SBIR Phase I: Alpha Sigma Pi - A Method for Confident, Robust, and Optimal Process Control

该小型企业创新研究（SBIR）第一阶段项目提出了一个动态和定量的决策支持系统，以帮助制造商了解和优化具有多个过程设置和多个质量要求的复杂过程。 所提出的研究的智力价值源于与扩展单纯形法的可行性的分析解决方案。使用基于约束的方法，这种表示提供了全局的可行性和局部的灵活性的过程中给定的过程设置和质量要求之间的行为联系。遵循这些可行性图或“过程窗口”，过程工程师可以表征和理解制造过程的行为，评估单个过程条件的可行性，并发现任何和所有质量要求的潜在改进。初步结果表明，该接口是一个强大的工具，在优化工艺参数和收紧性能指标。所提出的研究将解决的目标包括：（1）过程行为和可行性的自适应细化;（2）过程设置和质量要求的动态调整;以及（3）开发一个易于使用的系统与数据处理和回归。拟议中的研究将有广泛的影响过程和质量控制的扩展和合理化六西格玛和其他技术目前在制造企业中使用。利用该工具，制造商可以综合和归档过程信息，深入了解其过程行为，并快速收敛到更优化的过程配置。考虑到多个过程参数和质量要求的相互作用以及变化和不确定性的影响，这种直观的界面在当今可用的任何其他决策方法中都是不可用的。此外，从拟议的研究中得出的决策支持系统可以吸引当前用户的电子表格模型和其他形式的模拟与工程设计，制造，金融，保险和军事行动中的应用。

This Small Business Innovation Research (SBIR) Phase I project proposes a dynamic and quantitative decision support system to assist manufacturers in understanding and optimizing complex processes with multiple process settings and multiple quality requirements. The intellectual merit of the proposed research stems from the analytical solution of the feasibility with the Extensive Simplex Method. Using a constraint based approach, this representation provides the global feasibility and the local flexibility of the process given the behavioral linkages between the process settings and the quality requirements. Following these feasibility maps or "process windows", the process engineer can characterize and understand the behavior of manufacturing process, evaluate the feasibility of individual process conditions, and discover potential improvements in any and all of the quality requirements. The preliminary results indicate that the proposed interface is a powerful tool in optimizing process parameters and tightening performance specifications. Objectives that the proposed research will resolve include: (1) adaptive refinement of process behavior and feasibility; (2) dynamic tuning of process settings and quality requirements; and (3) development of an easy to use system with data handling and regression. The proposed research will have a broad impact on process and quality control by extending and rationalizing Six Sigma and other techniques currently utilized in manufacturing enterprises. With the developed tool, manufacturers can synthesize and archive process information, gain insight into their process' behavior, and rapidly converge to more optimal process configurations. Such an intuitive interface, considering the interaction of multiple process parameters and quality requirements as well as the effects of variation and uncertainty, is not available in any other decision making approach available today. Furthermore, the decision support systems derived from the proposed research can appeal to current users of spreadsheet models and other forms of simulation with applications in engineering design, manufacturing, finance, insurance, and military operations.