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A prediction method for the precision of extrusion grinding of a needle valve body

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Abstract

The needle valve body is an important part of the fuel injection nozzle of a diesel engine, and its machining precision will affect the performance of the diesel engine. As an important process of needle valve finishing, extrusion grinding can reduce the flow error and improve the flow consistency. However, it is difficult to determine the non-linear relationship between the precision of the grinding process and the processing parameters using conventional experimental methods. Firstly, the various parameters affecting the grinding precision of the needle valve body are analyzed. Secondly, an experiment is designed to acquire the test data with our grinding machine. The method based on support vector machine combined with particle swarm optimization (PSO-SVM) is proposed to predict the precision of the extrusion grinding of the needle valve body. The particle swarm optimization (PSO) algorithm is used to optimize the parameters of the SVM. The results show that our optimized prediction model is more accurate and faster than the BP neural network algorithm. The proposed prediction method provides guidance for selecting the grinding parameters of the needle valve body to further improve the precision of processing.

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References

  1. Bustillo A, Correa M (2012) Using artificial intelligence to predict surface roughness in deep drilling of steel components. J Intell Manuf 23(5):1893–1902

    Article  Google Scholar 

  2. Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):1–35

    Article  Google Scholar 

  3. Chou PH, Wu MJ, Chen KK (2009) Integrating support vector machine and genetic algorithm to implement dynamic wafer quality prediction system. Expert Syst Appl 37(6):4413–4424

    Article  Google Scholar 

  4. Çaydaş U, Ekici S (2012) Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel. J Intell Manuf 23(3):639–650

    Article  Google Scholar 

  5. Chen WC, Tai PH et al (2008) A neural network-based approach for dynamic quality prediction in a plastic injection molding process. Expert Syst Appl 35(3):843–849

    Article  MathSciNet  Google Scholar 

  6. D’Addona DM, de Aguiar PR, Matarazzo D, Bianchi EC, Martins CHR (2016) Neural networks tool condition monitoring in single-point dressing operations. Procedia CIRP 41:431–436

    Article  Google Scholar 

  7. Fan HB, Zhang YT, Ren GQ, Luo HF (2006) Study on prediction model of oil spectrum based on support vector machines. Lubr Eng, 11:148–150.

    Google Scholar 

  8. Ge M, Du R, Zhang G, Xu Y (2004) Fault diagnosis using support vector machine with an application in sheet metal stamping operations. Mech Syst Signal Process 18(1):143–159

    Article  Google Scholar 

  9. Huang Y, Zhang XJ, Li J, Li XM (2011) Research on the micro jet nozzle fluid the experiment. China Surf Eng 24(5):68–72

    Google Scholar 

  10. Jain VK, Adsul SG (2000) Experimental investigations into abrasive flow machining. Mach Tools Manuf 40(7):1003–1021

    Article  Google Scholar 

  11. Jurkovic Z, Cukor G, Brezocnik M et al (2016) A comparison of machine learning methods for cutting parameters prediction in high speed turning process. J Intell Manuf. doi:10.1007/s10845-016-1206-1

    Google Scholar 

  12. Kennedy J, Eberhart R (1995). Particle swarm optimization. IEEE Int Conf Neural Netw Piscataway 15(7):1942–1948. doi:10.1109/ICNN.1995.488968

    Google Scholar 

  13. Keerthi SS, Lin CJ (2003) Asymptotic behaviors of support vector machine with gaussian kernel. Neural Comput 15(7):1667–1689. doi:10.1162/089976603321891855

    Article  MATH  Google Scholar 

  14. Li D, Chen W, Liu C et al (2012) A non-linear quality improvement model using SVR for manufacturing TFT-LCDs. J Intell Manuf 23(3):835–844

    Article  Google Scholar 

  15. Mahesh G, Muthu S, Devadasan SR (2015) Prediction of surface roughness of end milling operation using genetic algorithm. Int J Adv Manuf Technol. doi:10.1007/s00170-014-6425-z

    Google Scholar 

  16. Sivarao PB, El-Tayeb NSM, Vengkatesh VC (2009) Neural network multi-layer perceptron modeling for surface quality prediction in laser machining. Appl Mach Learn. doi:10.5772/8612

    Google Scholar 

  17. Saric T, Simunovic G, Simunovic K (2013) Use of neural networks in prediction and simulation of steel surface roughness. Int J Simul Model. doi:10.2507/IJSIMM12(4)2.241

    Google Scholar 

  18. Song W, Wu ZH, Tang WP (2003) Determine the optimal combination of abrasive flow process factors using orthogonal test method. Modern Vehicle Power 109:26–32

    Google Scholar 

  19. Spatti D, Nakai ME, Aguiar PR, Junior HG, Bianchi EC, D’Addona DM (2015) Evaluation of neural models applied to the estimation of tool wear in the grinding of advanced ceramics. J Expert Syst Appl 42/20:7026–7035

    Google Scholar 

  20. Tang WP, Song W, Yu MX (2003) Research on the extrusion and grinding process of the injection nozzle. Modern Vehicle Power 110:30–34

    Google Scholar 

  21. Tang WP, Song W (2003) Research on orthogonal regression experiment of abrasive flow machining process. Modern Vehicle Power 111:40–43

    Google Scholar 

  22. Vapink VN (1999) An overview of statistical learning theory. Neural Netw IEEE Trans 10(5):988–999

    Article  Google Scholar 

  23. Yu XT, Chu FL, Hao R (2009) Fault diagnosis approach for rolling bearing based on support vector machine and soft morphological filters. J Mech Eng 45(7):75–80. doi:10.3901/JME.2009.07.075

    Article  Google Scholar 

  24. Ye YW, Lu JJ, Qian ZQ, Wang YX (2016) Study on the temperature error prediction of mechanical temperature instrument based on LS-SVM. Chin J Sci Instr 37(1):57–66

    Google Scholar 

  25. Zhao ZG, Zhang CJ, Gou XF, Sang HT (2015) Solar cell temperature prediction model of support vector machine optimized by particle swarm optimization algorithm. Acta Physica Sinica. doi:10.7498/aps.64.0088801.

    Google Scholar 

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Acknowledgements

This work was supported by Shanghai Science and Technology Commission, under Grant No. 13521103604. We are grateful for the financial support, and also would like to thank the anonymous reviewers and the editor for their comments and suggestions.

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Correspondence to Wei Liu.

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Cai, Hx., Liu, W. A prediction method for the precision of extrusion grinding of a needle valve body. Prod. Eng. Res. Devel. 11, 295–305 (2017). https://doi.org/10.1007/s11740-017-0723-x

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  • DOI: https://doi.org/10.1007/s11740-017-0723-x

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