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面向加工特征的工件制造过程能耗预测方法
引用本文:饶立群,张华,鄢威.面向加工特征的工件制造过程能耗预测方法[J].机械设计与制造,2021(4):66-69.
作者姓名:饶立群  张华  鄢威
作者单位:武汉科技大学绿色制造工程研究院,湖北 武汉 430081;武汉科技大学冶金装备及其控制教育部重点实验室,湖北 武汉 430081
基金项目:机械加工制造系统固有能效属性及其优化创新方法研究
摘    要:在工件的制造过程中,具有多个特征的工件在加工时,其加工特征的能耗属性不同,为了研究在工件不同的加工特征对能耗的影响,创建了面向加工特征的工件制造过程能耗预测模型。首先,将工件看成单个加工特征的集合体,其次对工件特征切削功率的分析,创建有关切削功率的能耗数学模型;然后采用GM(1,1)模型完成工件加工时其切削功率的灰色建模,实现整个工件能耗的预测过程,最后通过对冲床顶支座的加工过程为例,说明了该模型的准确性。

关 键 词:加工特征  能耗预测  灰色建模  GM(1  1)模型

Energy Consumption Prediction Method for Workpiece Manufacturing Process Based on Machining Features
RAO Li-qun,ZHANG Hua,YAN Wei.Energy Consumption Prediction Method for Workpiece Manufacturing Process Based on Machining Features[J].Machinery Design & Manufacture,2021(4):66-69.
Authors:RAO Li-qun  ZHANG Hua  YAN Wei
Affiliation:(Academy of Green Manufacturing Engineering,Wuhan University of Science and Technology,Hubei Wuhan430081,China;Ministry of Education Key Laboratory of Metallurgical Equipment and Control Technology,Wuhan University of Science and Technology,Hubei Wuhan430081,China;Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering,Wuhan University of Science and Technology,Hubei Wuhan430081,China)
Abstract:During the manufacturing process of workpieces,the workpieces with multiple features have different energy consumption properties when processing.In order to study the influence of different machining features on energy consumption in the design stage of workpiece structure,a prediction model of energy consumption in workpiece manufacturing process based on machining features was created.Firstly,the workpiece is regarded as a collection of individual machining features,and then the cutting power of the workpiece features is analyzedto establish a mathematical model of energy consumption based on cutting power;The gray model of workpiece machining energy consumption is realized by GM(1,1)model,so as to predict the energy consumption of the whole workpiece.Finally,taking the analysis of a certain workpiece processing as an example,the accuracy of the model is illustrated.
Keywords:Machining Features  Energy Consumption Prediction  Grey Correlation Analysis  GM(1  1)model
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