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.

这个小型企业技术转移计划(STTR)第一阶段项目的更广泛影响/商业潜力是提高航空航天、汽车、能源和其他应用中的碳纤维增强复合材料的强度和刚度。碳纤维增强聚合物(CFRP)材料被用来在不影响结构部件强度的情况下减轻重量，但由于它们是逐层建造的，它们有一定的弱点。该项目将推进一种新的方法，以使更高性能的汽车、飞机、涡轮机等的生产成为可能。该过程可以高度自动化，具有成本效益，并与现有的CFRP制造无缝集成。这可以集成到现有的流程中。更坚固和更轻的复合材料部件的持续发展也将对包括可再生能源在内的相关领域产生影响。这项技术有望降低运营成本，提高燃料效率，减少排放。这将对先进制造的国家安全产生重大影响。该项目的重大技术创新是确定了使用静电纺丝在预浸料CFRP辊上涂覆碳纳米管(CNT)/环氧纳米丝，这种纳米丝可以形成面积为平方米、厚度仅为数十微米的CNT增强粘接表面。该项目将使用低材料成本和高度自动化的工艺来解决长期存在的CFRP预浸料层间强度低的问题。该项目将在主要创新的基础上，通过遗传算法辅助机器学习(GAML)和人工神经网络(ANN)发展对多尺度纳米支架增强CFRP先进复合材料的理论理解。其目标是建立一个实验和计算支持的GAML框架，以确定最佳的工艺参数和结构特征。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，认为值得支持。

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