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Optimization of machining parameters of 2.25Cr1Mo0.25V steel based on response surface method and genetic algorithm

  • Wei Zhang
  • Lei ZhangEmail author
  • Shuqi Wang
  • Baichen Ren
  • Shuai Meng
Technical Paper
  • 19 Downloads

Abstract

High-strength steel 2.25Cr–1Mo–0.25V is an important material for making large pressure vessels due to its superior performance. First, study the effect of the interaction between cutting parameters on cutting force and material removal rate (MRR). The central composite response surface method was used to establish the prediction model of cutting force and MRR during the process of turning high strength steel 2.25Cr–1Mo–0.25V. Secondly, the variance analysis method was used to test the predictive model and the significance of each input parameter. The influence of the interaction between cutting parameters on the cutting force and MRR is analyzed, and the accuracy of the prediction model was further verified. Finally, the genetic algorithm is used to obtain the optimal combination of cutting parameters and the minimum cutting force and maximum MRR.

Keywords

Cutting force Material removal rate Response surface methodology Genetic algorithm 

Notes

Acknowledgements

Thanks to the support of the National Natural Science Foundation of China (51775151).

References

  1. 1.
    Özel, T., Hsu, T.K., Zeren, E.: Effects of cutting edge geometry, workpiece hardness, feed rate and cutting speed on surface roughness and forces in finish turning of hardened AISI H13 steel. Int. J. Adv. Manuf. Technol. 25(3–4), 262–269 (2005)CrossRefGoogle Scholar
  2. 2.
    Subramanian, M., Sakthivel, M.: Optimization of cutting parameters for cutting force in shoulder milling of Al7075-T6 using response surface methodology and genetic algorithm. Procedia Eng. 64(12), 690–700 (2013)CrossRefGoogle Scholar
  3. 3.
    Asiltürk, İ., Akkuş, H.: Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method. Measurement 44, 1697–1704 (2011)Google Scholar
  4. 4.
    Nalbant, M., Gökkaya, H., Sur, G.: Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning. Mater. Des. 28(4), 1379–1385 (2006)CrossRefGoogle Scholar
  5. 5.
    Chen, X., Ma, L., Li, C., et al.: Experimental study and genetic algorithm-based optimization of cutting parameters in cutting engineering ceramics. Int. J. Adv. Manuf. Technol. 74(5–8), 807–817 (2014)CrossRefGoogle Scholar
  6. 6.
    Kosaraju, S., Anne, V.G.: Optimal machining conditions for turning Ti–6Al–4V using response surface methodology. Adv. Manuf. 1(4), 329–339 (2013)CrossRefGoogle Scholar
  7. 7.
    Dong, M., Wang, N.: Adaptive network-based fuzzy inference system with leave-one-out cross-validation approach for prediction of surface roughness. Appl. Math. Model. 35(3), 1024–1035 (2010)CrossRefzbMATHGoogle Scholar
  8. 8.
    Zain, A.M., Haron, H.: Prediction of surface roughness in the end milling machining using fuzzy rule-based. Expert Syst. Appl. 37(2), 1755–1768 (2010)CrossRefGoogle Scholar
  9. 9.
    Zain, A.M., Haron, H., Sharif, S.: Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process. Expert Syst. Appl. 37(6), 4650–4659 (2009)CrossRefGoogle Scholar
  10. 10.
    Palanisamy, P., Rajendran, I., Shanmugasundaram, S.: Optimization of machining parameters using genetic algorithm and experimental validation for end-milling operations. Int. J. Adv. Manuf. Technol. 32(7–8), 644–655 (2007)CrossRefGoogle Scholar
  11. 11.
    Mandal, N., Doloi, B., Mondal, B.: Force prediction model of zirconia toughened alumina (ZTA) inserts in hard turning of AISI 4340 steel using response surface methodology. Int. J. Precis. Eng. Manuf. 13(9), 1589–1599 (2012)CrossRefGoogle Scholar
  12. 12.
    Bouacha, K., Yallese, M.A., Mabrouki, T., et al.: Statistical analysis of surface roughness and cutting forces using response surface methodology in hard turning of AISI 52100 bearing steel with CBN tool. Int. J. Refract. Met. Hard Mater. 28(3), 349–361 (2009)CrossRefGoogle Scholar
  13. 13.
    Liao, Y.S., Lin, H.M., Wang, J.H.: Behaviors of end milling Inconel 718 superalloy by cemented carbide tools. J. Mater. Process. Technol. 201, 460–465 (2007)CrossRefGoogle Scholar
  14. 14.
    Chunjing, L., Dunbing, T., Hua, H., et al.: Analysis of surface roughness robustness based on response surface turning. J. Nanjing Univ. Aeronaut. Astronaut. 44(4), 520–525 (2012)Google Scholar
  15. 15.
    Yongshou, L., Yaoyao, S., Junxue, R., et al.: Research on GH4169 milling force prediction model based on response surface methodology. Mech. Sci. Technol. 29(11), 1547–1552 (2010)Google Scholar
  16. 16.
    Feng, J., Jiafei, Z., Ying, N., et al.: Optimization of cutting parameters of cemented carbide machining based on response surface methodology. Mech. Des. Res. 4, 112–115 (2015)Google Scholar

Copyright information

© Springer-Verlag France SAS, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of Mechanical and Power EngineeringHarbin University of Science and TechnologyHarbinChina
  2. 2.Measurement-Control Technology and Instrument Key Laboratory of Universities in Heilongjiang ProvinceHarbin University of Science and TechnologyHarbinChina

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