注射模冷却系统在线分析技术研究  

Research on online analysis technology of cooling system of injection mould

作　　者：王明中 黄志高[1,2] 侯斌魁 张云[1] 周华民 WANG Ming-zhong;HUANG Zhi-gao;HOU Bin-kui;ZHANG Yun;ZHOU Hua-min(State Key Laboratory of Material Processing and Die&Mould Technology,Huazhong University of Science and Technology,Wuhan,Hubei 430074,China;National Innovation Institute of Digital Design and Manufacturing,Wuhan,Hubei 430075,China)

机构地区：[1]华中科技大学材料成形与模具技术国家重点实验室,湖北武汉430074 [2]国家数字化设计与制造创新中心,湖北武汉430075

出　　处：《模具工业》2020年第8期1-5,11,共6页Die & Mould Industry

基　　金：佛山市核心技术攻关项目(1920001000677)。

摘　　要：注射模冷却系统CAE分析过程繁琐,计算耗时,限制了冷却系统优化过程中的分析次数和优化质量,为提高冷却系统的分析效率,提出基于2次CAE分析结果的冷却系统在线分析方法。综合考虑冷却系统和制品结构特征,将冷却影响因素分为外部因素和内部因素。通过热源模型和CAE分析结果量化,分别得到外部因子和内部因子的表征模型,以此为输入参数建立冷却效率为评价指标的BP神经网络预测模型,基于MoldFlow和Siemens NX平台开发了原型系统,并采用盒形制品验证了方法的有效性。The CAE analysis process of the cooling system of injection mould is tedious and time-consuming,which seriously limits the analysis times and optimization quality in the optimization process of the cooling system.In order to improve the efficiency of cool⁃ing system analysis,an online analysis method of cooling system based on the results of two CAE analysis was proposed.Considering the characteristics of cooling system and prod⁃uct structure,the influence factors of cooling were divided into external factors and inter⁃nal factors.Through the heat source model and CAE analysis results,the representation models of external factors and internal factors were obtained,and then the BP neural net⁃work prediction model with cooling efficiency as the evaluation index was established.The prototype system was developed based on MoldFlow and Siemens NX platform,and the va⁃lidity of the method was verified by using box products.

关 键 词：冷却系统 在线分析 BP神经网络 冷却液 网格单元 

分 类 号：TG76[金属学及工艺—刀具与模具] O242.21[理学—计算数学]