基于模糊逻辑推理的多响应稳健参数优化
|
摘要
研究一种基于模糊逻辑推理的多响应稳健参数优化方法。利用满意度函数计算含噪声干扰过程的各个响应的满意度值,然后经过模糊逻辑推理,将多个响应的满意度值转化为模糊推理等级;进而根据主效应法选取最优参数组合。构建包含可控因素和噪声因素的神经网络预测模型,对最优参数组合处的噪声实验进行预测,得到优化参数的响应质量指标值;并且计算出优化参数的信噪比。将该方法运用于铣切削工艺过程多响应参数稳健优化。结果表明:基于模糊逻辑推理的多响应稳健参数优化方法得到的最优参数组合,不仅有效地降低了铣切削表面的粗糙程度和粗糙高度,而且对噪声因素的影响具有较好的稳健性。
A robust parameter optimization method for multi-responses was investigated based on fuzzy logic inference. The desirability value of each responses with noise interference is calculated by desirability function method. Then using the fuzzy logic inference to convert the desirability values of multi-responses into a fuzzy reasoning grade,and the optimal parameter combination is selected according to the main effect chart. A neural network prediction model with controllable factors and noise factors is constructed to predict the optimal parameter combination under the noise interference. The response quality index of the optimized parameters is obtained,and the signal to noise rate( SNG) of the optimized parameters is calculated. This method is applied to optimize the end milling process for multi-responses. The results show that the optimal parameter combination of multi-response robust parameter optimization based on fuzzy logic inference not only effectively reduces the surface roughness and height roughness,but also the results have a good robustness under the noise interference.
A robust parameter optimization method for multi-responses was investigated based on fuzzy logic inference. The desirability value of each responses with noise interference is calculated by desirability function method. Then using the fuzzy logic inference to convert the desirability values of multi-responses into a fuzzy reasoning grade,and the optimal parameter combination is selected according to the main effect chart. A neural network prediction model with controllable factors and noise factors is constructed to predict the optimal parameter combination under the noise interference. The response quality index of the optimized parameters is obtained,and the signal to noise rate( SNG) of the optimized parameters is calculated. This method is applied to optimize the end milling process for multi-responses. The results show that the optimal parameter combination of multi-response robust parameter optimization based on fuzzy logic inference not only effectively reduces the surface roughness and height roughness,but also the results have a good robustness under the noise interference.
引文
1欧阳林寒,马义中,汪建均,等.基于模型不确定性的响应曲面建模.系统工程与电子技术,2015;37(8):1818-1824Ouyang Hanlin,Ma Yizhong,Wang Jianjun,et al.Response surface modeling based on model uncertainty.Systems Engineering and Electronics,2015;37(8):1818-1824
2顾晓光,马义中,汪建均,等.多元质量特性的满意参数设计.控制与决策,2014;29(6):1064-1070Gu Xiaoguang,Ma Yizhong,Wang Jianjun,et al.Satisfactory parameter design for multivariate quality characteristics.Control and Decision,2014;29(6):1064-1070
3杨文焕,王超慧,李卫平,等.响应面法优化紫外激活过硫酸盐降解莠灭净.科学技术与工程,2016;16(34):238-243Yang Wenhuan,Wang Chaohui,Li Weiping,et al.Degradation of ametryn by UV activated persulfate:optimization by response surface methodology.Science Technology and Engineering,2016;16(34):238-243
4 Osunkanmibi O B,Owolabi T O,Betiku E.Comparison of artificial neural network and response surface methodology performance on fermentation parameters optimization of bioconversion of cashew apple juice to gluconic acid.International Journal of Food Engineering,2015;11(3):393-403
5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.运筹与管理,2015;24(4):83-91Liu Yumin,Zhao Lixiao.A robust optimization model for multi-responses based on desirability function approach and the empirical study.Operations Research and Management Science,2015;24(4):83-91
6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.工业工程,2016;19(1):74-80Xu Jing,He Zhen,Chen Zhezhi,et al.A study of optimizing functional response problems based on principal component analysis.Industrial Engineering Journal,2016;19(1):74-80
7崔建勋,安实.基于模糊逻辑的驾驶员路线选择行为分析.科学技术与工程,2010;10(4):1077-1082Cui Jianxun,An Shi.A study of driver’s route choice behavior based on fuzzy logic method.Science Technology and Engineering,2010;10(4):1077-1082
8 Lee H J,Jin B P,Chen G.Robust fuzzy control of nonlinear systems with parametric uncertainties.IEEE Transactions on Fuzzy Systems,2001;9(2):369-379
9潘正华.模糊推理算法的数学原理.计算机研究与发展,2008;45(A1):165-168Pan Zhenghua.Mathematical principle of the algorithms of fuzzy reasoning.Journal of Computer Research and Development,2008;45(A1):165-168
10白羽,杨帆.基于模糊及遗传算法的电火花加工参数优化研究.机械制造,2016;54(1):64-67Bai Yu,Yang Fan.Edm parameters optimization based on fuzzy and genetic algorithm.Machinery Manufacturing,2016;54(1):64-67
11岳睿,王忠宾,彭安华.基于模糊推理的熔融堆积成型工艺参数的优化.煤矿机械,2012;33(12):125-127Yue Rui,Wang Zhongbin,Peng Anhua.Process-parameter optimization in fused deposition modeling based on fuzzy inference.Coal Mine Machinery,2012;33(12):125-127
