微车后桥关键工序生产异常损失评估及预警方法.docVIP

微车后桥关键工序生产异常损失评估及预警方法 //.paper.edu.cn - 1 - 中国科技论文在线 微车后桥关键工序生产异常损失评估及预 警方法# 郭晨，尹超** 基金项目：国家 863 计划资助项目（2012AA040914） 作者简介：郭晨（1989- ），男，重庆大学硕士研究生，主要从事制造系统工程方面研究 通信联系人：尹超（1974- ），男，重庆大学教授，博士生导师，主要从事制造系统工程和制造业信息化 方面研究 （重庆大学机械传动国家重点实验室，重庆 400044） 5 摘要：针对微车后桥关键工序生产异常损失评估及预警困难等问题，提出了一种集关键工序生产异常信息 实时采集、生产异常损失评估、生产异常损失预警为一体的微车后桥关键工序生产异常损失评估及预警方 法。对微车后桥关键工序生产异常事件进行了分类，建立了关键工序生产异常损失的评价指标体系，并对 基于灰色模糊评判的关键工序生产异常损失评估、基于径向基函数(RBF)神经网络的关键工序生产异常损失 预警等关键技术进行了研究。最后，将以上研究成果在重庆某后桥生产工厂进行应用验证，取得了良好的10 效果。 关键词：微车后桥；关键工序；生产异常损失；RBF神经网络；评估；预警 中图分类号：TH166 Evaluation and Early Warning Method of Abnormal 15 Production Loss for Minicar Rear Axle Key Process GUO Chen, YIN Chao (State Key Laboratory of Mechanical Transmission，CHONGQing 400044) Abstract: In 6#00aa00view of the difficulties in evaluation and early warning of abnormal production loss for minicar rear axle key process, an abnormal production loss evaluation and early warning method was 20 put forward, which integrates real-time acquisition of abnormal production information for key process, grey fuzzy evaluation of production abnormal loss, early warning of abnormal production loss. The abnormal events of minicar rear axle key process were classified, and an evaluation index system of key process abnormal production was established. Then the key technologies such as evaluation method of abnormal production loss based on grey fuzzy evaluation and the early warning method of 25 abnormal production loss based on RBF neural networks were studied. Finally, the research results were successfully applied in a minicar rear axle product enterprise, and good effects have been achieved. Key words: minicar rear axle; key process; production abnormal loss; neural networks; evaluation; early warning 30 0 引言 微车后桥是微车中十分重要的组装件，主要由后桥桥壳总成、主减速器总成、差速器总 成、驱动轮半轴总成、驱动轮轮毂总成等部分组合而成，在多数情况下为驱动桥，是一个复 杂的多传动系统[1]。生产异常损失是指在生产过程中由于设备异常、物料异常、质量异常、35 工艺异常、人员异常等事件造成的企业有形损失与