Robust Parameter Design: Modeling, Analysis and Layout Techniques

9704649吴建福稳健参数设计采用统计方法和工程思想，通过降低产品或工艺对不可控变化的敏感度来提高产品或工艺的质量。正如许多工业案例研究所表明的那样，它对制造业产生了巨大影响。主要的重点是研究参数设计实验数据的建模和分析，以及有效的实验规划布局技术。首先，考虑具有静态特征的稳健设计的三个方面：1.稳健因子设置的理论特征及其与常用图解方法的关系；2.使用非常一般的损失函数，特别是在无法识别调整因子的情况下，由于质量差而造成的损失；以及3.对收集有序分类数据的测量系统的评估。与最后一个方面相关的问题是，当某些数据响应被错误分类时，对来自设计实验的有序分类数据进行建模和分析。其次，研究了具有功能特性(即响应是函数关系而不是单值)的稳健参数设计的两个方面：基于广义信噪比的方法与采用两阶段建模的响应函数建模方法的比较；以及为提高稳健可靠性而进行的试验退化曲线的建模和分析。最后，对具有静态特性的参数设计和具有信号响应系统的实验进行了有效的规划。新的理论和计算工具被用来解决这个问题。稳健的参数设计使用统计方法和工程思想，通过降低产品或过程对不可控变化的敏感度来提高产品或过程的质量。许多工业案例研究表明，该方法已成功实施。研究人员研究了稳健参数设计在比目前所考虑的更复杂的情况下的应用，从而扩大了稳健参数设计的范围。稳健参数设计使用实验，其两个主要问题是如何正确选择实验条件(即实验方案)和如何对数据进行建模和分析(即分析)，以确定重要因素并为这些因素推荐最佳设置。在第一部分中，(I)获得了稳健因素设置的理论特征并与图解方法相关，(Ii)开发了新的策略来最小化由非常一般的损失函数度量的质量差造成的损失，(Iii)对收集有序分类数据的测量系统进行评估和分析。在第二部分中，(I)比较了两种分析信号-响应系统的方法(例如，燃气表，其中信号是储气罐中的气体量，而响应是从燃气表读取的气体量)(Ii)研究了分析降解曲线的一般模型和方法(例如，荧光强度随着时间的推移而减小)。使用降级数据具有战略意义，因为可以更快、更有效地获得有关可靠性的信息。第二部分的研究成果将在一个汽车项目上进行检验。第三部分针对参数设计问题，研究了高效经济的试验计划问题。

9704649 Chien-Fu J. Wu Robust parameter design employs statistical methods and engineering ideas to improve the quality of a product or process by making it less sensitive to uncontrollable variation. It has had a big impact on manufacturing as demonstrated by many industrial case studies. The main focus is to study modeling and analysis of data from parameter design experiments and layout techniques for efficient experimental planning. First, three aspects of robust design with static characteristics are considered: 1. theoretical characterizations of robust factor settings and their relation to commonly used graphical methods; 2. loss due to poor quality using very general loss functions, especially where no adjustment factor can be identified; and 3. assessment of measurement systems that collect ordered categorical data. A related problem to the last aspect is the modeling and analysis of ordered categorical data from designed experiments when some of the data responses are misclassified. Next, two aspects of robust parameter design with functional characteristics (i.e., whose response is a functional relation, instead of a single value) are studied: comparison of generalized signal-to-noise ratio based method to a response function modeling method that employs a two-stage modeling, and the modeling and analysis of degradationcurves from experiments for robust reliability improvement. Finally, efficient planning of experiments is studied for parameter designswith static characteristics and with signal-response systems. Novel theoretical and computational tools are employed to solve this problem. Robust parameter design employs statistical methods and engineering ideas to improve the quality of a product or process by making it less sensitive to uncontrollable variation. It has been successfully implemented as demonstrated by many industrial case studies. The investigators study the use of robust parameter design in more complicated situations than considered to date, thereb y, extending the scope of robust parameter design. Robust parameter design uses experiments whose two major issues are how to properly choose the experimental conditions (i.e., experimental plans) and how to model and analyze the data (i.e., analysis) to identify the important factors and recommend the optimal settings for these factors. In Part I, (i) theoretical characterizations of robust factor settings are obtained and related to graphical methods, (ii) new strategies are developed for minimizing loss due to poor quality as measured by very general loss functions, (iii)assessment and analysis of measurement systems that collect ordered categorical data. In Part II, (i) two methods of analyzing signal-response systems (e.g., gas gauge where the signal is the amount of gas in the gas tank and the response is the reading from the gas gauge) are compared (ii) general models and methods for analyzing degradation curves are investigated, (e.g., fluorescent light intensity decreases over time). Using degradation data is strategic because information about reliability can be obtained sooner and more efficiently. The research results in Part II will be tested on an automobile project. In Part III, the problem of efficient and economic planning of experiments is studied for parameter design problems.