| 基于犹豫模糊决策的铣削参数优化 |
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| 引用本文： | 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. |
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| 作者姓名： | 禹建丽 谷丰盈 陈洪根 |
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| 作者单位： | 郑州航空工业管理学院管理工程学院,河南郑州,450046;郑州航空工业管理学院管理工程学院,河南郑州,450046;郑州航空工业管理学院管理工程学院,河南郑州,450046 |
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| 基金项目： | 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） |
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| 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Ra和Rt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。
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| 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 |
| 收稿时间： | 2018-10-30 |
Optimization of Milling Parameters Based on Hesitant Fuzzy Decision |
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| YU Jianli,GU Fengying,CHEN Honggen.Optimization of Milling Parameters Based on Hesitant Fuzzy Decision[J].Industrial Engineering Journal,2019,22(3):93-99. |
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| Authors: | YU Jianli GU Fengying CHEN Honggen |
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| Affiliation: | School of Management Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450046, China |
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| 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 Ra and Rt 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. |
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| Keywords: | CNC milling process hesitation Euclidean distance fuzzy logic inference multi-response parameter optimization |
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