Collaborative Research: Monitoring Process and Product Quality Profiles

合作研究：监控过程和产品质量概况

• 批准号: 0354866
• 负责人: Douglas Montgomery
• 金额: $ 5万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2007-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0354866&HistoricalAwards=false
• 关键词: Collaborative; Research; Monitoring; Process; Product

This grant provides funding for the development of statistical methods for the effective monitoring of curves that represent the quality of a process or product. In most current statistical process control applications in industry it is assumed that that quality can be adequately represented by the distribution of a univariate quality characteristic or by the general multivariate distribution of a vector consisting of several correlated quality characteristics. In many practical situations, however, the quality of a process or product is better characterized and summarized by a relationship between a response variable and one or more explanatory variables. Thus, at each sampling stage one observes a collection of data points that can be represented by a curve (or profile). In some calibration applications, the profile can be represented adequately by a simple straight-line model, while in other applications more complicated models are needed. In this work control charts will be developed and evaluated to monitor such process and product quality profiles. This application will be related to functional data analysis and methods will be developed for linear profiles, nonlinear profiles, and those currently handled using splines and wavelets. The results of this research will lead to more effective use of industrial data to characterize and monitor the quality of processes and products. In particular, the methods can be used to monitor calibration relationships and avoid the over-calibration of measurement devices. This lowers costs and improves measurement accuracy. Applications exist in the pharmaceutical industry in the monitoring of non-linear dose-response curves of successive batches of drugs. Applications have been identified and data have already been obtained in semiconductor manufacturing and in the calibration of chemical and other measurement processes, including one at NASA.

该补助金为开发统计方法提供资金，以有效监控代表过程或产品质量的曲线。 在工业中的大多数当前统计过程控制应用中，假定质量可以由单变量质量特性的分布或由由若干相关质量特性组成的向量的一般多变量分布来充分地表示。 然而，在许多实际情况下，一个过程或产品的质量更好地表征和概括之间的关系的响应变量和一个或多个解释变量。 因此，在每个采样阶段，人们观察到可以由曲线（或轮廓）表示的数据点的集合。 在一些校准应用中，轮廓可以由简单的直线模型充分表示，而在其他应用中需要更复杂的模型。 在这项工作中，将制定和评价控制图，以监测此类工艺和产品质量概况。 这个应用程序将涉及到功能数据分析和方法将开发线性配置文件，非线性配置文件，以及目前使用样条和小波处理。 这项研究的结果将导致更有效地利用工业数据来表征和监测过程和产品的质量。 特别地，该方法可以用于监控校准关系并避免测量设备的过度校准。 这降低了成本并提高了测量精度。 在制药工业中存在对连续批次药物的非线性剂量-响应曲线的监测的应用。 在半导体制造和化学及其他测量过程的校准中，包括在美国航天局的校准中，已经确定了应用，并获得了数据。