STTR Phase I: Manufacturing of Enhanced Composites via Interlaminar Incorporation of CNT/Epoxy Nanoscaffolds using Genetic Algorithm Assisted Machine Learning and Neural Networks

STTR 第一阶段：使用遗传算法辅助机器学习和神经网络，通过 CNT/环氧树脂纳米支架的层间结合制造增强复合材料

• 批准号: 2036490
• 负责人: Asel Habarakada Liyanage
• 金额: $ 25.6万
• 依托单位: MULTISCALE INTEGRATED TECHNOLOGY SOLUTIONS LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-15 至 2022-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2036490&HistoricalAwards=false
• 关键词: STTR; Phase; Manufacturing; Enhanced; Composites

The broader impact/commercial potential of this Small Business Technology Transfer Program (STTR) Phase I project is to improve strength and stiffness of carbon fiber reinforced composites in aerospace, automotive, energy, and other applications. Carbon fiber reinforced polymer (CFRP) materials are used to reduce weight without compromising the strength of structural components, but because they are built layer by layer, they have certain weaknesses. This project will advance a new approach to to enable production of higher performance automobiles, airplanes, turbines and more. The process can be highly automated, cost-effective, and seamlessly integrated with existing CFRP manufacturing. This can be integrated into existing processes. The continued development of stronger and lighter composite parts will also have an impact in related fields including renewable energy resources. The technology holds the promise of reducing operating costs, improving fuel efficiency, and decreasing emissions. This will lead to significant impacts on national security in advanced manufacturing.The strong technical innovation in the project is identifying the use of electrospinning to coat a prepreg CFRP roll with carbon nanotube (CNT)/epoxy nanofilaments that can form a CNT-reinforced bonding surface that is square meters in area and just tens of microns in thickness. This project will address the longstanding weak through-thickness interlaminar strength of CFRP prepreg laminates using a low materials-cost and highly automated process. The project will expand on the primary innovation to develop a theoretical understanding of multiscale nanoscaffold-enhanced CFRP advanced composites through genetic algorithm assisted machine learning (GAML) and artificial neural networks (ANN). The objective is to establish an experimentally and computationally supported GAML framework for determining optimal processing parameters and structural features. Results will lead to better optimization tools in the design of new composite materials, and the development of instrumentation supporting the emergence of new weight and cost saving opportunities in the composite industry for complex systems.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.

该小型企业技术转移计划（STTR）I阶段项目的更广泛的影响/商业潜力是提高航空航天，汽车，能源和其他应用中碳纤维增强复合材料的强度和刚度。碳纤维增强聚合物（CFRP）材料用于减轻体重而不损害结构成分的强度，但是由于它们是逐层建造的，因此它们具有某些弱点。 该项目将推进一种新的方法，以实现高性能汽车，飞机，涡轮机等。该过程可以高度自动化，具有成本效益，并且可以与现有的CFRP制造无缝集成。这可以集成到现有过程中。持续的更强和更轻的复合零件的开发也将在包括可再生能源在内的相关领域产生影响。 该技术具有降低运营成本，提高燃油效率并降低排放的希望。这将导致对先进制造业国家安全的重大影响。该项目的强大技术创新是确定使用静电纺丝用碳纳米管（CNT）/环氧纳米丝涂上预处理CFRP卷，这些纳米丝可以形成CNT强化的粘合表面，该表面是厚度和厚度的平方米平方米，厚度仅是平方米的。该项目将使用低材料成本和高度自动化的过程来解决CFRP预处理层压层次层次的长期弱化层间强度。该项目将扩大主要创新，以通过遗传算法辅助机器学习（GAML）和人工神经网络（ANN）对多尺度增强的CFRP高级复合材料（ANN）发展理论理解。目的是建立一个实验和计算支持的GAML框架，以确定最佳处理参数和结构特征。结果将在设计新复合材料的设计中提供更好的优化工具，并开发支持复杂系统复合行业中新的体重和成本节省机会的仪器的开发。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知识分子优点和更广泛影响的审查标准来通过评估来支持的。

项目成果

Engineering the electrospinning of MWCNTs/epoxy nanofiber scaffolds to enhance physical and mechanical properties of CFRPs

• DOI: 10.1016/j.compscitech.2021.108941
• 发表时间: 2021-07-10
• 期刊: COMPOSITES SCIENCE AND TECHNOLOGY
• 影响因子: 9.1
• 作者: Wable, Vidya;Biswas, Pias Kumar;Dalir, Hamid
• 通讯作者: Dalir, Hamid

Higher strength carbon fiber lithium‐ion polymer battery embedded multifunctional composites for structural applications

• DOI: 10.1002/pc.26589
• 发表时间: 2022-03
• 期刊: Polymer Composites
• 影响因子: 5.2
• 作者: P. Biswas;Asel Habarakada Liyanage;Mayur M. Jadhav;Mangilal Agarwal;H. Dalir