Intelligent Decision System for Grinding Process of Typical Parts

doi:10.3901/JME.2023.12.126

Abstract

Abstract: Grinding is the final processing method of most typical parts, and the intelligent decision-making of typical parts grinding process is an important means to satisfy its high precision and efficiency. Hence, a framework of intelligent process decision-making system for typical parts grinding based on 6R model is present. Firstly, the comprehensive confidence factor of each case is calculated based on the case-based reasoning and the confidence degree to carry out case retrieval. Then, a hybrid prediction model consisted of heterogeneous integration learning and PSO algorithm is established to obtain the optimal process plan output. Finally, based on Qt 4.8.7 and SQLite 3, a typical part grinding process decision-making system is developed. Taking the grinding of the grinder wheel spindle as an example, after using the proposed grinding process decision-making system, the surface quality of the parts is improved by 73.25%, the grinding process plan decision-making time is shortened from nearly 20 hours to 2-4 hours, and the processing time is shortened from 10 minutes to 5 minutes. The machining efficiency of single product is increased by more than 25%, up to 47%, and the decision-making accuracy rate reaches 98.2%, realizing high-efficiency and high-precision grinding of typical parts.

Key words: grinding, process intelligent decision-making, three-way decisions theory, heterogeneous integrated learning

CLC Number:

TG156

DENG Zhaohui, LI Zhongyang, GE Jimin, LIU Tao. Intelligent Decision System for Grinding Process of Typical Parts[J]. Journal of Mechanical Engineering, 2023, 59(12): 126-138.

