摘要
在实际的制造过程中,被加工零部件或产品往往具有多个质量特性,且这些质量特性之间存在一定的相关性,如何确定该过程的过程能力指数以及对过程质量进行诊断,是迫切需要解决的问题,该问题的研究不仅对多元制造过程能力分析研究具有重要的意义,而且对多元制造过程的质量进行监控和诊断均具有一定的理论意义和实用价值。
本文在实地调查的基础上,以制造生产过程为研究对象,针对具体的问题,采用主成分分析、支持向量机等方法和技术,系统地研究了多元制造过程中多元过程能力指数分析及质量诊断的理论方法和技术,这对于制造企业分析其过程现状,从而提高其产品的质量具有很重要的意义。本文的研究内容主要包括:
1.生产过程质量控制与诊断体系构建。针对制造企业过程中的质量的问题,引起产品质量缺陷的既有过程运行参数也有设备及零部件因素,较完善地构建了生产过程质量控制与诊断体系与流程,更好地考虑了产品加工过程中导致产品质量缺陷的深层次原因,为制造过程波动溯源和质量改进提供有力和明确的支持。
2.多元过程能力指数的度量与构建。产品的制造过程能力体现了过程稳定地实现加工质量的水平,过程能力分析的目的是研究制造过程的变异相对于设定公差的满足程度。由于产品需要多个质量特性进行描述,因而增加了多元过程能力分析的复杂性,需要对其进行降维,以简化分析过程。首先应用主成分分析法对多元过程进行降维,得到主成分分量的规格区间。在此基础上,首先提出基于体积比的改进Taam多元过程能力指数,并对其进行了统计假设检验。之后,利用主成分分量的概率密度函数,给出了多元过程的三种不合格品率,并基于此进行了过程能力分析。
3.多元质量控制与诊断应用研究。在多元质量控制中,由于多个质量特性之间存在相关性的问题,多元控制图一旦发出报警信号,很难判断是哪个或者是那些变量的组合出现了问题。针对实际多元过程中均值偏移问题应用支持向量机方法进行了诊断研究,首先选取要研究的变量,每一个变量分为正常和异常两种情况,每一个变量分别用一个SVM分类模型来诊断该变量是否发生偏移,在对每一个变量的偏移进行诊断时,同时考虑了其他变量对该变量的影响,模型子个数与过程质量特性个数相等,最终结果将多元过程均值偏移划分成不同的模式组合,从而实现过程质量的诊断。
In manufacturing practice, the quality of a process or product is usually characterized by multiple correlated critical characteristic which are correlated. It is critical to assess the process capability and to diagnose the assignable causes in these cases in quality engineering. This dissertation mainly studies the multivariate process capability analysis (MPCA) and multivariate process diagnosis in manufacturing process with multivariate characteristics. Aiming at this objective, two multivariate process capability indices (MPCIs) are proposed to assess the process capability and a monitoring and diagnosis scheme is proposed and applied in multivariate manufacturing application.
With the introduction of basic theories and methods of MPCIs and multivariate diagnosis approaches, the proposed MPCIs and the process monitoring and diagnosis scheme are proposed as follows:
Firstly, a monitoring and diagnosis scheme is constructed based on statistical process control and multivariate quality diagnosis methods. The proposed scheme is well developed to detect the abnormal signal when the process is out of control, and to estimate the assignable causes which lead to the out-of-control of the manufacturing process.
Secondly, two multivariate process capability indices based on principle component analysis (PCA) are proposed to estimate the process capability. Before measuring MPCIs, PCA is used to reduce the data dimension, and the tolerance of principle components (PCs) can be obtained. Then the MPCI proposed by Taam et al. is modified based on the PCs’tolerance, and the confidence interval is estimated through hypothesis test. Moreover, the estimation of non-conforming proportion on the basis of the probability density function of PCs is proposed to analyze the process capability. Case studies show that the two methods of MPCA are both convenient to assess multivariate process.
Thirdly, the application of multivariate process control and diagnosis is studied in automobile manufacturing. The diagnosis approach based on support vector machine (SVM) is proposed to find the assignable causes after the multivariate control chart gives an alarm. The classification model based on SVM is suggested to detect the shift for each variable, while considering the effects of other variables. Therefore, different combinations of mean shifts of multivariate process are obtained and used to find the assignable causes. Case study shows that the identification of assignable causes is effective in diagnosing multivariate process.
引文
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