NSF-DFG: Multiscale Data-Physics Models for the Critical Role of Interfaces in Overmolded Thermoplastic Parts

NSF-DFG：多尺度数据物理模型显示界面在包覆成型热塑性零件中的关键作用

• 批准号: 2225290
• 负责人: Jacob Fish
• 金额: $ 77.78万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2225290&HistoricalAwards=false
• 关键词: NSF; DFG; Multiscale; Data; Physics

This award supports US-German collaborative research related to joining two dissimilar polymer layers. The polymer overmolding technique is employed in numerous industrial manufacturing processes, in application areas including automotive, aeronautics, energy, and biomedicine. These composite materials can achieve their desired properties and avoid part failure or overdesign only if there is good adhesion at the interface between the two disparate components. Currently this is achieved through the use of simple ad hoc models and trial-and-error practices. Detailed models and understanding have not yet been formed because there are multiple spatial and temporal scales and process-morphology parameters that govern manufactured part behavior. The effect of various molecular processes and the role of each of the process/morphology parameters and their combined effect on the manufactured component is not well understood today. This award will support fundamental research to help understand the bonding strength and fracture toughness, by relating interface properties to molecular processes taking place at these critical interfaces. Industrial collaborators will help ensure the practical application of the new knowledge and computational tools. The Integrated Computational Materials Engineering paradigm will be advanced by educating the next generation of engineers and scientists in integrated design of products, the materials that comprise them, their associated materials processing methods, and multiscale modeling.To address the fundamental issue of interface failure in overmolded thermoplastic parts, this project will employ novel multiscale computational modeling, coupled to the evolving tools of machine learning, to quantitatively understand the factors that affect adhesion strength and fracture toughness. In particular, this project will: (i) develop a molecular-level understanding to quantitatively predict the temporal evolution of the strength and fracture toughness of a laminate/semicrystalline polymer interface as a function of process parameters; (ii) combine this information with a coarser-grained integrator to predict and hence optimize interface properties; and (iii) design a validated hybrid data-physics driven multiscale framework for integrated manufacturing and product design of these composites.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项支持与连接两个不同聚合物层相关的美德合作研究。聚合物包覆成型技术广泛应用于汽车、航空、能源和生物医学等应用领域的众多工业制造过程中。仅当两个不同组件之间的界面具有良好的粘合力时，这些复合材料才能实现其所需的性能并避免零件故障或过度设计。目前，这是通过使用简单的临时模型和试错实践来实现的。由于存在多个空间和时间尺度以及控制制造零件行为的工艺形态参数，因此尚未形成详细的模型和理解。目前，各种分子过程的影响、每个过程/形态参数的作用及其对制造部件的综合影响还没有得到很好的理解。该奖项将支持基础研究，通过将界面特性与这些关键界面上发生的分子过程联系起来，帮助了解粘合强度和断裂韧性。工业合作者将帮助确保新知识和计算工具的实际应用。将通过教育下一代工程师和科学家进行产品、构成产品的材料、相关材料加工方法和多尺度建模的集成设计，来推进集成计算材料工程范式。解决包覆成型中界面失效的根本问题对于热塑性零件，该项目将采用新颖的多尺度计算模型，结合不断发展的机器学习工具，定量地了解影响粘合强度和断裂韧性的因素。特别是，该项目将：（i）发展分子水平的理解，以定量预测层压板/半晶聚合物界面的强度和断裂韧性随工艺参数的变化； (ii) 将这些信息与粗粒度积分器相结合，以预测并优化界面特性； (iii) 设计一个经过验证的混合数据物理驱动的多尺度框架，用于这些复合材料的集成制造和产品设计。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。