Please wait a minute...
浙江大学学报(工学版)
JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE)  2018, Vol. 52 Issue (1): 16-23    DOI: 10.3785/j.issn.1008-973X.2018.01.003
Mechanical and Energy Engineering     
Optimization of grinding parameters based on parts' friction properties
ZHAO Bin, ZHANG Song, LI Jian-feng
1. School of Mechanical Engineering, Shandong University, Jinan 250061, China;
2. Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, Shandong University, Jinan 250061, China
Download:   PDF(1179KB) HTML
Export: BibTeX | EndNote (RIS)      

Abstract  

An optimization model with multiple-input and multiple-output was achieved using genetic algorithm method and neural network method. Surface roughness parameters (arithmetic mean deviation, surface bearing index, core fluid retention index and valley fluid retention index) were taken as input layer factors, and the output layer factors were grinding parameters (wheel linear speed, workpiece linear speed, grinding depth and longitudinal feed rate). Moreover, this model was applied to predict grinding parameters for special surface topography aiming at different friction performances in hydrodynamic lubrication. Grinder, grinding wheel and workpiece size different from sample experiment were applied in the verification test. Results show that the maximum error between predicted values and experiments is only 12.87%, which implies the good accuracy, reliability and universality of this optimization model. This optimization model can efficiently improve the design efficiency of process plan.

Received: 25 February 2017      Published: 15 December 2017
CLC:  TH117  
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Cite this article:

ZHAO Bin, ZHANG Song, LI Jian-feng. Optimization of grinding parameters based on parts' friction properties. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2018, 52(1): 16-23.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2018.01.003     OR     http://www.zjujournals.com/eng/Y2018/V52/I1/16


基于零件摩擦学性能的磨削参数优化

采用遗传算法和神经网络相结合，以表面形貌评价参数（表面算数平均偏差、表面支承指数、核心区液体滞留指数和谷底区液体滞留指数）为输入层，以磨削参数（砂轮转速、工件速度、横向进给量和背吃刀量）为输出层，建立多输入多输出的优化预测模型；针对不同使用需求的特定表面形貌结构，利用此模型预测流体润滑条件下相应的磨削工艺参数.验证实验采用与样本实验不同的机床、砂轮和工件尺寸，结果显示，预测值与实验值的最大偏差为12.87%，充分证明了该模型的优化精确性、可靠性和普适性；该模型可有效提高工艺方案的设计效率.

