Modeling, Analysis, and Control of Variation Propagation in Manufacturing Processes

这项拨款为复杂制造过程中变异传播的建模、分析和控制方法的开发提供资金。工艺变化和不一致是制造过程中主要的质量问题。对于涉及多个操作步骤的复杂制造过程，不同步骤的工艺变化会累积在产品上，并沿过程传播。该项目旨在开发一种系统的方法来描述和减少过程变化，从而提高过程质量。建立定量变异传播模型。在该模型中，基于产品/过程设计和工程知识的分析和经验方法被用于将关键过程变异源和关键产品质量特征联系起来。该定量模型可以利用系统论和先进的统计技术（如线性混合模型的方差成分分析）对变异传播进行正向和反向分析。前向分析可以识别重要的工艺阶段，为设计改进提供指导，后向分析可以快速识别质量变化的根本原因。研究结果将最终在工业环境中得到验证。如果成功，该研究项目将为制造过程的过程控制和质量改进提供科学基础。使用开发的方法，来自产品设计、过程设计、过程中感知和产品质量检验的大量信息将集成在一个定量模型下。该集成模型为开发有效的变异传播分析和快速变异根源识别技术奠定了基础。在工业中有效实施已开发的方法将为计算机辅助产品/工艺设计、工艺监测和诊断提供一套强大的工具，以减少变化，从而为美国工业提供竞争力的推动。研究成果也将转移到本科和研究生课程中，这将对质量工程和制造教育产生长期影响。

This grant provides funding for the development of a methodology for modeling, analysis, and control of variation propagation in complicated manufacturing processes. Process variation and inconsistency are the major quality concerns in a manufacturing process. For a complicated manufacturing process involving multiple operation steps, the process variation at different steps will be accumulated on the product and propagates along the process. This project aims at developing a systematic methodology to describe and reduce the process variation and hence improve the process quality. A quantitative variation propagation model will be developed. Both analytical and empirical methods based on product/process design and engineering knowledge are used to link the key process variation sources and key product quality characteristics in this model. This quantitative model allows system theory and advanced statistical techniques (e.g., variance component analysis of linear mixed models) to be adopted in order to conduct forward and backward analysis of variation propagation. The forward analysis can identify important process stages and provide guidelines for design improvement, while the backward analysis can quickly identify the root causes of quality variations. The research results will be finally validated in industrial settings.If successful, this research project will contribute to the science base of process control and quality improvement for manufacturing processes. Using the developed methodology, vast amounts of information from product design, process design, in-process sensing, and product quality inspection will be integrated under a quantitative model. This integrated model lays a foundation to develop effective techniques of variation propagation analysis and quick variation root cause identification. Effective implementation of the developed methodology in industry will provide a set of powerful tools for computer-aided product/process design and process monitoring and diagnosis for variation reduction, and thus provide a competitive boost to U.S. industry. The research accomplishments will be transferred into undergraduate and graduate curricula as well, which will generate long-term impact on the education of quality engineering and manufacturing.