The identification of control chart patterns is very important in statistical process control. Control chart patterns are categorized as natural and unnatural. The presence of unnatural patterns means that a process i...The identification of control chart patterns is very important in statistical process control. Control chart patterns are categorized as natural and unnatural. The presence of unnatural patterns means that a process is out of statistical control and there are assignable causes for process variation that should be investigated. This paper proposes an artificial neural network algorithm to identify the three basic control chart patterns;natural, shift, and trend. This identification is in addition to the traditional statistical detection of runs in data, since runs are one of the out of control situations. It is assumed that a process starts as a natural pattern and then may undergo only one out of control pattern at a time. The performance of the proposed algorithm was evaluated by measuring the probability of success in identifying the three basic patterns accurately, and comparing these results with previous research work. The comparison showed that the proposed algorithm realized better identification than others.展开更多
论文对生产过程的多元质量控制和诊断技术进行了分析与探讨,针对在生产过程中多个特性的相关性给出了相应的算法,改进了传统的使用多个单变量控制图分别探测生产过程的波动方法,同时,从功能的角度介绍了作者开发设计的用于多元质量控制...论文对生产过程的多元质量控制和诊断技术进行了分析与探讨,针对在生产过程中多个特性的相关性给出了相应的算法,改进了传统的使用多个单变量控制图分别探测生产过程的波动方法,同时,从功能的角度介绍了作者开发设计的用于多元质量控制的分析软件MPAC(Measurement Process Analysis and Control)。展开更多
文摘The identification of control chart patterns is very important in statistical process control. Control chart patterns are categorized as natural and unnatural. The presence of unnatural patterns means that a process is out of statistical control and there are assignable causes for process variation that should be investigated. This paper proposes an artificial neural network algorithm to identify the three basic control chart patterns;natural, shift, and trend. This identification is in addition to the traditional statistical detection of runs in data, since runs are one of the out of control situations. It is assumed that a process starts as a natural pattern and then may undergo only one out of control pattern at a time. The performance of the proposed algorithm was evaluated by measuring the probability of success in identifying the three basic patterns accurately, and comparing these results with previous research work. The comparison showed that the proposed algorithm realized better identification than others.
文摘论文对生产过程的多元质量控制和诊断技术进行了分析与探讨,针对在生产过程中多个特性的相关性给出了相应的算法,改进了传统的使用多个单变量控制图分别探测生产过程的波动方法,同时,从功能的角度介绍了作者开发设计的用于多元质量控制的分析软件MPAC(Measurement Process Analysis and Control)。