[1] XIAO L, BJÖRKLUND S, ROSÉN B G. The influence of surface roughness and the contact pressure distribution on friction in rolling/sliding contacts[J]. Tribology International, 2007, 40(4):694-698.
[2] 汪久根, 冯兰兰, 李阳. 粗糙表面形貌对电蚀的影响[J]. 浙江大学学报:工学版, 2015, 49(11):2025-2032. WANG Jiu-gen, FENG Lan-lan, LI Yang. Influences of surface topography on electrial pitting[J]. Journal of Zhejiang University:Engineering Science, 2015, 49(11):2025-2032.
[3] 方新燕. 三维表面形貌的表征及润滑特性的研究[D]. 合肥:合肥工业大学, 2006. FANG Xin-yan. Characterization and lubrication analysis of three-dimensional surface topography[D]. Hefei:HeFei University of Technology, 2006.
[4] 刘会静, 洪军, 杨国庆,等. 微凸体切向滑移特性[J]. 浙江大学学报:工学版, 2014, 48(6):1114-1119. LIU Hui-jing, HONG Jun, YANG Guo-qing, et al. Tangential sliding characteristics of asperities[J]. Journal of Zhejiang University:Engineering Science, 2014, 48(6):1114-1119.
[5] RAMESH A, AKRAM W, MISHRA S P, et al. Friction characteristics of microtextured surfaces under mixed and hydrodynamic lubrication[J]. Tribology International, 2013, 57(4):170-176.
[6] YAO C F, JIN Q C, HUANG X C, et al. Research on surface integrity of grinding Inconel 718[J]. The International Journal of Advanced Manufacturing Technology, 2013, 65(5):1019-1030.
[7] 项欣, 陈平, 李俊玲,等. 圆凹坑织构对线接触摩擦副摩擦学性能的影响[J]. 中国表面工程, 2015, 28(4):33-38. XIANG Xin, CHEN Ping, LI Jun-ling, et al. Influence of dimple texture on tribological behavior of line contact friction pairs[J]. China Surface Engineering, 2015, 28(4):33-38.
[8] LIU W, DENG Z, SHANG Y, et al. Effects of grinding parameters on surface quality in silicon nitride grinding[J]. Ceramics International, 2016, 43(1):1571-1577.
[9] ALI F, KANETA M, KRUPKA I, et al. Experimental and numerical investigation on the behavior of transverse limited micro-grooves in EHL point contacts[J]. Tribology International, 2015, 84(Suppl.1):81-89.
[10] 万轶, 李建亮, 熊党生. 滑动速度对织构化表面润滑状态的影响[J]. 中南大学学报:自然科学版, 2015, 46(12):4442-4447. WAN Yi, LI Jian-liang, XIONG Dang-sheng. Influence of sliding velocity on lubrication state of surface texturing[J]. Journal of Central South University:Science and Technology, 2015(12):4442-4447.
[11] 冯付良. 轴向车铣表面粗糙度的研究[D]. 上海:上海交通大学, 2008. FENG Fu-liang. Research on the nexus of influence element for turn milling machined surface roughness[D]. Shanghai:Shanghai Jiao Tong University, 2008.
[12] BENARDOS P G, VOSNIAKOS G C. Predicting surface roughness in machining:a review[J]. International Journal of Machine Tools and Manufacture, 2003, 43(8):833-844.
[13] EL-SONBATY I A, KHASHABA U A, SELMY A I, et al. Prediction of surface roughness profiles for milled surfaces using an artificial neural network and fractal geometry approach[J]. Journal of Materials Processing Technology, 2008, 200(1):271-278.
[14] LIU Y, WARKENTIN A, BAUER R, et al. Investigation of different grain shapes and dressing to predict surface roughness in grinding using kinematic simulations[J]. Precision Engineering, 2013, 37(3):758-764.
[15] ZHANG N, SHETTY D. An effective LS-SVM based approach for surface roughness prediction in machined surfaces[J]. Neurocomputing, 2016, 198(C):35-39.
[16] KARKALOS N E, GALANIS N I, MARKOPOULOS A P. Surface roughness prediction for the milling of Ti-6Al-4V ELI alloy with the use of statistical and soft computing techniques[J]. Measurement, 2016, 90:25-35.
[17] EZUGWU E O, FADARE D A, BONNEY J, et al. Modelling the correlation between cutting and process parameters in high-speed machining of Inconel 718 alloy using an artificial neural network[J]. International Journal of Machine Tools and Manufacture, 2005, 45(12/13):1375-1385.
[18] ÖZEL T, KARPAT Y. Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks[J]. International Journal of Machine Tools and Manufacture, 2005, 45(4):467-479.
[19] HANIEF M, WANI M F. Modeling and prediction of surface roughness for running-in wear using Gauss-Newton algorithm and ANN[J]. Applied Surface Science, 2015, 357(1):1573-1577.
[20] ÇAYDA ÇU, HASÇALIK A. A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method[J]. Journal of Materials Processing Technology, 2008, 202(1-3):574-582.
[21] MIA M, DHAR N R. Prediction of surface roughness in hard turning under high pressure coolant using Artificial Neural Network[J]. Measurement, 2016, 92:464-474.
[22] 傅荟璇. MATLAB:神经网络应用设计[M]. 北京:机械工业出版社, 2010:153-173.
[23] 温诗铸, 黄平. 摩擦学原理[M]. 北京:清华大学出版社, 2012:147-151.
[24] SARAVANAKUMAR P, MANESH K K, SINGAPERUMAL M, et al. Modelling of fluid continuum considering 3D surface parameters in hydraulic assemblies[J]. Precision Engineering, 2009, 33(1):99-106.
[25] ZHAO B, ZHANG S, LI J, et al. Friction characteristics of sliding guideway material considering original surface functional parameters under hydrodynamic lubrication[J]. ARCHIVE Proceedings of the Institution of Mechanical Engineers, Part J:Journal of Engineering Tribology, 2016, 231(7):813-825.
[1] WANG Fei-fei, XU Ying-qiang, LIU Kai-an. Probability analysis method of tangential contact stiffness on rough surfaces[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2018, 52(2): 255-260.
[2] LIU Wen, YANG Yun, LIN Teng-jiao, ZHANG Jin-hong. Method of calculating friction excitation of helical gear with geometric eccentricity[J]. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2017, 51(8): 1559-1567.