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.

该项目将使用实验研究创建新的计算能力，以了解3D印刷聚合物复合材料中的断裂和故障。 3D打印正在从指示性原型过渡到影响广泛工业领域的功能产品。但是，许多基于聚合物的3D印刷零件容易裂缝和故障。这限制了他们在承载组件中的应用。正在开发以颗粒和/或纤维增强的各种聚合物复合丝，以改善3D印刷组件的性能。当前的研究和开发受到3D打印的复杂变化的阻碍。因此，它在很大程度上仍然处于反复试验阶段，科学指导不足。该项目将制定基于科学的策略，该策略将计算建模和模拟与最佳实验套件相结合。这种方法有助于获得对多尺度骨折的基本了解，并量化与3D印刷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用于3D打印高性能组成部分的新技术。这项研究的结果可以应用于各种各样的行业。这项研究将得到教育和外展活动的补充。其中包括课程增强功能，动手3D打印研讨会以及与K-12和代表性不足的少数族裔学生相关的STEM教育计划。该项目将面临量化过程结构实现性能 - 绩效关系的挑战，并得出多尺度上的裂缝机制，以添加多块裂缝机制。尽管增材制造能够打印具有相对复杂的几何形状的零件，但必须在AM促进生产功能复合材料之前解决一些基本问题。当前的局限性包括由于制造过程中引起的强热梯度，异质界面粘结条件以及较大的断裂和故障性能变化而引起的微观结构缺陷。因此，该项目的研究目标包括：1）开发能够预测添加剂制造过程中能够预测热机械化学耦合和流体结构相互作用的直接介质模拟，这将解决有关运动和变形，温度梯度，温度梯度，融化/凝固的基本问题，与细丝/固化之间的融化/固化与互联网之间的融合/固化与另一种相关的互动， 2）基于机器学习微裂缝模拟和宏观裂缝预测的相位场模型的裂缝模型，并对制造过程的原位监视以及用于直接模型验证的多尺度实验表征； 3）开发一种基于最佳模型的不确定性量化协议，该协议组织了计算和实验活动，以验证模型，研究参数敏感性并量化过程/属性变化。研究成果将促进对添加剂制造过程参数和断裂行为之间复杂相互作用的基本知识。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的评估审查标准来通过评估来获得支持的。