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结合对质量控制指标进行监控,通过统计原理、数据挖掘技术进行产品质量分析,建立一套切合企业实际的分析及控制方法,并应用到企业的质量改善及工程分析中,可用于提供质量改进的依据和途径。针对一般统计过程控制(SPC)方法的缺点,在传统的SPC基础上加入主分量分析方法(PCA)方法,将多种监测参数转化为一种或几种监测指标进行监测,将监测的复杂性大大降低,达到质量监控的目的。系统采用数据挖掘方法,从历史样本数据中分析判断影响过程质量特性的关键参数,给出合理的参数区间范围,发现工艺和质量之间的相关性规律,对现有的工艺条件下提出改进、优化方向。在企业现场的应用中,采用确保产品在整个制造过程中的质量和在线参量,帮助企业真正实现过程稳定,受控和品质提高的信息管理系统。
Combined with the quality control indicators to monitor, through statistical principles, data mining technology product quality analysis, the establishment of a set of practical business analysis and control methods, and applied to the enterprise quality improvement and engineering analysis can be used to provide quality improvement Basis and approach. In view of the shortcomings of the general statistical process control (SPC) method, the principal component analysis method (PCA) is added to the traditional SPC method to convert various monitoring parameters into one or several monitoring indicators for monitoring. The complexity of the monitoring Greatly reduce the quality control purposes. The system uses the data mining method to analyze and judge the key parameters that affect the process quality characteristics from the historical sample data, gives the reasonable range of the parameter range, finds the correlation between the process and the quality, proposes the improvement under the existing process conditions, Optimize the direction. In the field of enterprise applications, to ensure that the product throughout the manufacturing process quality and online parameters, to help enterprises truly process stability, control and quality improvement of information management system.