基于犹豫模糊决策的铣削参数优化

doi:10.3969/j.issn.1007-7375.2019.03.012

工业工程 ›› 2019, Vol. 22 ›› Issue (3): 93-99.doi: 10.3969/j.issn.1007-7375.2019.03.012

基于犹豫模糊决策的铣削参数优化

禹建丽, 谷丰盈, 陈洪根

郑州航空工业管理学院管理工程学院, 河南郑州 450046

收稿日期:2018-10-30 出版日期:2019-06-30 发布日期:2019-06-27
作者简介:禹建丽(1960-),女,河南省人,教授,博士,主要研究方向为工业工程与质量管理
基金资助:
国家自然科学基金资助项目（U1404702）；航空科学基金资助项目（2017ZG55029）；河南省科技攻关资助项目（182102210107）；郑州航空工业管理学院研究生教育创新计划资助项目（2018CX17）

Optimization of Milling Parameters Based on Hesitant Fuzzy Decision

YU Jianli, GU Fengying, CHEN Honggen

School of Management Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450046, China

Received:2018-10-30 Online:2019-06-30 Published:2019-06-27

摘要/Abstract

摘要： 为提升铣削加工质量，研究一种基于犹豫模糊决策的数控铣削参数优化方法。根据铣削过程机理和实验数据建立铣削参数优化的数学模型，将犹豫欧氏距离与模糊逻辑推理相结合，对铣削过程中多响应系统进行简化，既避免了传统模糊测度方法中权重的设置，也充分提取了各响应之间相关性的有效信息，最后通过对实验中可控因子与模糊推理过程输出值进行主效应分析，得到铣削过程控制因子的最佳参数组合：当进给量为0.01 mm/tooth，铣削深度为0.064 mm、铣削速度达到396 m/min，铣削宽度达到12.26 mm时，加工零件的表面粗糙度R_a和R_t可以得到整体优化，从而提升加工零件的质量。该方法首次将犹豫模糊决策理论方法应用于铣削过程工艺参数优化，避免了均值处理法带来的信息损失，可增加实验设计的鲁棒性。与满意度函数法相比，研究的基于犹豫模糊决策的铣削参数优化方法不受权重大小制约，能够同时使过程的两个响应得到优化，具有实用的有效性和可操作性。

关键词: 铣削过程, 犹豫欧氏距离, 模糊逻辑推理, 多响应参数优化

Abstract: To optimize the quality of milling process, a milling parameter optimization method based on hesitant fuzzy decision making is studied. According to the mechanism of milling cutting process, the controllable factors are introduced into the experiment firstly. The mathematical optimization model of milling parameter is established based on experimental data. Then it combines the hesitant Euclidean distance with fuzzy logic inference to simplify the multi-response system in the milling process. The above process avoids the setting of right weights in the traditional fuzzy measure method and extracts the effective information of the correlation of the response at the same time. Finally, the most suitable combination of parameters is obtained through the main effect analysis among the controllable factors and the output value of the fuzzy inference process:when the feed speed is 0.01 mm/tooth, the cutting depth is 0.064 mm, the cutting speed reaches 396 m/min, and the cutting width reaches 12.26 mm, the surface roughness R_a and R_t of the machined components are optimized, which improves the quality of the machining parts. From the result, it is clear that the method of hesitant fuzzy decision theory is applied to the optimization of milling parameters for the first time. This application avoids the information loss caused by the mean processing method and can increase the robustness of experimental design. Compared with the desirability function method, the proposed milling parameter optimization method based on hesitant fuzzy decision is not restricted by the right weight and it can optimize the two responses of the process at the same time, which has practical effectiveness and reliability.

Key words: CNC milling process, hesitation Euclidean distance, fuzzy logic inference, multi-response parameter optimization

中图分类号:

TG506.1

禹建丽, 谷丰盈, 陈洪根. 基于犹豫模糊决策的铣削参数优化[J]. 工业工程, 2019, 22(3): 93-99.

YU Jianli, GU Fengying, CHEN Honggen. Optimization of Milling Parameters Based on Hesitant Fuzzy Decision[J]. Industrial Engineering Journal , 2019, 22(3): 93-99.

