轻工装备制造阶段关键绿色质量特性提取模型  

An extraction model of critical to green quality characteristics in light industry equipment manufacturing stage

作　　者：郑辉[1] 赵乃莹 郭甜甜 邢萌 ZHENG Hui;ZHAO Naiying;GUO Tiantian;XING Meng(Department of Industrial Engineering,Tianjin University of Science and Technology,Tianjin 300457,CHN)

机构地区：[1]天津科技大学工业工程系,天津300457

出　　处：《制造技术与机床》2022年第1期152-157,共6页Manufacturing Technology & Machine Tool

基　　金：科技部创新方法工作专项资助(2019IM02030);教育部研究重大课题攻关项目(16JZD014)。

摘　　要：为了提升轻工装备制造业在绿色发展过程中的可持续竞争力,在关键质量特性概念的基础上提出关键绿色质量特性(critical to green quality characteristics,CTGQs),建立CTGQs提取模型,识别出对环境影响最大的工艺过程参数,实现轻工装备在制造阶段的关键绿色质量特性提取,为实现绿色制造提供了理论基础。为更好地消除提取过程中的冗余数据,将改进ReliefF算法与自适应粒子群(adaptive particle swarm optimization,APSO)算法相结合,提高CTGQs提取准确性。最后以啤酒发酵罐为例,验证了该模型的有效性。In order to enhance the sustainable competitiveness of light industry equipment manufacturing industry in the process of green development,based on the concept of critical to green quality characteristics(CTGQs),a CTGQs extraction model is established to identify the process parameters that have the greatest impact on the environment,The critical to green quality characteristics extraction of light industry equipment in manufacturing stage is realized.In order to eliminate the redundant data in the extraction process,the improved ReliefF algorithm is combined with adaptive particle swarm optimization(APSO)algorithm to improve the accuracy of CTGQs extraction.Finally,a beer fermentation tank is taken as an example to verify the effectiveness of the model.

关 键 词：轻工装备制造 CTGQs提取模型 APSO算法 RELIEFF算法 

分 类 号：TS2[轻工技术与工程—食品科学与工程] TS261.3