多品种小批量机加工车间关键工序动态SPC研究及应用  

Research and Application on Dynamic Statistical Process Control of Key Process in Multi-varieties and Small-batch Machining Workshop

作　　者：曹军[1] 尹超[1] 刘飞[1] 李进才[1] 尹胜[1] 

机构地区：[1]重庆大学机械传动国家重点实验室,重庆400044

出　　处：《中国机械工程》2011年第23期2822-2827,共6页China Mechanical Engineering

基　　金：国家自然科学基金资助项目(51175528);国家高技术研究发展计划(863计划)资助项目(2007AA040701)

摘　　要：针对多品种小批量机加工车间关键工序因质量数据实时采集困难且样本数据量不足,导致质量统计过程实时控制困难和难于实现质量状态预警等问题,提出了一种集生产现场质量数据实时采集、小样本数据转化处理、质量状态在线控制及预警为一体的多品种小批量关键工序动态SPC(statistical process control)三层技术实现框架,并对其中基于多功能信息交互终端的车间现场质量数据实时采集、多图联合的小样本数据SPC质量控制、基于BP(back propagation)神经网络的质量状态预警等实现方法和技术进行了研究。最后,将该方法在一多品种小批量机加工车间进行了应用,取得了良好的效果。According to the problems of quality data acquisition not in time, lacking of quality sample data in key process of multi--varieties and small--batch machining workshop, which lead to the difficulty for implementing dynamic SPC （statistical quality control） and early warning for quality status, a three--layer technical framework of dynamic SPC for key process in multi--varieties and small--batch machining workshop was proposed, which integrated real--time filed data acquisition, data transforming of small samples, quality status on--line controlling and early warning. Meanwhile, the key technologies for the framework were studied, including the dynamic quality data acqui sition based on the multi--functional information interactive terminal, the multi--charts SPC method under condition of small samples, and the quality status early warning based on BP neural network. Finally, the method was applied in a machining workshop, and good results were obtained.

关 键 词：多品种小批量 机加工车间 关键工序 质量控制 预警 

分 类 号：TH166[机械工程—机械制造及自动化]