核模糊聚类和BP神经网络的切削工艺绿色度评价  

Green Degree Evaluation of Cutting Process with Kernel Fuzzy Clustering and BP Neural Network

作　　者：王宇钢[1] 修世超[2] WANG Yu-gang;XIU Shi-chao(School of Mechanical Engineering and Automation,Liaoning University of Technology,Liaoning Jinzhou 121000,China;School of Mechanical Engineering and Automation,Northeastern University,Liaoning Shenyang 110819,China)

机构地区：[1]辽宁工业大学机械工程与自动化学院,辽宁锦州121000 [2]东北大学机械工程与自动化学院,辽宁沈阳110819

出　　处：《机械设计与制造》2018年第11期41-44,共4页Machinery Design & Manufacture

基　　金：国家自然科学基金资助项目(51375083);辽宁省自然科学基金资助项目(20170540445)

摘　　要：为了对切削工艺绿色度进行客观有效评价,提出一种基于核模糊聚类算法和BP神经网络的工艺绿色度评价模型。根据面向绿色制造的工艺规划方法,建立切削工艺绿色度评价指标体系。通过核模糊聚类算法划分训练样本集,提升样本分类精度。利用BP神经网络建立工艺绿色度评价模型,基于训练结果自适应获取输入到输出的模糊规则,提高评价效率。通过汽车零件的切削工艺绿色度评价实例,验证了核模糊聚类和BP神经网络的切削工艺绿色度评价方法有效性。To evaluate the green degree of the cutting process effectively and objectively,a green degree evaluation model is proposed based on kernel fuzzy clustering and BP neural network.According to the process planning method for green manufacturing,green evaluation index system of the cutting process was established.Training sample set was constructed by the kernel fuzzy clustering algorithm,and the accuracy of sample classification was improved.The green degree evaluation model for the process was established using BP neural network.The fuzzy rules from input to output was obtained self-adaptively based on training results,and evaluation efficiency was increased.Green degree evaluation of the cutting process of auto parts was taken as example to verify the effectiveness of the evaluation method.

关 键 词：核模糊聚类 BP神经网络 切削工艺 绿色度 评价 

分 类 号：TH16[机械工程—机械制造及自动化] TH162