Optimal Tolerancing: The Link Between Design and Manufacturing Productivity

最佳公差：设计与制造生产力之间的联系

• 批准号: 9402432
• 负责人: Mark Iannuzzi
• 金额: $ 30万
• 依托单位: Variation Systems Analysis Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-12-15 至 1996-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9402432&HistoricalAwards=false
• 关键词: Optimal; Tolerancing; Link; Between; Design

This SBIR Phase II research addresses a computational environment for optimal dimensional management of components and assemblies. The approach couples a genetic optimization method with a Monte Carlo based tolerance test. Both tolerance ranges and nominal dimensions are included as design variables. The goal is to generate the minimum cost assembly which meets all design and manufacturing considerations, which may be formed as constraints or as multiple objectives. Successful application of the technology will dramatically reduce product development times and manufacturing cost. Current tolerance analysis software does not allocate tolerances or modify nominal dimensions in an optimal fashion. Traditional nonlinear programming methods are insufficiently robust to solve the highly nonlinear, multimodal problem resulting from the consideration of a "real" component or assembly. The combination of the genetic algorithm with a tolerance analysis package has the advantage of efficient operation on a parallel or distributed computing network. This research is directed toward increasing algorithmic efficiency and reliability for both the genetic optimized and the tolerance model simulation.

这个SBIR第二阶段的研究解决了组件和组件的最佳尺寸管理的计算环境。该方法将遗传优化方法与蒙特卡罗耐受性试验相结合。公差范围和标称尺寸都包括在设计变量中。目标是产生满足所有设计和制造考虑的最低成本组装，这些考虑可能形成约束或多个目标。该技术的成功应用将大大减少产品开发时间和制造成本。当前的公差分析软件不能以最佳方式分配公差或修改标称尺寸。传统的非线性规划方法鲁棒性不足，无法解决由于考虑“真实”部件或装配而产生的高度非线性、多模态问题。遗传算法与容差分析包的结合具有在并行或分布式计算网络上高效运行的优点。本研究旨在提高遗传优化和耐受性模型仿真的算法效率和可靠性。