Research Initiation Award: Adaptive Statistical Process Control

9309270 Runger这项工作的目标是为自适应统计过程控制(SPC)算法的设计提供一个全面的、最佳的解决方案。这项工作将为SPC开发自适应算法，以优化SPC算法的基本设计参数。该系统是自适应的，因为它有能力根据测量变量与其目标值的偏差动态改变元素，如采样间隔和样本大小。研究的一个阶段将使用半经济模型将系统总成本与系统设计参数联系起来。本研究将对自适应程控系统进行优化。这些技术将改善制造操作中的过程控制。其结果是能够更快地识别偏离目标的过程，不需要额外的错误警报，也不需要额外的样本。这转化为更强大的过程控制系统，从而导致更低的制造成本和更好的质量。一个软件系统将为设计自适应SPC系统提供指导，它将提供一种将重要的新技术迅速转移到工业中的手段。

9309270 Runger The objective of this work is a comprehensive, optimal solution to the design of an adaptive statistical process control (SPC) algorithm. The work will develop adaptive algorithms for SPC that optimize the fundamental design parameters of an SPC algorithm. The system is adaptive in the sense that it has the capability to dynamically vary elements, such as sampling intervals and sample sizes, based on the deviations of the measured variable from its target value. One phase of the research will use a semi economic model to relate total system cost to system design parameters. This research will optimize adaptive SPC systems. Process control in manufacturing operations will be improved by these techniques. The result is the ability to identify a process which is off target much faster, without additional false alarms, and without additional samples. This translates to a more powerful process control system, which results in lower manufacturing costs and better quality. A software system which will provide guidance in designing an adaptive SPC system will provide a means of quickly transferring a significant, new technology to industry.