References

[1] WEGENER K,BLEICHER F,KRAJNIK P,et al. Recent developments in grinding machines[J]. CIRP Annals,2017,66(2):779-802.
[2] LV L S,DENG Z H,LIU T,et al. Intelligent technology in grinding process driven by data:A review[J]. Journal of Manufacturing Processes,2020,58:1039-1051.
[3] 刘涛,邓朝晖,葛智光,等. 面向凸轮轴磨削加工的智能决策云服务实现[J]. 中国机械工程,2020,31(7):773-780. LIU Tao,DENG Zhaohui,GE Zhiguang,et al. Implementation of intelligent decision cloud service for camshaft grinding processes[J]. China Mechanical Engineering,2020,31(7):773-780.
[4] 赵香港,郝秀清,岳彩旭,等. 黑色金属的金刚石刀具加工技术研究进展[J]. 中国表面工程,2022,35(1):34-52. ZHAO Xianggang,HAO Xiuqing,YUE Caixu,et al. Research progress of diamond tool machining technology for ferrous metals[J]. China Surface Engineering,2022,35(1):34-52.
[5] 刘文浩,陈燕,王杰,等. SLM成型零件型腔内表面电解辅助磁粒研磨加工研究[J]. 中国表面工程,2021,34(3):100-109. LIU Wenhao,CHEN Yan,WANG Jie,et al. Study on electrolysis assisted magnetic abrasive finishing of SLM parts cavity surface[J]. China Surface Engineering,2021,34(3):100-109.
[6] 周云,何玉洋,杨泽檀,等. 复合织构刀具切削铝合金的性能[J]. 中国表面工程,2022,35(3):281-288. ZHOU Yun,HE Yuyang,YANG Zetan,et al. Performance of hybrid textured tools in cutting aluminum alloy[J]. China Surface Engineering,2022,35(3):281-288.
[7] ZHANG X H,DENG Z H,LIU W,et al. Combining rough set and case based reasoning for process conditions selection in camshaft grinding[J]. Journal of Intelligent Manufacturing,2013,24(2):211-224.
[8] DENG Z H,ZHANG X H,LIU W,et al. A hybrid model using genetic algorithm and neural network for process parameters optimization in NC camshaft grinding[J]. The International Journal of Advanced Manufacturing Technology,2009,45(9):859.
[9] XIANG K,LIU Z Q,XING A I. Case modifying of high-speed cutting database based on CSP and similarity theory[J]. Frontiers of Mechanical Engineering in China,2008,4(1):83-87.
[10] XIANG K J,LIU Z Q,AI X. The research for data acquisition technology in high-speed cutting database[J]. Applied Mechanics and Materials,2014,3276(590):418-421.
[11] 赵凯,秦闯,刘战强. 汽车零配件工艺特征切削加工数据系统的开发[J]. 机床与液压,2015,43(23):109-112. ZHAO Kai,QIN Chuang,LIU Zhanqiang. Development of cutting data system for automobile parts based on processing features[J]. Machine Tool & Hydraulics,2015,43(23):109-112.
[12] JIANG Z,JIANG Y,WANG Y,et al. A hybrid approach of rough set and case-based reasoning to remanufacturing process planning[J]. Journal of Intelligent Manufacturing,2019,30(1):19-32.
[13] LI S,ZHANG H,YAN W,et al. A hybrid method of blockchain and case-based reasoning for remanufacturing process planning[J]. Journal of Intelligent Manufacturing,2021,32(5):1389-1399.
[14] 葛智光,邓朝晖,刘伟,等. 机床主轴智能磨削工艺软件的研究与开发[J]. 金刚石与磨料磨具工程,2018,38(4):72-76,82. GE Zhiguang,DENG Zhaohui,LIU Wei,et al. Research and development of intelligent grinding process software for machine tool spindle[J]. Diamond and Abrasives Engineering,2018,38(4):72-76,82.
[15] 刘献礼,孙庆贞,岳彩旭,等. 基于数据挖掘技术的钛合金铣削工艺参数优化[J]. 计算机集成制造系统,2022,28(8):2440-2448. LIU Xianli,SUN Qinzhen,YUE Caixu,et al. Optimization of milling process parameters of titanium alloy based on data mining technology[J]. Computer Integrated Manufacturing System,2022,28(8):2440-2448.
[16] 刘献礼,刘强,岳彩旭,等. 切削过程中的智能技术[J]. 机械工程学报,2018,54(16):45-61. LIU Xianli,LIU Qiang,YUE Caixu,et al. Intelligent technology in the cutting process[J]. Journal of Mechanical Engineering,2018,54(16):45-61.
[17] 刘海鸥,卢佳兴,彭建鑫,等. 基于决策树算法的AMT挂挡过程冗余控制研究[J]. 北京理工大学学报,2022,42(1):63-73. LIU Haiou,LU Jiaxing,PENG Jianxin,et al. Research on redundant control of amt gearshift process based on decision tree algorithm[J]. Journal of Beijing Institute of Technology,2022,42(1):63-73.
[18] 何定桥,王鹏军,杨军. 深度神经网络在EMD虚假分量识别中的应用[J]. 工程力学,2021,38(S1):195-201. HE Dingqiao,WANG Pengjun,YANG Jun. Application of deep neural network in EMD false component recognition[J]. Engineering Mechanics,2021,38(S1):195-201.
[19] YAO Y. Three-way decisions with probabilistic rough sets[J]. Information Sciences,2010,180(3):341-353.
[20] YAO Y. Three-way decision and granular computing[J]. International Journal of Approximate Reasoning,2018,103:107-123.
[21] 刘牧雷,徐菲菲. 基于三支决策的序列数据代价敏感分类算法[J]. 智能系统学报,2019,14(6):1255-1261. LIU Mulei,XU Feifei. A sequence data,cost-sensitive classification algorithm based on three-way decisions[J]. Journal of Intelligent Systems,2019,14(6):1255-1261.
[22] YAO Yiyu. Three way decision:an interpretation of rules in rough set theory[C]//Proceedings of Rough Sets and Knowledge Technology:4th International Conference,RSKT 2009,Gold Coast,Australia,2009:642-649.
[23] 薛占熬,朱泰隆,薛天宇,等. 基于三支决策理论的条件属性权重构造方法[J]. 计算机科学,2015,42(8):265-268,272. XUE Zhanao,ZHU Tailong,XUE Tianyu,et al. Conditional attribute weight construction method based on three-branch decision theory[J]. Computer Science,2015,42(8):265-268,272.
[24] SAATY T L. A scaling method for priorities in hierarchical structures[J]. Journal of Mathematical Psychology,1977,15(3):234-281.
[25] WANG W,ZHAN J,ZHANG C. Three-way decisions based multi-attribute decision making with probabilistic dominance relations[J]. Information Sciences,2021,559:75-96.