参考文献

[1] 王洁. 重型棒料铣削加工自动上下料系统研究[J]. 控制工程, 2018, 25(2):319-322 WANG Jie. Automatic loading and unloading system for heavy bar milling processing[J]. Control Engineering of China, 2018, 25(2):319-322
[2] 谢黎明, 张威, 靳岚. 6061铝合金高速铣削切削参数对表面粗糙程度的影响分析[J]. 机械设计与制造工程, 2018, 47(3):56-60 XIE Liming, ZHANG Wei, JIN Lan. The analysis on the influence of cutting parameters to surface roughness in the high speed milling 6061 aluminum alloy[J]. Machine Design and Manufacturing Engineering, 2018, 47(3):56-60
[3] 赵韩, 冯宝林, 董晓慧, 等. 基于改进的BP神经网络对切削参数的优化选择[J]. 机床与液压, 2008, 36(5):213-215, 226 ZHAO Han, FENG Baolin, Dong Xiaohui, et al. Optimization choice of cutting parameter based on improved BP neural network[J]. Machinetool & Hydraulics, 2008, 36(5):213-215, 226
[4] 苏晓云, 汪建新, 辛李霞. 基于神经网络的铣削大理石表面粗糙程度预测模型[J]. 表面技术, 2017, 46(8):274-279 SU Xiaoyun, WANG Jianxin, XIN Lixia. Neural network-based prediction model for surface roughness of milled marble[J]. Surface Technology, 2017, 46(8):274-279
[5] 李建广, 姚英学, 刘长清, 等. 基于遗传算法的车削用量优化研究[J]. 计算机集成制造系统, 2006, 12(10):1651-1656 LI Jianguang, YAO Yingxue, LIU Changqing, et al. Cutting parameters optimization in turning based on genetic algorithm[J]. Computer Integrated Manufacturing Systems, 2006, 12(10):1651-1656
[6] 刘海江, 黄炜. 基于粒子群算法的数控加工切削参数优化[J]. 同济大学学报(自然科学版), 2008, 36(6):803-806 LIU Haijiang, HUANG Wei. Computer numerical control machining parameter optimization based on particle swarm optimization[J]. Journal of Tongji University (Natural Science), 2008, 36(6):803-806
[7] 许静, 何桢, 陈喆芝, 等. 基于主成分分析的函数型产品质量特性的优化方法研究[J]. 工业工程, 2016, 19(1):74-80 XU Jing, HE Zhen, CHEN Zhezhi, et al. A study of optimizing functional response problems based on principal component analysis[J]. Industrial Engineering Journal, 2016, 19(1):74-80
[8] 刘玉敏, 赵利肖. 基于满意度函数的多响应稳健优化模型及实证研究[J]. 运筹与管理, 2015, 4(8):83-91 LIU Yumin, ZHAO Lixiao. A robust optimization model for multi-responses based on desirability function approach and the empirical study[J]. Operations Research and Management Science, 2015, 4(8):83-91
[9] 马义中, 汪建均, 苏国进. 动态多响应系统的稳健参数设计[J]. 系统工程理论与实践, 2012, 32(8):1841-1849 MA Yizhong, WANG Jianjun, SU Guojin. Robust parameter design for dynamic multi-response systems[J]. Systems Engineering-Theory & Practice, 2012, 32(8):1841-1849
[10] AMERI A A, POURGHASEMI H R, CERDA A. Erodibility prioritization of sub-watersheds using morphometric parameters analysis and its mapping:A comparison among TOPSIS, VIKOR, SAW, and CF multi-criteria decision making models[J]. Science of the Total Environment, 2018, 613-614:1385-1400
[11] NATARAJAN U, HYACINTH SUGANTHI X, PERIYANAN P R. Modeling and multiresponse optimization of quality characteristics for the Micro-EDM drilling process[J]. Transactions of the Indian Institute of Metals, 2016, 69(9):1675-1686
[12] KUMARV, SHARMA H K, SINGH K, et al. Optimization of process parameters for the production of taro chips using RSM with fuzzy modeling[J]. Journal of Food Measurement and Characterization, 2015, 9(3):400-413
[13] 张迎冬, 何桢. 改进的主成分分析法在多响应优化中的应用[J]. 组合机床与自动化加工技术, 2012(11):97-100 ZHANG Yingdong, HE Zhen. Application of improved principal component analysis method in multi-response optimization[J]. Modular Machine Tool & Automatic Manufacture Technique, 2012(11):97-100
[14] ZADEH L A. Fuzzy sets[J]. Information & Control, 1965, 8(3):338-353
[15] FARHADINIA B. A series of score functions for hesitant fuzzy sets[J]. Information Sciences, 2014, 277(20):102-110
[16] LEE H J, JIN B P, CHEN G. Robust fuzzy control of nonlinear systems with parametric uncertainties[J]. IEEE Transactions on Fuzzy Systems, 2001, 9(2):369-379
[17] TORRA V. Hesitant fuzzy sets[J]. International Journal of Intelligent Systems, 2010, 25(6):529-539
[18] 潘笑天, 禹建丽, 张黎. 基于模糊逻辑推理的铣切削过程稳健参数优化[J]. 河南科学, 2017, 35(10):1633-1640 PAN Xiaotian, YU Jianli, ZHANG Li. The robust optimization for multiple responses in end milling process based on fuzzy logic inference[J]. Henan Science, 2017, 35(10):1633-1640
[19] 刘小弟, 朱建军, 张世涛, 等. 考虑属性权重优化的犹豫模糊多属性决策方法[J]. 控制与决策, 2016, 31(2):297-302 LIU Xiaodi, ZHU Jianjun, ZHANG Shitao, et al. Hesitant fuzzy multiple attribute decision making method based on optimization of attribute weights[J]. Control and Decision, 2016, 31(2):297-302
[20] 许永平, 朱延广, 杨峰, 等. 基于ANP和模糊积分的多准则决策方法及其应用[J]. 系统工程理论与实践, 2010, 30(6):1099-1105 XU Yongping, ZHU Yanguang, YANG Feng, et al. New multi criteria decision making approach based on ANP and fuzzy integral and its application[J]. Systems Engineering -Theory & Practice, 2010, 30(6):1099-1105
[21] MAMDANI E H. Advances in the linguistic synthesis of fuzzy controllers[J]. International Journal of Man-Machine Studies, 1976, 8(6):669-678

基于犹豫模糊决策的铣削参数优化

Optimization of Milling Parameters Based on Hesitant Fuzzy Decision

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