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

这个小型企业创新研究(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工艺设置和多个质量要求的复杂工艺。所提出的研究的智力价值来源于用扩展单纯形法得到的可行性的解析解。使用基于约束的方法，该表示法在给定过程设置和质量要求之间的行为联系的情况下提供了过程的全局可行性和局部灵活性。根据这些可行性图或“工艺窗口”，工艺工程师可以描述和了解制造工艺的行为，评估个别工艺条件的可行性，并在任何和所有质量要求中发现潜在的改进。初步结果表明，该界面是优化工艺参数、严格性能指标的有力工具。拟议的研究将解决的目标包括：(1)过程行为和可行性的适应性改进；(2)过程设置和质量要求的动态调整；以及(3)开发一个具有数据处理和回归功能的易于使用的系统。拟议的研究将通过扩展和合理化目前在制造企业中使用的六西格玛和其他技术，对过程和质量控制产生广泛的影响。使用开发的工具，制造商可以合成和存档工艺信息，洞察其工艺行为，并快速收敛到更优化的工艺配置。考虑到多个工艺参数和质量要求的相互作用以及变化和不确定性的影响，这种直观的界面在目前可用的任何其他决策方法中都是不可用的。此外，根据所提出的研究得出的决策支持系统可以吸引电子表格模型和其他形式的模拟的当前用户，并在工程设计、制造、金融、保险和军事行动中应用。

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.