Integrated Multiscale Computational and Experimental Investigations on Fracture of Additively Manufactured Polymer Composites

增材制造聚合物复合材料断裂的综合多尺度计算和实验研究

• 批准号: 2309845
• 负责人: Jun Li
• 金额: $ 40.53万
• 依托单位: University of Massachusetts, Dartmouth
• 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: 2023-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2309845&HistoricalAwards=false
• 关键词: Integrated; Multiscale; Computational; Experimental; Investigations

This project will create new computational capabilities using experimental investigations to understand fracture and failure in 3D printed polymer composites. 3D printing is transitioning from demonstrative prototypes to functional products that impact a wide range of industrial sectors. However, many polymer-based 3D printed parts are prone to fracture and failure. This limits their applications in load-bearing components. Various polymer composite filaments reinforced with particles and/or fibers are being developed to improve the performance of 3D printed components. The current research and development are hindered by the complex variabilities of 3D printing. It thus largely remains in a trial-and-error stage with insufficient scientific guidance. This project will develop a science-based strategy that combines computational modeling and simulations with an optimal suite of experiments. This approach helps to gain a fundamental understanding of multiscale fracture as well as to quantify uncertainties associated with 3D printed polymer composites. The new knowledge achieved through this research can develop new technologies for 3D printing of high-performance components. The outcomes of this research can be applied to a broad array of industries. The research will be complemented by educational and outreach activities. These include curriculum enhancements, hands-on 3D printing workshops, and STEM education programs that engage K-12 and underrepresented minority students.This project will take on the challenges of quantifying the process-structure-property-performance relationship and deriving multiscale fracture mechanics mechanisms for additively manufactured polymer composites. Although additive manufacturing is capable of printing parts with relatively complex geometries, several fundamental issues must be addressed before AM can advance to producing functional composites. Current limitations include microstructural defects due to strong thermal gradients induced during manufacturing, heterogeneous interface bonding conditions, and large fracture and failure performance variations. The research objectives of this project thus include: 1) developing direct mesoscale simulations capable of predicting thermo-mechanical-chemical coupling and fluid-structure interactions during the additive manufacturing process, which will address fundamental questions of how motions and deformations, temperature gradients, melting/solidification between filaments and reinforced particles/fibers interplay with one other in assocoation with micro-crack nucleation and propagation; 2) deriving multiscale modeling of fracture based on machine learning of micro-crack simulations and phase-field models of macro-crack predictions, with in-situ monitoring of manufacturing processes and multiscale experimental characterizations being used for direct model validations; and 3) developing an optimal model-based uncertainty quantification protocol that organizes computational and experimental activities to validate the model, investigate parameter sensitivities, and quantify process/property variations. The research outcomes will advance fundamental knowledge of the complex interplay between additive manufacturing process parameters and fracture behaviors.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.

该项目将通过实验研究创建新的计算能力，以了解 3D 打印聚合物复合材料的断裂和失效。 3D 打印正在从示范原型过渡到影响广泛工业领域的功能性产品。然而，许多基于聚合物的 3D 打印零件容易断裂和失效。这限制了它们在承载部件中的应用。人们正在开发各种用颗粒和/或纤维增强的聚合物复合长丝，以提高 3D 打印组件的性能。目前的研究和开发受到3D打印复杂多变性的阻碍。因此，它在很大程度上仍处于试错阶段，科学指导不足。该项目将开发一种基于科学的策略，将计算建模和模拟与一套最佳实验相结合。这种方法有助于获得对多尺度断裂的基本了解，并量化与 3D 打印聚合物复合材料相关的不确定性。通过这项研究获得的新知识可以开发高性能组件 3D 打印的新技术。这项研究的成果可以应用于广泛的行业。该研究将得到教育和外展活动的补充。其中包括课程改进、实践 3D 打印研讨会以及吸引 K-12 和代表性不足的少数族裔学生的 STEM 教育计划。该项目将应对量化工艺-结构-性能关系和推导多尺度断裂力学机制的挑战用于增材制造的聚合物复合材料。尽管增材制造能够打印具有相对复杂几何形状的零件，但在增材制造能够生产功能复合材料之前，必须解决几个基本问​​题。目前的局限性包括由于制造过程中产生的强热梯度而产生的微观结构缺陷、异质界面粘合条件以及大的断裂和失效性能变化。因此，该项目的研究目标包括：1）开发能够预测增材制造过程中热-机械-化学耦合和流体-结构相互作用的直接介观模拟，这将解决运动和变形、温度梯度、熔化等基本问题。 /长丝和增强颗粒/纤维之间的凝固相互作用，与微裂纹的成核和扩展相关； 2）基于微裂纹模拟的机器学习和宏观裂纹预测的相场模型推导断裂的多尺度模型，并使用制造过程的现场监测和多尺度实验表征来进行直接模型验证； 3) 开发基于最佳模型的不确定性量化协议，组织计算和实验活动来验证模型、研究参数敏感性并量化过程/特性变化。研究成果将增进对增材制造工艺参数和断裂行为之间复杂相互作用的基础知识。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。