Fundamental Studies of Process-Material Interactions in Advanced Adhesion-Driven Manufacturing with Automated Placement of Uncured Thermoset Tows as Model Process

以自动放置未固化热固性丝束作为模型工艺的先进粘合驱动制造中工艺与材料相互作用的基础研究

• 批准号: 2127361
• 负责人: Subramani Sockalingam
• 金额: $ 65.17万
• 依托单位: University of South Carolina at Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2127361&HistoricalAwards=false
• 关键词: Fundamental; Studies; Process; Material; Interactions

Automated tow placement (ATP) is an adhesion-driven manufacturing process for polymer matrix composites in which a bundle of fibers impregnated with resin—known as a tow—is placed onto a tool or onto a previously-placed tow. The technique is widely used in the aerospace industry and is also gaining interest in automotive, military, and energy sectors due to its potential for improved productivity and reduced material waste. The ATP process requires application of temperature and pressure, and the process conditions can result in defects (wrinkles, folds) that are known to be detrimental to composite bond strength and can lead to premature component failure. This award supports fundamental research into defect formation that will enable better process simulations, which will enhance composite part quality by providing guidelines for the ATP manufacturing process. Knowledge developed regarding process-material interactions can significantly decrease manufacturing defects, which should reduce manufacturing cycle time, as well as expensive and time-intensive manual inspection that increases manufacturing cost. Improved understanding of fundamental defect deformation mechanisms will enable development of emerging additive manufacturing processes for fiber composites and ATP processing for complex geometries with curved paths in lightweight composite structures. A specific effort will be made to recruit graduate and undergraduate students from under-represented groups through the Society for Women Engineers at the University of South Carolina. In addition, a module on ATP manufacturing and process simulations developed in this research will be incorporated into the Composites Manufacturing course for undergraduate and graduate students to provide knowledge on the governing process-structure-property links for design and manufacturing.Improved understanding of the fundamentals of adhesion-driven manufacturing can improve part quality while reducing the overall cost associated with re-work or remanufacturing of defective components. This research aims to fill knowledge gaps by investigating the fundamentals of how in-situ bond strength-toughness develops and how defects form during contact and adhesion of uncured, polymer-rich materials. In particular, the time-temperature superposition principle at characteristic millisecond time scales will be applied using parameters gleaned from novel cohesion experiments and high speed stereo digital image correlation. In parallel, a multi-physics process model will be developed to study how process parameters affect defect formation, with data-driven machine learning techniques employed to establish defect-free process parameter windows and provide guidance for enhanced manufacturing processes. The new fundamental knowledge will enable the establishment of processing parameter windows for defect-free tow placement and will serve as a guide to enhance composite part quality.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.

自动丝束铺放 (ATP) 是一种粘合驱动的聚合物基复合材料制造工艺，其中将一束用树脂浸渍的纤维（称为丝束）放置在工具或先前放置的丝束上。该技术被广泛使用。 ATP 工艺需要施加温度和压力，而工艺条件可能会导致缺陷，因此它在航空航天工业中也引起了人们的兴趣，因为它具有提高生产率和减少材料浪费的潜力。已知对复合材料粘合强度不利并可能导致组件过早失效的缺陷（皱纹、折叠）该奖项支持对缺陷形成的基础研究，从而实现更好的工艺模拟，从而为 ATP 提供指导，从而提高复合材料零件的质量。关于工艺与材料相互作用的知识可以显着减少制造缺陷，从而减少制造周期时间，以及增加制造成本的昂贵且耗时的手动检查，这将有助于开发新兴的缺陷变形机制。纤维复合材料的增材制造工艺和轻质复合材料结构中具有弯曲路径的复杂几何形状的 ATP 处理将通过南卡罗来纳大学女性工程师协会专门招募研究生和本科生。本研究中开发的制造和工艺模拟将纳入本科生和研究生的复合材料制造课程中，以提供有关设计和制造的控制工艺-结构-性能链接的知识。加深对粘合驱动制造基础知识的理解提高零件质量，同时降低与缺陷部件返工或再制造相关的总体成本。本研究旨在通过研究原位粘合强度-韧性如何发展以及在未固化的接触和粘合过程中如何形成缺陷的基础知识来填补知识空白。特别是，将使用从新颖的内聚实验和高速立体数字图像相关性中收集的参数来应用特征毫秒时间尺度的时间-温度叠加原理。将开发多物理场工艺模型来研究工艺参数如何影响缺陷形成，并采用数据驱动的机器学习技术来建立无缺陷工艺参数窗口并为增强的制造工艺提供指导。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。