Collaborative Research: Blind Discovery of Variation Sources for Visualization by Multidisciplinary Teams

系统地减少生产差异和提高质量的程序（通常称为六西格玛程序）现在已在工业中牢固地建立起来。然而，绝大多数六西格玛分析工具是在几十年前设计的，用于有限数量的数据和相对简单的结构。本研究将开发一种知识发现方法，用于减少六西格玛变化，该方法专为现代制造操作中的高维数据结构而设计。与为已知变异源构建先验模型，然后在数据中寻找特定的预建模模式的方法相反，本研究的目标是仅基于数据样本，不进行预建模，盲目地发现变异模式的性质及其来源。每个已识别模式的交互式图形可视化将使用户能够可视化变化的根本原因。为了实现这一点，本研究将开发一种范式来表示变化模式，包括各种数据结构中的线性和非线性现象。该研究还将开发算法，以尽可能准确、健壮和自动化地盲目识别模式。如果成功，这项研究将使变异减少方法现代化，以跟上测量和信息技术的进步。虽然算法必然是复杂的，但自动化，加上将开发用于可视化结果的方法，将创建易于使用和广泛适用的工具。这些特征是传统的六西格玛标志，将促进该方法的广泛传播和采用，并使其能够被多学科合作团队（例如，具有不同背景的操作员、工程师、统计学家和管理人员）使用。通过将结果整合到面向本科生和博士生的六西格玛和数据挖掘课程中，以及面向专业硕士课程中种族和技术多样化的工程师和管理人员的课程中，将进一步加强传播。

Programs for systematically reducing manufacturing variation and improving quality (often called six-sigma programs) are now firmly established in industry. However, the vast majority of six-sigma analysis tools were designed decades ago for use with limited amounts of data with relatively simple structure. This research will develop a knowledge discovery methodology for six-sigma variation reduction that is designed for the high-dimensional data structures found in modern manufacturing operations. In contrast to methods in which one constructs prior models for known variation sources and then looks for those specific premodeled patterns in the data, the goal of this research is to blindly discover the nature of the variation patterns and their sources, based solely on a sample of data, with no premodeling. Interactive graphical visualizations of each identified pattern will enable users to visualize the root causes of variation. To accomplish this, this research will develop a paradigm for representing variation patterns that encompasses linear and nonlinear phenomena in a variety of data structures. The research will also develop algorithms for blindly identifying the patterns with as much accuracy, robustness, and automation as possible. If successful, this research will modernize variation reduction methods to keep pace with advances in measurement and information technology. Although the algorithms will necessarily be complex, the automation, coupled with methods that will be developed for visualizing the results, will create tools that are easy-to-use and widely applicable. These characteristics, which are traditional six-sigma hallmarks, will facilitate broad dissemination and adoption of the methodology and enable its use by multidisciplinary teams of collaborators (e.g., operators, engineers, statisticians, and managers with varying backgrounds). Dissemination will be further enhanced by integrating the results into six-sigma and data mining courses offered to undergraduate and Ph.D. students and to an ethnically and technically diverse spectrum of engineers and managers in professional masters' courses.