12吕永锋.基于模糊推理的薄壁类零件注射模工艺优化设计.机械设计与研究,2013;29(3):77-79LüYongfeng.Optimal design of the injection molding process parameters of thin-walled parts based on fuzzy reasoning.Journal of Machine Design and Research,2013;29(3):77-79
13 Satheesh M,Edwin J,Raja D.Multi-objective optimization of weld parameters of boiler steel using fuzzy based desirability function.Journal of Engineering Science and Technology Review,2014;7(1):29-36
14 Kumar V,Sharma H K,Singh K,et al.Optimization of process parameters for the production of taro chips using RSM with fuzzy modeling.Journal of Food Measurement and Characterization,2015;9(3):400-413
15 Soleymani P,Mousavi S M,Vahdani B,et al.A new fuzzy multiobjective optimisation method with desirability function under uncertainty.Applied Decision Science,2016;9(1):100-120
16 Trautmann H,Weihs C.On the distribution of the desirability index using Harrington’s desirability function.Metrika,2006;63(2):207-213
17 Brito T G,Paiva A P,Ferreira J R,et al.A normal boundary intersection approach to multiresponse robust optimization of the surface roughness in end milling process with combined arrays.Precision Engineering,2014;38(3):628-638
18侯连浪,梁利喜,刘向君,等.基于BP神经网络的页岩静弹性模量预测研究.科学技术与工程,2016;16(30):176-180Hou Lianlang,Liang Lixi,Liu Xiangjun,et al.Prediction of shale static elastic modulus based on BP neural network.Science Technology and Engineering,2016;16(30):176-180
2顾晓光,马义中,汪建均,等.多元质量特性的满意参数设计.控制与决策,2014;29(6):1064-1070Gu Xiaoguang,Ma Yizhong,Wang Jianjun,et al.Satisfactory parameter design for multivariate quality characteristics.Control and Decision,2014;29(6):1064-1070
3杨文焕,王超慧,李卫平,等.响应面法优化紫外激活过硫酸盐降解莠灭净.科学技术与工程,2016;16(34):238-243Yang Wenhuan,Wang Chaohui,Li Weiping,et al.Degradation of ametryn by UV activated persulfate:optimization by response surface methodology.Science Technology and Engineering,2016;16(34):238-243
4 Osunkanmibi O B,Owolabi T O,Betiku E.Comparison of artificial neural network and response surface methodology performance on fermentation parameters optimization of bioconversion of cashew apple juice to gluconic acid.International Journal of Food Engineering,2015;11(3):393-403
5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.运筹与管理,2015;24(4):83-91Liu Yumin,Zhao Lixiao.A robust optimization model for multi-responses based on desirability function approach and the empirical study.Operations Research and Management Science,2015;24(4):83-91
6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.工业工程,2016;19(1):74-80Xu Jing,He Zhen,Chen Zhezhi,et al.A study of optimizing functional response problems based on principal component analysis.Industrial Engineering Journal,2016;19(1):74-80
7崔建勋,安实.基于模糊逻辑的驾驶员路线选择行为分析.科学技术与工程,2010;10(4):1077-1082Cui Jianxun,An Shi.A study of driver’s route choice behavior based on fuzzy logic method.Science Technology and Engineering,2010;10(4):1077-1082
8 Lee H J,Jin B P,Chen G.Robust fuzzy control of nonlinear systems with parametric uncertainties.IEEE Transactions on Fuzzy Systems,2001;9(2):369-379
9潘正华.模糊推理算法的数学原理.计算机研究与发展,2008;45(A1):165-168Pan Zhenghua.Mathematical principle of the algorithms of fuzzy reasoning.Journal of Computer Research and Development,2008;45(A1):165-168
10白羽,杨帆.基于模糊及遗传算法的电火花加工参数优化研究.机械制造,2016;54(1):64-67Bai Yu,Yang Fan.Edm parameters optimization based on fuzzy and genetic algorithm.Machinery Manufacturing,2016;54(1):64-67
11岳睿,王忠宾,彭安华.基于模糊推理的熔融堆积成型工艺参数的优化.煤矿机械,2012;33(12):125-127Yue Rui,Wang Zhongbin,Peng Anhua.Process-parameter optimization in fused deposition modeling based on fuzzy inference.Coal Mine Machinery,2012;33(12):125-127
12吕永锋.基于模糊推理的薄壁类零件注射模工艺优化设计.机械设计与研究,2013;29(3):77-79LüYongfeng.Optimal design of the injection molding process parameters of thin-walled parts based on fuzzy reasoning.Journal of Machine Design and Research,2013;29(3):77-79
13 Satheesh M,Edwin J,Raja D.Multi-objective optimization of weld parameters of boiler steel using fuzzy based desirability function.Journal of Engineering Science and Technology Review,2014;7(1):29-36
14 Kumar V,Sharma H K,Singh K,et al.Optimization of process parameters for the production of taro chips using RSM with fuzzy modeling.Journal of Food Measurement and Characterization,2015;9(3):400-413
15 Soleymani P,Mousavi S M,Vahdani B,et al.A new fuzzy multiobjective optimisation method with desirability function under uncertainty.Applied Decision Science,2016;9(1):100-120
16 Trautmann H,Weihs C.On the distribution of the desirability index using Harrington’s desirability function.Metrika,2006;63(2):207-213
17 Brito T G,Paiva A P,Ferreira J R,et al.A normal boundary intersection approach to multiresponse robust optimization of the surface roughness in end milling process with combined arrays.Precision Engineering,2014;38(3):628-638
18侯连浪,梁利喜,刘向君,等.基于BP神经网络的页岩静弹性模量预测研究.科学技术与工程,2016;16(30):176-180Hou Lianlang,Liang Lixi,Liu Xiangjun,et al.Prediction of shale static elastic modulus based on BP neural network.Science Technology and Engineering,2016;16(30):176-180