Automated Identification of Basic Control Charts Patterns Using Neural Networks

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.

用于SPC控制图识别的神经网络训练并行算法研究

在研究统计过程控制基础上建立控制图识别的神经网络模型。通过对前向型神经网络分析,设计了粒子群优化算法与阻尼最小二乘法相结合的复合训练算法,基于并行运算原理对上述复合算法进行优化。实验结果表明,该复合算法经过并行优化后训练速度明显提高,具有较大的实用价值。

Statistical Process Control in Flexible Manufacturing Systems

The statistical process control techniques used in flexible manufacturing systems arestudied in this paper.Control charts which can be used in the low volume production are pro-posed.The automatic recognizer of unnatural patterns for the control chart by using back-propagation neural network is also presented.

基于控制图和神经网络的印刷过程质量智能监控技术

将SPC方法和BP神经网络技术相结合,应用于印刷过程,研究了印刷过程质量智能监控技术.分析影响印刷品质量的因素,设计印刷质量管理数据库;根据印刷过程特点,结合印刷质量的检测方法,确定控制图的绘图方案;通过实验,建立4个BP神经网络模型,实现控制图模式智能识别及异常模式特征参数的估计,并进一步分析异常原因及诊断建议.在分析研究的基础上,开发了印刷过程质量智能监控系统,对提高印刷过程的自动化和智能化水平具有重要的意义.

FMS统计过程控制技术的研究

对统计过程控制技术（ＳＰＣ）在柔性制造系统（ＦＭＳ）中的应用进行了研究，主要研究内容包括：适用于多品种、小批量生产的质量控制图；基于神经网络的质量控制图异常模式自动识别技术。

FMS质量保证体系数据采集及分析系统的研究

用容栅数显卡尺对FMS加工过程中的零件几何参数进行测量,以无线电通讯方式将数据传给数据采集器,数据采集器通过标准RS485串行口与计算机相连,计算机对测量数据进行处理,形成质量控制图并用神经网络方法对质量控制图进行分析(对质量控制图模式识别)。

基于ART1神经网络的统计过程控制系统

统计过程控制(SPC)在改进过程水品、提高产品质量方面作出了巨大贡献。本文讨论了一种基于自适应谐振理论(ART)神经网络的SPC系统。与一般SPC系统相比,本系统不仅可以在线检测过程异常,对各种控制图异常模式还具有实时学习、在线识别功能。同时,本系统对过程的分析,无需如常规控制图一样,建立在正态假设的前提下,因此应用更方便、范围更广泛。作为一种新的SPC工具,ART1神经网络为改进控制图的应用提供了一种新的可能。