Development of a Multiscale Modeling and Simulation Methodology for Predictive Paint Material Application in Automotive Coating

开发用于预测汽车涂料中涂料材料应用的多尺度建模和仿真方法

• 批准号: 0700178
• 负责人: Yinlun Huang
• 金额: $ 24.46万
• 依托单位: Wayne State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2013-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0700178&HistoricalAwards=false
• 关键词: Development; Multiscale; Modeling; Simulation; Methodology

In polymeric coating research, a deep gap exists between the knowledge for optimizing macroscale production and that for characterizing finer-scale material properties and product behavior. This is the main reason for product quality and process performance not assured in manufacturing. To close the gap, this project will generate an integrated multiscale modeling and simulation methodology for correlating macro-to-microscale product quality, material and energy efficiency, and environmental quality in automotive paint applications. Moreover, an integrated product-process performance analysis method and a multiscale, multi-objective optimization method will be developed for achieving sustainable, predictive coating manufacturing.The proposed research has distinct features, as the modeling, analysis and optimization methods will permit the study of material, process and product behavior in a very broad spectrum of length and time. If successful, this will generate a variety of scientific guidance and reveal various new opportunities for predictive manufacturing. Industrial collaboration will ensure not only successful fundamental research, but also methodological applicability. The methodologies to be developed will be, in principle, applicable to many other types of manufacturing, such as metal coating and electroplating. The research will make a profound impact on the development of next-generation manufacturing technologies as it will show how to correlate macroscopic phenomena with meso-microscopic behavior of the target systems, and how to improve product design and process operation through utilizing multiscale information. The project will directly benefit education through developing a new course and training students under industrial expert's co-supervision

在聚合物涂层研究中，优化宏观生产的知识与表征精细材料性能和产品行为的知识之间存在很大的差距。 这是产品质量和工艺性能在生产中无法保证的主要原因。 为了缩小差距，该项目将产生一个集成的多尺度建模和仿真方法，用于关联宏观到微观的产品质量，材料和能源效率，以及汽车涂料应用中的环境质量。 此外，一个集成的产品-过程性能分析方法和多尺度，多目标优化方法将开发实现可持续的，可预测的涂层manufacturing.The拟议的研究具有鲜明的特点，作为建模，分析和优化方法将允许在一个非常广泛的长度和时间范围内的材料，工艺和产品行为的研究。 如果成功，这将产生各种科学指导，并揭示预测制造的各种新机会。 工业合作不仅将确保基础研究的成功，还将确保方法的适用性。 原则上，将开发的方法将适用于许多其他类型的制造，如金属涂层和电镀。 该研究将对下一代制造技术的发展产生深远的影响，因为它将展示如何将宏观现象与目标系统的细观-微观行为相关联，以及如何通过利用多尺度信息来改进产品设计和过程操作。 该项目将通过开发新课程和在工业专家的共同监督下培养学生来直接受益于教育