工程设计学报2017，Vol.24Issue(4)：449-458,10.DOI:10.3785/j.issn.1006-754X.2017.04.012
基于BP神经网络和FPA的高速干切滚齿工艺参数低碳优化决策
Low carbon optimization decision for high-speed dry hobbing process parameters based on BP neural networks and FPA
摘要
Abstract
Aiming at some problems of high subjective dependence and long time consuming in the process of high speed dry hobbing process parameters decision , a low carbon optimization decision method for high speed dry hobbing process parameters based on case-based reasoning (CBR) and optimization algorithms was proposed .At the same time ,it was a method to achieve the low carbon of the hobbing processing .At the beginning ,a back propagation (BP) neural net-works model was established based on the cases of high speed hobbing process ,which could pre-dict the machining effect evaluation of hobbing processing .In addition ,an improved K-means al-gorithm was used to obtain the similarity example extraction set for target process problem ,and several process solutions were obtained to construct process parameter constraints .Moreover ,the flower pollination algorithm (FPA ) was applied to search the optimal process parameters for tar -get process problems ,which took the minimum carbon consumption of the hobbing processing as the optimization objective .A high speed dry hobbing machine in an enterprise was used as an in -stance to verify the feasibility and effectiveness of proposed method .The experimental results in-dicate that the proposed optimization method is a very useful tool for achieving lower energy con-sumption and better processing effect .The method can also effectively avoid relying on process manuals ,personal experience or cutting experiments so as to improve decision efficiency .Moreo-ver ,the results also show that it is conducive to achieve high performance and low carbon opera-tion of high speed dry cutting hobbing machine ,which can provide important reference value for ma-chinery manufacturing enterprises to achieve low carbon manufacturing .关键词
高速干切滚齿/工艺参数/低碳/BP神经网络/花朵授粉算法Key words
high speed dry hobbing/process parameters/low carbon/BP neural networks/flower pollination algorithm分类
机械制造引用本文复制引用
钟健,阎春平,曹卫东,陈诚..基于BP神经网络和FPA的高速干切滚齿工艺参数低碳优化决策[J].工程设计学报,2017,24(4):449-458,10.基金项目
国家自然科学基金资助项目(51575071) （51575071）
