2015)

    Article  Google Scholar 

  14. Junhua Xiong, T., Wang, R., Li: Research on a hybrid LSSVM intelligent algorithm in short term load forecasting. Clust. Comput. 31(2), 1–8 (2018)

    Google Scholar 

  15. Zheng, E., Rang, Q., Yang, Z.: Simulation research of cloud models intelligent algorithm for temperature control. Appl. Mech. Mater. 84(4), 543–547 (2014)

    Google Scholar 

  16. Mia, M., Dey, P.R., Hossain, M.S., et al: Taguchi S/N based optimization of machining parameters for surface roughness, tool wear and material removal rate in hard turning under MQL cutting condition. Measurement 12(3), 380–391 (2018)

    Article  Google Scholar 

  17. Yue, Z., Huang, C., Zhu, H., et al: Optimization of machining parameters in the abrasive waterjet turning of alumina ceramic based on the response surface methodology. Int. J. Adv. Manuf. Technol. 71(9–12), 2107–2114 (2014)

    Article  Google Scholar 

  18. Bharathi, R.S., Srinivas Pramod, C.V., Vamshee Krishna, K., et al.: Optimization of electrical discharge machining parameters on hardened die steel using Firefly Algorithm. Eng. Comput. 31(1), 1–9 (2015)

    Article  Google Scholar 

  19. Rufeng, X.U.: Tool orientation optimization method based on kinematics constraints of the machine tool in multi-axis machining with a ball-end cutter. J. Mech. Eng. 51(23), 160 (2015)

    Article  Google Scholar 

  20. Rao, C.M., Rao, K.J.: Optimization of material removal rate & surface roughness in dry turning of medium carbon steel En19 by using Taguchi method. Int. J. Eng. Res. Appl. 31(13), 57 (2015)

    Google Scholar 

  21. Deris, A.M., Zain, A.M., Sallehuddin, R., et al.: Harmony search optimization in dimensional accuracy of die sinking EDM process using SS316L stainless steel. J. Phys. Conf. Ser. 892(1), 2003 (2017)

    Google Scholar 

  22. Yahya, E., Ding, G.F., Qin, S.F.: Sensitivity analysis and optimization of machining parameters based on surface roughness prediction for AA6061. Appl. Mech. Mater. 598(3), 181–188 (2014)

    Article  Google Scholar 

  23. Xiao-Yu, W.: Research on hydraulic control system of jumbolter based on intelligent mix optimization algorithm. Int. J. Control Autom. 9(9), 163–170 (2016)

    Article  Google Scholar 

  24. Sabarinath, P., Thansekhar, M.R., Saravanan, R.: Research article multiobjective optimization method based on adaptive parameter harmony search algorithm. J. Appl. Math. 2015(8), 12 (2015)

    MATH  Google Scholar 

  25. Rajmohan, T., Palanikumar, K., Kathirvel, M.: Optimization of machining parameters in drilling hybrid aluminium metal matrix composites—TNMSC. Trans. Nonferr. Metals Soc. China 22(6), 1286–1297 (2015)

    Article  Google Scholar 

  26. Zare, M., Koch, M.: Hybrid signal processing/machine learning and PSO optimization model for conjunctive management of surface–groundwater resources. Neural Comput. Appl. 33, 8067–8088 (2021)

    Article  Google Scholar 

  27. Muralidhar, N., Himabindu, M., Ravikrishna, R.V.: Modeling of a hybrid electric heavy duty vehicle to assess energy recovery using a thermoelectric generator. Energy 148(APR.1), 1046–1059 (2018)

    Article  Google Scholar 

  28. Mahmoodzadeh, A., Mohammadi, M., Daraei, A., et al.: Forecasting tunnel geology, construction time and costs using machine learning methods. Neural Comput. Appl. 33, 321–348 (2021)

    Article  Google Scholar 

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Acknowledgements

This work was support by the Zhejiang provincial key research and development project (No. 2021C01149), Zhejiang provincial Science Foundation (No. LGF19F020010).

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Authors and Affiliations

  1. School of Intelligent Manufacturing, Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou, 310053, Zhejiang, China

    Jie Cai & Wei Zhang

  2. School of Creative Arts and Design, Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou, 310053, Zhejiang, China

    Jinlian Deng

  3. School of Automation, Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou, 310053, Zhejiang, China

    Weisheng Zhao

Authors
  1. Jie Cai
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  2. Wei Zhang
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  3. Jinlian Deng
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  4. Weisheng Zhao
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Correspondence to Jie Cai.

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Cai, J., Zhang, W., Deng, J. et al. Optimization method of machining parameters based on intelligent algorithm. Distrib Parallel Databases 40, 737–752 (2022). https://doi.org/10.1007/s10619-021-07357-8

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