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

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
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-07-01 至 2026-06-30

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中文摘要
翻译
该项目将使用实验研究来创建新的计算能力,以了解3D打印聚合物复合材料的断裂和失效。3D打印正在从示范性原型过渡到影响广泛工业部门的功能性产品。然而,许多基于聚合物的3D打印部件容易断裂和失效。这限制了它们在承载部件中的应用。正在开发用颗粒和/或纤维增强的各种聚合物复合材料长丝,以提高3D打印部件的性能。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,缺乏科学指导。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理解,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部件3D打印的新技术。这项研究的成果可以应用于广泛的行业。这项研究将辅之以教育和外联活动。这些措施包括课程改进,动手3D打印研讨会,并吸引K-12和代表性不足的少数民族学生的STEM教育计划。该项目将承担量化过程-结构-性能-性能关系的挑战,并推导增材制造聚合物复合材料的多尺度断裂力学机制。虽然增材制造能够打印具有相对复杂几何形状的部件,但在AM能够生产功能复合材料之前,必须解决几个基本问题。目前的限制包括由于制造过程中引起的强烈热梯度、异质界面结合条件以及大的断裂和失效性能变化而导致的微观结构缺陷。因此,本项目的研究目标包括:1)开发能够预测增材制造过程中的热-机械-化学耦合和流体-结构相互作用的直接介观模拟,这将解决细丝和增强颗粒/纤维之间的运动和变形、温度梯度、熔化/固化如何与微裂纹成核和扩展相互作用的基本问题; 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导出断裂的多尺度建模,其中制造过程的原位监测和多尺度实验表征用于直接模型验证;以及3)开发最佳的基于模型的不确定性量化协议,其组织计算和实验活动以验证模型,研究参数敏感性,并量化工艺/性能变化。该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
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.
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Discovery Projects - Grant ID: DP210101100
  • 批准号:
    ARC : DP210101100
  • 项目类别:
    Discovery Projects
  • 资助金额:
    $31.84万
  • 财政年份:
    2021
  • 负责人:
    Jun Li
  • 依托单位:
Explore Electrocatalysis to Improve the Cathode Performance in Li-S Batteries
  • 批准号:
    2054754
  • 项目类别:
    Standard Grant
  • 资助金额:
    $38.64万
  • 财政年份:
    2021
  • 负责人:
    Jun Li
  • 依托单位:
CIF: Small: Coding Techniques for Distributed Machine Learning
  • 批准号:
    2101388
  • 项目类别:
    Standard Grant
  • 资助金额:
    $37.23万
  • 财政年份:
    2020
  • 负责人:
    Jun Li
  • 依托单位:
Offline and Online Change-point Analysis for Large-scale Time Series Data
  • 批准号:
    1916239
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $10.0万
  • 财政年份:
    2019
  • 负责人:
    Jun Li
  • 依托单位:
海外基金