Quality Technology for Variation Reduction

小行星9501217 本研究关注的是开发有效的参数设计方法，田口介绍了减少变异的方法。在参数设计中，目标是通过降低产品或过程对不可控干扰（噪声变量）的敏感性来实现鲁棒性。本研究将利用参数设计实验中固有的结构来获得有效的技术。将研究具有固定目标的静态问题以及具有动态特性的情况。参数设计的适用性也将通过开发更复杂的裂区实验方法和噪声变量无法控制的研究（如在线调查）来扩大。将开发用于分析非标准数据的模型和方法，例如计数或分类响应或来自混合模型的数据。最后，将考虑多种质量特性的情况，并制定系统的数据分析策略。将开发软件，以便利所提供的模型和分析技术的应用。 在当今竞争激烈的市场中，质量改进方法的重要性怎么强调都不过分。工业实验是昂贵和耗时的。 因此，使用有效的设计和分析方法来最大限度地提高这些研究的信息质量是很重要的。 这项研究的成果将使产品和工艺工程师更有效地进行参数设计研究，特别是在动态系统。如果成功，作为本研究的一部分开发的方法将扩大工程应用的范围，包括更复杂的实验情况，在线研究和不同类型的数据分析。 该研究还将产生一个全面的，系统的策略，用于分析实践中常见的多种质量特征。

9501217 Nair This research is concerned with the development of efficient methods for parameter design, an approach for variation reduction introduced by Taguchi. In parameter design, the goal is to achieve robustness by reducing the sensitivity of the product or process to uncontrollable disturbances (noise variables). The research will exploit the structure inherent in parameter design experiments to obtain efficient techniques. Both static problems with fixed target as well as situations with dynamic characteristics will be studied. The applicability of parameter design will also be broadened by developing methods for more complex split-plot experiments and for studies where the noise variables cannot be controlled, as in on-line investigations. Models and methods for analyzing non-standard data, such as count or categorical responses or data from mixture models, will be developed. Finally, the case of multiple quality characteristics will be considered and a systematic data analysis strategy will be developed. Software will be developed to facilitate the application of the models and the analysis techniques provided. The importance of quality improvement methods in today's competitive market cannot be over emphasized. Industrial experiments are expensive and time consuming to conduct. Therefore, it is important that efficient methods of design and analysis are used to maximize the quality of information from such studies. The outcome of this research will enable product and process engineers to conduct parameter design studies more efficiently, especially in dynamic systems. If successful, the methods developed as part of this research will broaden the scope of engineering applications to include more complex experimental situations, on-line studies, and the analysis of different types of data. The research will also yield a comprehensive, systematic strategy for analyzing multiple quality characteristics as commonly found in practice.