An integrated multi-scale approach to modeling and optimization of forming-induced wrinkles in woven fabric composites

机织织物增强聚合物(WFRP)作为一种特殊的纺织结构复合材料，由于其易于成型、轻质和高强度等优点，在航空航天、汽车、建筑、船舶、国防等主要行业得到了广泛的关注。尽管有了广泛的兴趣，但在最佳制造条件下仍然缺乏知识和标准化，应该应用于WFRP以避免主要缺陷，如面内和/或面外皱纹。目前的技术水平表明，为了正确地捕捉和减少WFRP零件中的皱纹，需要一种集成的设计和制造方法，从而可以考虑从悬垂阶段到最终树脂固化阶段(即在加工条件下)的皱纹演变和残余应力的形成。为了解决建模的高度复杂性，以及工业对捕捉高保真和有效的缺陷缓解策略的高需求，NSERC Discovery研究计划旨在开发一种新的集成的多物理、多尺度的模拟和优化方法，用于WFRP中由覆盖和加工引起的皱纹。为了增加拟议的织物加工模型的可靠性，还将通过与织物微观结构的X射线微层析分析相联系的稳健的实验设计技术来考虑不确定性影响。 这项研究计划的主要有形成果将是：(I)加深我们对不同织物特性、成型条件和部件形状下导致WFRP中起皱的多尺度机理的基本了解；(Ii)开发一种新的机器学习和优化工具，可帮助工业设计师就具有成本效益和高效的抗皱策略提出最终建议；以及(Iii)培训一批具有高级表征、建模、成形模拟和优化方面能力的高素质人员(HQP)。一旦建立和验证，拟议的集成方法以及计算机工具预计将被合作的加拿大公司在复合材料研究网络(CRN)广泛使用，并解决制造商因随机试验和错误而承受的过程开发间接成本(目前高达70%)。

Woven Fabric-Reinforced Polymers (WFRP), as a special class of Textile Structural Composites, have found considerable attention in several leading industries including aerospace, automotive, construction, marine, and defense, owing to their ease of formability along with a combination of light weight and high strength. Despite this wide interest, there still exists a lack of knowledge and standardization in optimum manufacturing conditions that should be applied on WFRP to avoid major defect such as in-plane and/or out-of-plane wrinkles. The current state-of-the-art shows that to correctly capture and mitigate wrinkles in WFRP parts, an integrated design and manufacturing approach is required, whereby the evolution of wrinkles from the draping stages all through to the final resin curing stages (i.e. under processing conditions) and residual stress formations can be accounted for. To address the high degree of complexity in modeling, as well as the high industrial demand for capturing high-fidelity and effective defect mitigation strategies, this NSERC Discovery research program aims at developing a new integrated multi-physics, multi-scale simulation and optimization approach for draping- and processing-induced wrinkles in WFRP. In order to add to the reliability of the proposed fabric processing models, uncertainty effects will also be accounted via robust design-of-experiment techniques linked to X-ray micro-tomography analysis of fabrics microstructure. The main tangible outcomes of this research program will be (i) enhancing our fundamental understanding of multi-scale mechanisms contributing to the formation of wrinkles in WFRP under different fabric properties, forming conditions and part shapes; (ii) developing a new machine-learning and optimization tool that can assist industrial designers to make final recommendations toward cost-effective and efficient wrinkling mitigation strategies, and (iii) training a number of highly qualified personnel (HQP) with competencies in advanced characterization, modeling, forming simulation and optimization of textile composites. Once established and validated, the proposed integrated approach along with the computer tools are expected to be widely used by the collaborating Canadian companies at the Composites Research Network (CRN), and address the process development overhead costs (currently as high as 70%) endured by manufacturers due to random trial and errors.