What Is Performance Improvement?
Research on performance evaluation and improvement methods of process quality control is a paper written by Wang Haiyu.
Research on Performance Evaluation and Improvement Methods of Process Quality Control
Right!
- Chinese name
- Research on Performance Evaluation and Improvement Methods of Process Quality Control
- Author
- Wang Haiyu
- Research on performance evaluation and improvement methods of process quality control is a paper written by Wang Haiyu.
- Subtitle
- Foreign title
- Author of the paper
- By Wang Haiyu
- tutor
- Xu Jichao
- major
- Management Science and Engineering
- Degree level
- PhD thesis
- Degree-granting unit
- Northwestern Polytechnical University
- Degree award time
- 2006
- Key words
- Enterprise Management Product Management Quality Control Process Control Fluctuation Theory
- Collection number
- F273.2
- Collection catalog [1]
- The extensive application of advanced domestic and foreign quality technologies and quality control methods is of great significance for enterprises to improve product quality and enhance product competitiveness. How to use quality engineering technology to design and produce low-cost, short-cycle, high-quality, and high-reliability products, thereby gaining a competitive advantage, has become a concern of the majority of domestic and foreign theoretical researchers and practical workers. The mainstream of modern quality engineering is to reduce, suppress and control fluctuations in product realization. Fluctuation is the root cause of quality problems. How to reduce and control fluctuations in product realization has become the core content of contemporary quality engineering. Based on the theory of fluctuations, this paper systematically studies the theory, methods, and implementation techniques of reducing and controlling fluctuations in products with various quality characteristics in the realization process based on processes and using empirical and simulation methods. This article first points out some of the shortcomings of the most widely used Shewhart control charts today, and introduces the average product length (APL) as a measurement tool for control chart monitoring efficiency when the conventional output quality characteristics follow a univariate normal distribution. Sensitivity analysis is performed on each parameter of the control chart, and the reasonable selection of each parameter is guided to obtain the optimal design of the conventional control chart. Exponentially weighted moving average (EWMA) chart is a control chart method suitable for monitoring small fluctuations in the process. This paper also uses APL as a performance measurement tool to construct an optimized EWMA diagram design model. This method is an improvement on the general EWMA method. Through comparative analysis, it shows that it can monitor the smaller fluctuations in the process more sensitively. Traditional process quality control methods are performed under the assumption that the process output quality characteristics follow a normal distribution, but in practical applications, there are a large number of non-normal phenomena. Based on the analysis of non-normally distributed process control methods, this paper introduces the weighted variance method as a separate distribution technology to construct asymmetric control lines. Then use APL as a performance measurement tool to establish non-normal optimization design models of Shewhart control chart and EWMA control chart respectively. The coordinated control of multiple quality characteristics is an important aspect of ensuring product quality. Based on the analysis of several commonly used multivariate quality control charts, this paper proposes a simple multivariate control chart method based on multipoint alarms. This method can quickly find smaller fluctuations in multivariate quality processes. At the same time, this paper proposes a method for diagnosing out-of-control signals based on virtual variable regression technology, which can effectively solve the problem that multiple quality control charts cannot determine the source of abnormal fluctuations when out-of-control signals appear. Finally, this article discusses how to use the residuals to construct an EWMA chart for the process mean and variance monitoring for processes with autocorrelation phenomena. Comparison with several other process monitoring methods shows that this EWMA residual graph has good performance when monitoring autocorrelation processes. Then discussed how to integrate statistical process control (SPC) and automatic process control (APC) reasonably to form an effective process quality control method. This article provides practical and practical techniques for real-time monitoring of production systems from both theoretical and application perspectives. The improved process quality control methods proposed in this paper are applicable to the needs of normal, skew, small fluctuation, multivariate, autocorrelation, etc. The method researched will greatly improve the efficiency of process control, which is of great significance for improving product quality, reducing quality loss, and improving the market competitiveness of enterprises. [1]