GOALI/IUCRP: Development of a Methodology for Optimal ModelDesign to Minimize Warpage and Weldline in Injection Molded Parts

GOALI/IUCRP：开发最佳模型设计方法，以最大限度地减少注塑零件的翘曲和熔接线

• 批准号: 9500009
• 负责人: Byung Kim
• 金额: $ 17.22万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-08-01 至 1998-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9500009&HistoricalAwards=false
• 关键词: GOALI; IUCRP; Development; Methodology; Optimal

9500009 Kim Automated design methodology research in injection molding will be conducted on an actual production part provided by the industrial partner. Prior research has demonstrated the predictive capability of neural network learning algorithm applied to injection molded parts. Building on that experience, this research will optimize the location of weldline and minimize warpage using statistical methods, combining both Box complex and Tagnchi experimental designs. The design space will cover part, mold, machine set up and resin. The simulation will be carried out at the University and actual injection molding will be conducted at the site of the industrial partner. This is a case study approach with a specific practical, high volume part. Such a demonstration enables industrial production to replace the trial and error process optimization with an automated design. Injection molding is a widely applied process for the manufacturing of numerous plastic parts. Process parameters are often setup by trial and error. Successful completion of this project has the potential to improve the consistency and quality of injection molded products. By integrating product design with process simulation, products can be designed for manufacture thus reducing the developmental time. As a result of cooperative research between the University and industry, manufacturing research relevance to the real world scenario will be maintained as a result of the cooperative research.

小行星9500009 注塑成型自动化设计方法研究将在工业合作伙伴提供的实际生产零件上进行。 先前的研究已经证明了神经网络学习算法应用于注塑件的预测能力。 基于这些经验，本研究将使用统计方法，结合Box复合体和Tagnchi实验设计，优化焊缝位置并最大限度地减少翘曲。 设计空间将涵盖零件、模具、机器设置和树脂。 模拟将在大学进行，实际注塑成型将在工业合作伙伴的现场进行。 这是一个案例研究方法，具有特定的实用性，高容量部分。 这样的演示使工业生产能够用自动化设计取代试错过程优化。 注塑成型是一种广泛应用于制造许多塑料零件的工艺。 工艺参数通常通过反复试验来设置。 该项目的成功完成有可能提高注塑产品的一致性和质量。 通过将产品设计与过程仿真相结合，可以为制造而设计产品，从而缩短开发时间。 作为大学和产业之间的合作研究的结果，制造研究的相关性，真实的世界的情况下，将保持作为合作研究的结果。