FMSG: Cyber: Toward Future Underwater Additive Manufacturing of Bio-Based Construction Materials Through AI-Guided Sensing and Material Modeling

FMSG：网络：通过人工智能引导的传感和材料建模迈向未来生物基建筑材料的水下增材制造

• 批准号: 2328188
• 负责人: Yen-Fang Su
• 金额: $ 48.52万
• 依托单位: Louisiana State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-15 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2328188&HistoricalAwards=false
• 关键词: FMSG; Cyber; Toward; Future; Underwater

With the global rise in sea levels and frequent extreme weather conditions, effective and efficient underwater construction methods are increasingly essential for the resilience of coastal communities. However, traditional underwater construction approaches face a host of challenges, including severe working conditions, restricted access, and potential ecological damage. This Future Manufacturing Seed Grant (FMSG) funded project will explore the potential of additive manufacturing as an autonomous, advanced construction method to overcome these hurdles. Nevertheless, the complexity of implementing underwater additive manufacturing, from materials selection and process optimization to instrumentation development, presents significant challenges. The key concern is the formulation of concrete additives, typically handled via a trial-and-error method due to the regional variation of materials. Utilizing artificial intelligence-driven material modeling coupled with novel smart sensing systems, this research aims to unravel new insights to enable innovative underwater concrete additive manufacturing. This research could revolutionize underwater construction methods, fostering more efficient, sustainable, and eco-friendly solutions for coastal communities and infrastructure. The research will also be complemented by incorporating courses and outreach programs on artificial intelligence and underwater additive manufacturing topics for graduate, undergraduate, and K-12 students. Specifically, the research team will actively involve underrepresented K-12 students at fundamental project levels and inspire them to further explore STEM fields. The specific goal of this project is to decipher the intricate process-structure-property in underwater concrete additive manufacturing. This endeavor will replace traditional, tedious trial-and-error methods using molecular dynamics simulations, providing a comprehensive understanding of the physical principles governing the interactions between cementitious compositions and various chemical additives. The project will address compatibility issues and potential side effects of chemical admixtures on the cementitious system. Additionally, considering changing materials formulations and underwater environmental conditions that will affect concrete rheological properties, the project will develop a novel multi-sensor system that will be integrated into the concrete 3D printer, providing an accurate real-time monitoring approach. The team will develop an advanced data fusion methodology that merges experimental data, simulation outputs, and sensor results. The study will incorporate fluid mechanics, thermal dynamics, and domain knowledge (such as hydration curves) to build a physics-guided machine learning model. This model will offer a comprehensive understanding of the additive manufacturing process, leading to precise parameter control and improved reliability in underwater concrete additive manufacturing. The project's outcomes will advance the fundamental comprehension of the process-structure-property relationship in additive manufacturing and accelerate the technique development.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.

随着全球海平面上升和频繁的极端天气条件，有效和高效的水下施工方法对于沿海社区的恢复力越来越重要。然而，传统的水下施工方法面临着一系列挑战，包括恶劣的工作条件，限制进入和潜在的生态破坏。这个未来制造种子基金（FMSG）资助的项目将探索增材制造作为一种自主的先进建筑方法的潜力，以克服这些障碍。然而，实施水下增材制造的复杂性，从材料选择和工艺优化到仪器开发，都带来了重大挑战。关键问题是混凝土添加剂的配方，由于材料的区域差异，通常通过试错法处理。利用人工智能驱动的材料建模与新型智能传感系统，这项研究旨在揭示新的见解，以实现创新的水下混凝土增材制造。这项研究可以彻底改变水下施工方法，为沿海社区和基础设施提供更高效、可持续和生态友好的解决方案。该研究还将通过为研究生，本科生和K-12学生整合人工智能和水下增材制造主题的课程和推广计划来补充。具体而言，研究团队将积极参与基础项目水平的代表性不足的K-12学生，并激励他们进一步探索STEM领域。 该项目的具体目标是破译水下混凝土增材制造中复杂的过程-结构-性能。这一奋进将取代传统的，繁琐的试错法使用分子动力学模拟，提供了一个全面的了解的物理原理之间的相互作用水泥组合物和各种化学添加剂。该项目将解决相容性问题和化学外加剂对水泥系统的潜在副作用。此外，考虑到材料配方和水下环境条件的变化会影响混凝土的流变性能，该项目将开发一种新型的多传感器系统，该系统将集成到混凝土3D打印机中，提供准确的实时监测方法。该团队将开发一种先进的数据融合方法，将实验数据、模拟输出和传感器结果融合在一起。该研究将结合流体力学，热动力学和领域知识（如水合曲线）来构建物理指导的机器学习模型。该模型将提供对增材制造过程的全面了解，从而实现水下混凝土增材制造的精确参数控制和提高可靠性。该项目的成果将促进对增材制造中工艺-结构-性能关系的基本理解，并加速技术开发。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。