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

这笔赠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了资金。在制造过程中，工艺差异和不一致性是主要的质量问题。对于一个涉及多个工序的复杂制造过程，不同工序的工艺变异会累积在产品上，并沿着工艺传播。该项目旨在开发一种系统的方法来描述和减少过程差异，从而提高过程质量。建立了定量变异传播模型。该模型以产品/工艺设计和工程知识为基础，采用解析和实证相结合的方法，将关键工艺变量来源和关键产品质量特征联系起来。这种定量模型允许采用系统理论和先进的统计技术(例如，线性混合模型的方差分量分析)，以便对变异传播进行正向和反向分析。正向分析可以识别重要的过程阶段，并为设计改进提供指导方针，而反向分析可以快速确定质量差异的根本原因。研究成果将最终在工业环境中得到验证，如果研究成功，将为制造过程的过程控制和质量改进提供科学依据。使用开发的方法，来自产品设计、工艺设计、过程中传感和产品质量检测的海量信息将在一个定量模型下集成。该集成模型为发展有效的变异传播分析技术和快速识别变异根源奠定了基础。开发的方法在工业中的有效实施将为计算机辅助产品/过程设计和过程监测与诊断提供一套强大的工具，从而减少差异，从而促进美国工业的竞争力。研究成果也将被转移到本科生和研究生课程中，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.