多工序制造过程计算机辅助误差诊断控制系统
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摘要
质量管理伴随企业管理的实践而不断发展和完善,现在已经成为一门独立的学科。其中,统计过程控制(Statistical Process Control, SPC)是目前企业中广泛采用的质量管理手段。它通过对关键质量参数和关键工序的样本采集和统计分析,以概率论和数理统计为基础,采用统计控制图、统计描述、统计相关分析、实验设计、回归分析等方法,分析处理与产品质量相关的生产过程数据。
传统的统计过程控制采用单变量统计过程控制方法,只对生产过程中某一个工序的一些重要指标单独地实施统计过程控制。如果需要分析多变量、多工序系统,传统的统计过程控制方法显然无能为力。研究多工序、多变量生产过程质量分析和评价方法,对正确实施多工序生产过程质量控制具有现实意义。减少产品制造过程中出现的各种波动,正确找出制造过程中的波动源,是多工序、多变量生产系统实施质量控制和质量改进的基础。
多元质量控制是同时对多个质量特性进行控制的一种方法。T~2控制图的优点是能够全面地考虑各元之间的相关性,并能在变量相关的条件下精确地给出第一类错误的概率α,但它最大的缺点就是不能诊断。当涉及到的变量数目很多时,在寻找样本的分布规律时工作量很大且样本之间关系容易分辨,另外由于各指标的数据信息之间不可避免的存在重叠。需要用少数变量对若干个指标进行综合,以期既能降低指标的维数,又能充分反映指标的信息。采用主成分分析(PCA)作为主要多元统计方法,把多个指标转化为少数几个独立指标分析。
结合T~2控制图控制图与主成分分析两者的优点,本文提出T~2 -PCA方法,在T~2控制图的基础上,对所有因素作主成分分析,并绘制相应的主成分单值控制图与单变量控制图,作为对T~2控制图的诊断手段。
在多工序加工过程(MMP)中,最终产品的变异是各工序中变异的积累或者累积。建模并控制故障传播,对提高产品空间质量非常必要。采用两种质量的三图诊断系统,借助选控图将上下工序责任分离,以达到诊断目的。
编写多工序制造过程的计算机辅助诊断系统,实现对多变量、多工序制造过程的数据处理,并输出相应的处理结果,以此诊断。
In 1920s, the concept of Quality Control was put forward. Since then, it has been developed and improved along with practices of corporation management, and has become an independent subject. Among all methods of Quality Control, Statistical Process Control (SPC) is most widely used in current enterprises, which has improved the level of corporation management considerably. Through sample collecting and statistical analysis of key quality parameters and key working procedures, with the aim of improving quality level, SPC can analyze producing process data related to quality of products.
Commonly used SPC can only statistically analyze important indexes of certain working procedures one by one, for example, drawing Shewhart charts for every one. Faced with systems of multi-variables, multi-stages, traditional SPC is not competent. For quality control in multi-stage process, it is just necessary to carry out the study on analytical and evaluating standards in multi-variable, multi-stage manufacturing process. Reducing fluctuating and finding out fluctuating resources in manufacturing process is the base of conducting quality control and improvement in multi-variable, multi-stage process.
Multi-variable quality control is a method of controlling several quality characters simultaneously. Despite of the existing numerous multi-variable control theories, multi-variable quality control charts are still preferred because of their trenchancy and intuitionistic characters. T~2 chart designed by H.Hotelling will be applied here to accomplish quality control of multi-variable process. Principle Component Analysis, as an important multi-variable statistical method, is used here to transform lots of indexes into less comprehensive indexes, while facing the problem of complicated sample data and reticula relationship among them.
The T~2 -PCA method is put forward by combining T~2 char and PCA, which is used for the single parameter charts and principle component charts, assisting diagnosis the fault exiting in the stage.
In a multi-stage process, the final variation is accumulated from previous stages. Modeling and controlling faults spread is essential for improvement of production quality. Two-quality Diagnosis Theory is applied two distinguish faults resource.
A computer-aided quality control system is designed to deal with data from multi-variable, multi-stage process and output results.
传统的统计过程控制采用单变量统计过程控制方法,只对生产过程中某一个工序的一些重要指标单独地实施统计过程控制。如果需要分析多变量、多工序系统,传统的统计过程控制方法显然无能为力。研究多工序、多变量生产过程质量分析和评价方法,对正确实施多工序生产过程质量控制具有现实意义。减少产品制造过程中出现的各种波动,正确找出制造过程中的波动源,是多工序、多变量生产系统实施质量控制和质量改进的基础。
多元质量控制是同时对多个质量特性进行控制的一种方法。T~2控制图的优点是能够全面地考虑各元之间的相关性,并能在变量相关的条件下精确地给出第一类错误的概率α,但它最大的缺点就是不能诊断。当涉及到的变量数目很多时,在寻找样本的分布规律时工作量很大且样本之间关系容易分辨,另外由于各指标的数据信息之间不可避免的存在重叠。需要用少数变量对若干个指标进行综合,以期既能降低指标的维数,又能充分反映指标的信息。采用主成分分析(PCA)作为主要多元统计方法,把多个指标转化为少数几个独立指标分析。
结合T~2控制图控制图与主成分分析两者的优点,本文提出T~2 -PCA方法,在T~2控制图的基础上,对所有因素作主成分分析,并绘制相应的主成分单值控制图与单变量控制图,作为对T~2控制图的诊断手段。
在多工序加工过程(MMP)中,最终产品的变异是各工序中变异的积累或者累积。建模并控制故障传播,对提高产品空间质量非常必要。采用两种质量的三图诊断系统,借助选控图将上下工序责任分离,以达到诊断目的。
编写多工序制造过程的计算机辅助诊断系统,实现对多变量、多工序制造过程的数据处理,并输出相应的处理结果,以此诊断。
In 1920s, the concept of Quality Control was put forward. Since then, it has been developed and improved along with practices of corporation management, and has become an independent subject. Among all methods of Quality Control, Statistical Process Control (SPC) is most widely used in current enterprises, which has improved the level of corporation management considerably. Through sample collecting and statistical analysis of key quality parameters and key working procedures, with the aim of improving quality level, SPC can analyze producing process data related to quality of products.
Commonly used SPC can only statistically analyze important indexes of certain working procedures one by one, for example, drawing Shewhart charts for every one. Faced with systems of multi-variables, multi-stages, traditional SPC is not competent. For quality control in multi-stage process, it is just necessary to carry out the study on analytical and evaluating standards in multi-variable, multi-stage manufacturing process. Reducing fluctuating and finding out fluctuating resources in manufacturing process is the base of conducting quality control and improvement in multi-variable, multi-stage process.
Multi-variable quality control is a method of controlling several quality characters simultaneously. Despite of the existing numerous multi-variable control theories, multi-variable quality control charts are still preferred because of their trenchancy and intuitionistic characters. T~2 chart designed by H.Hotelling will be applied here to accomplish quality control of multi-variable process. Principle Component Analysis, as an important multi-variable statistical method, is used here to transform lots of indexes into less comprehensive indexes, while facing the problem of complicated sample data and reticula relationship among them.
The T~2 -PCA method is put forward by combining T~2 char and PCA, which is used for the single parameter charts and principle component charts, assisting diagnosis the fault exiting in the stage.
In a multi-stage process, the final variation is accumulated from previous stages. Modeling and controlling faults spread is essential for improvement of production quality. Two-quality Diagnosis Theory is applied two distinguish faults resource.
A computer-aided quality control system is designed to deal with data from multi-variable, multi-stage process and output results.
引文
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