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首页> 外文期刊>International Journal of Computer Integrated Manufacturing >Applying ICA monitoring and profile monitoring to statistical process control of manufacturing variability at multiple locations within the same unit
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Applying ICA monitoring and profile monitoring to statistical process control of manufacturing variability at multiple locations within the same unit

机译：将ICA监视和配置文件监视应用于同一单元内多个位置的制造变异性的统计过程控制

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The assessment of in-process observations can provide useful information on potential sources of process variability. In this research, each source of variation was assumed to generate specific patterns in the spatial and temporal variations observed in data related to the measurement of quality characteristics. The spatial variation pattern and temporal pattern caused by a variation source may turn into the observed within-part and between-part variations in the monitoring of product measurements from multi-location. The traditional I - MR - R/S chart is a useful tool to deal with this type of process monitoring. To further improve the monitoring performance, this paper proposes two new process control methods based on independent component analysis (ICA) monitoring and profile monitoring. The ICA monitoring uses various monitoring statistics obtained from ICA to construct a control procedure. In the profile monitoring approach, a set of distance-based and statistical features is used as the input of a support vector regression (SVR)-based decision function to create a process monitoring method. Average run length (ARL) is used as a performance criterion to evaluate the capability of various control methods in detecting abnormalities. Simulation results show that profile monitoring approach has the best overall performance in terms of ARL, followed by ICA monitoring, and I - MR - R/S chart. This paper makes an important contribution to the monitoring of within-part and between-part variations.
机译：对过程中观察的评估可以提供有关过程可变性潜在来源的有用信息。在这项研究中，假设每个变异源都会在与质量特征测量相关的数据中观察到的时空变异中生成特定的模式。由变化源引起的空间变化模式和时间模式可能会转变为从多位置监视产品测量时观察到的零件内部和零件之间的变化。传统的I-MR-R / S图是处理此类过程监控的有用工具。为了进一步提高监控性能，本文提出了两种基于独立成分分析（ICA）监控和轮廓监控的新过程控制方法。 ICA监视使用从ICA获得的各种监视统计信息来构建控制程序。在概要文件监视方法中，将一组基于距离的统计特征用作基于支持向量回归（SVR）的决策函数的输入，以创建过程监视方法。平均行程长度（ARL）用作性能标准，以评估各种控制方法检测异常的能力。仿真结果表明，就ARL而言，概要文件监视方法具有最佳的总体性能，其次是ICA监视，以及I-MR-R / S图表。本文为监控零件内部和零件之间的变化做出了重要贡献。

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