采用向量场平滑的磨削质量模型修正方法
|
摘要
磨削过程复杂,影响因素众多,因此易产生过程波动,进而影响产品的加工质量。针对该问题,以磨削工艺参数为研究对象,提出了一种基于向量场平滑算法(VFS)的磨削模型修正方法。首先提出基于改进型马氏距离的磨削参数关联性分析方法,采用多元回归分析方法构建了磨削质量预测模型,引入参数灵敏度函数表征不同参数对于磨削质量的影响程度,提升了分析方法的准确性。然后依据参数关联性特征,采用VFS算法给出了磨削工艺参数修正方法,并基于新的工艺参数进行质量模型修正与参数规划。最后基于机器人磨削平台进行了砂带磨削实验。结果表明:采用质量修正模型后的工艺参数可以较好满足磨削质量要求,从而验证了本文方法的有效性。
The grinding process is complex and has many influencing factors, which is easy to produce process fluctuations and affects the product quality. Aiming at the above-mentioned problem, a grinding modified-model based on the vector field smoothing algorithm(VFS) is proposed. Firstly, the correlation analysis method of grinding parameters based on the improved-mahalanobis distance is proposed. The multi-regression analysis method is used to construct the prediction model for grinding quality and the parameter sensitivity function is introduced to characterize the influence degree of the different parameters on the grinding quality, which can improve the accuracy of the method. Secondly, according to the correlation characteristics of parameters, the modified method of grinding process parameters is given based on the VFS algorithm, and the modified quality model and parameter planning can be carried out based on the new process parameters. Finally, the abrasive belt grinding experiments were carried out based on the robotic grinding platform. The results show that the process parameters based on the modified quality model can meet the quality requirements, which verified the effectiveness of the method.
The grinding process is complex and has many influencing factors, which is easy to produce process fluctuations and affects the product quality. Aiming at the above-mentioned problem, a grinding modified-model based on the vector field smoothing algorithm(VFS) is proposed. Firstly, the correlation analysis method of grinding parameters based on the improved-mahalanobis distance is proposed. The multi-regression analysis method is used to construct the prediction model for grinding quality and the parameter sensitivity function is introduced to characterize the influence degree of the different parameters on the grinding quality, which can improve the accuracy of the method. Secondly, according to the correlation characteristics of parameters, the modified method of grinding process parameters is given based on the VFS algorithm, and the modified quality model and parameter planning can be carried out based on the new process parameters. Finally, the abrasive belt grinding experiments were carried out based on the robotic grinding platform. The results show that the process parameters based on the modified quality model can meet the quality requirements, which verified the effectiveness of the method.
引文
[1] Lim S H,Kim J H,Lee C M.Development of CAD/CAM system and profile measuring device for CNC grinding machine to obtain an optimal grinding speed[J].Journal of Central South University of Technology,2011,18(1):146-152
[2] Oliveira J F G,Silva E J,Guo C,et al.Industrial challenges in grinding[J].CIRP Annals,2009,58(2):663-680
[3] 杨长勇,徐九华,顾珅珅,等.陶瓷CBN砂轮磨削镍基铸造高温合金K418磨削力研究[J].机械科学与技术,2014,33(5):657-661Yang C Y,Xu J H,Gu K K,et al.Investigation on the grinding force of the casted nickel-based superalloy K418 with a vitrified CBN wheel[J].Mechanical Science and Technology for Aerospace Engineering,2014,33(5):657-661 (in Chinese)
[4] Zhao T,Shi Y Y,Lin X J,et al.Surface roughness prediction and parameters optimization in grinding and polishing process for IBR of aero-engine[J].The International Journal of Advanced Manufacturing Technology,2014,74(5-8):653-663
[5] Ren X,Cabaravdic M,Zhang X,et al.A local process model for simulation of robotic belt grinding[J].International Journal of Machine Tools and Manufacture,2007,47(6):962-970
[6] Axinte D A,Kritmanorot M,Axinte M,et al.Investigations on belt polishing of heat-resistant titanium alloys[J].Journal of Materials Processing Technology,2005,166(3):398-404
[7] 赵阳,曲兴华,李睿.基于模糊算法的自调整温度控制系统[J].天津大学学报,2011,44(1):73-78Zhao Y,Qu X H,Li R.Self-adjusting control system of temperature based on fuzzy algorithm[J].Journal of Tianjin University,2011,44(1):73-78 (in Chinese)
[8] 刘贵杰,巩亚东,王宛山.磨削加工参数智能化在线调整方法研究[J].中国机械工程,2003,14(15):1268-1271Liu G J,Gong Y D,Wang W S.Study on intelligent on-line adjusting method for grinding conditions[J].China Mechanical Engineering,2003,14(15):1268-1271 (in Chinese)
[9] Song Y X,Liang W,Yang Y.A method for grinding removal control of a robot belt grinding system[J].Journal of Intelligent Manufacturing,2012,23(5):1903-1913
