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
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
Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):1–35
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
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
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
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
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.
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
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
Jain VK, Adsul SG (2000) Experimental investigations into abrasive flow machining. Mach Tools Manuf 40(7):1003–1021
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
Kennedy J, Eberhart R (1995). Particle swarm optimization. IEEE Int Conf Neural Netw Piscataway 15(7):1942–1948. doi:10.1109/ICNN.1995.488968
Keerthi SS, Lin CJ (2003) Asymptotic behaviors of support vector machine with gaussian kernel. Neural Comput 15(7):1667–1689. doi:10.1162/089976603321891855
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
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
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
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
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
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
Tang WP, Song W, Yu MX (2003) Research on the extrusion and grinding process of the injection nozzle. Modern Vehicle Power 110:30–34
Tang WP, Song W (2003) Research on orthogonal regression experiment of abrasive flow machining process. Modern Vehicle Power 111:40–43
Vapink VN (1999) An overview of statistical learning theory. Neural Netw IEEE Trans 10(5):988–999
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
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
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.
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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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