[10] 周雒维,管春,卢伟国.多标签分类法在电能质量复合扰动分类中的应用[J].中国电机工程学报,2011,31(4):45-50Zhou L W,Guan C,Lu W G.Application of multi-label classification method to catagorization of multiple power quality disturbances[J].Proceedings of the CSEE,2011,31(4):45-50 (in Chinese)
[11] 刘思峰,李庆胜,赵妮.灰色犹豫模糊集的核与灰度的灰关联决策方法[J].南京航空航天大学学报,2016,48(5):683-688Liu S F,Li Q S,Zhao N.Grey correlation decision-making method of grey hesitant fuzzy sets based on kernel and grey degree[J].Journal of Nanjing University of Aeronautics & Astronautics,2016,48(5):683-688 (in Chinese)
[12] Manjula M,Mishra S,Sarma A V R S.Empirical mode decomposition with Hilbert transform for classification of voltage sag causes using probabilistic neural network[J].International Journal of Electrical Power & Energy Systems,2013,44(1):597-603
[13] 祖志文,李秦.关于马氏距离模糊聚类的有效性指标研究[J].陕西理工大学学报:自然科学版,2018,34(2):33-38Zu Z W,Li Q.Research on validity index of Mahalanobis distance fuzzy[J].Journal of Shaanxi University of Technology:Natural Science Edition,2018,34(2):33-38 (in Chinese)
[14] 杨宇航,黄云,王亚杰,等.GH4169高温镍基合金砂带磨削工艺试验分析[J].机械科学与技术,2015,34(9):1365-1369Yang Y H,Huang Y,Wang Y J,et al.Analysis on abrasive belt grinding process test of GH4169 nickel based superalloy[J].Mechanical Science and Technology for Aerospace Engineering,2015,34(9):1365-1369 (in Chinese)
[15] Takahashi J,Sagayama S.Vector-field-smoothed Bayesian learning for incremental speaker adaptation[C]//1995 International Conference on Acoustics,Speech,and Signal Processing.Detroit,MI,USA,USA:IEEE,1995,1:696-699
[2] Oliveira J F G,Silva E J,Guo C,et al.Industrial challenges in grinding[J].CIRP Annals,2009,58(2):663-680
[3] 杨长勇,徐九华,顾珅珅,等.陶瓷CBN砂轮磨削镍基铸造高温合金K418磨削力研究[J].机械科学与技术,2014,33(5):657-661Yang C Y,Xu J H,Gu K K,et al.Investigation on the grinding force of the casted nickel-based superalloy K418 with a vitrified CBN wheel[J].Mechanical Science and Technology for Aerospace Engineering,2014,33(5):657-661 (in Chinese)
[4] Zhao T,Shi Y Y,Lin X J,et al.Surface roughness prediction and parameters optimization in grinding and polishing process for IBR of aero-engine[J].The International Journal of Advanced Manufacturing Technology,2014,74(5-8):653-663
[5] Ren X,Cabaravdic M,Zhang X,et al.A local process model for simulation of robotic belt grinding[J].International Journal of Machine Tools and Manufacture,2007,47(6):962-970
[6] Axinte D A,Kritmanorot M,Axinte M,et al.Investigations on belt polishing of heat-resistant titanium alloys[J].Journal of Materials Processing Technology,2005,166(3):398-404
[7] 赵阳,曲兴华,李睿.基于模糊算法的自调整温度控制系统[J].天津大学学报,2011,44(1):73-78Zhao Y,Qu X H,Li R.Self-adjusting control system of temperature based on fuzzy algorithm[J].Journal of Tianjin University,2011,44(1):73-78 (in Chinese)
[8] 刘贵杰,巩亚东,王宛山.磨削加工参数智能化在线调整方法研究[J].中国机械工程,2003,14(15):1268-1271Liu G J,Gong Y D,Wang W S.Study on intelligent on-line adjusting method for grinding conditions[J].China Mechanical Engineering,2003,14(15):1268-1271 (in Chinese)
[9] Song Y X,Liang W,Yang Y.A method for grinding removal control of a robot belt grinding system[J].Journal of Intelligent Manufacturing,2012,23(5):1903-1913
[10] 周雒维,管春,卢伟国.多标签分类法在电能质量复合扰动分类中的应用[J].中国电机工程学报,2011,31(4):45-50Zhou L W,Guan C,Lu W G.Application of multi-label classification method to catagorization of multiple power quality disturbances[J].Proceedings of the CSEE,2011,31(4):45-50 (in Chinese)
[11] 刘思峰,李庆胜,赵妮.灰色犹豫模糊集的核与灰度的灰关联决策方法[J].南京航空航天大学学报,2016,48(5):683-688Liu S F,Li Q S,Zhao N.Grey correlation decision-making method of grey hesitant fuzzy sets based on kernel and grey degree[J].Journal of Nanjing University of Aeronautics & Astronautics,2016,48(5):683-688 (in Chinese)
[12] Manjula M,Mishra S,Sarma A V R S.Empirical mode decomposition with Hilbert transform for classification of voltage sag causes using probabilistic neural network[J].International Journal of Electrical Power & Energy Systems,2013,44(1):597-603
[13] 祖志文,李秦.关于马氏距离模糊聚类的有效性指标研究[J].陕西理工大学学报:自然科学版,2018,34(2):33-38Zu Z W,Li Q.Research on validity index of Mahalanobis distance fuzzy[J].Journal of Shaanxi University of Technology:Natural Science Edition,2018,34(2):33-38 (in Chinese)
[14] 杨宇航,黄云,王亚杰,等.GH4169高温镍基合金砂带磨削工艺试验分析[J].机械科学与技术,2015,34(9):1365-1369Yang Y H,Huang Y,Wang Y J,et al.Analysis on abrasive belt grinding process test of GH4169 nickel based superalloy[J].Mechanical Science and Technology for Aerospace Engineering,2015,34(9):1365-1369 (in Chinese)
[15] Takahashi J,Sagayama S.Vector-field-smoothed Bayesian learning for incremental speaker adaptation[C]//1995 International Conference on Acoustics,Speech,and Signal Processing.Detroit,MI,USA,USA:IEEE,1995,1:696-699