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; 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.

随着海平面上的全球增长和频繁的极端天气条件，有效，有效的水下建设方法对于沿海社区的弹性越来越重要。但是，传统的水下建设方法面临许多挑战，包括严重的工作条件，限制通道和潜在的生态损害。这项未来的制造种子赠款（FMSG）资助的项目将探索添加剂制造作为一种克服这些障碍的自主，先进的施工方法。然而，从材料选择和过程优化到仪器开发的实施水下添加剂制造的复杂性提出了重大挑战。关键问题是混凝土添加剂的制定，通常由于材料的区域变化而通过试用方法处理。该研究利用人工智能驱动的材料建模以及新型的智能传感系统，旨在揭开新的见解，以使创新的水下混凝土添加剂制造能够。这项研究可以彻底改变水下建设方法，从而为沿海社区和基础设施促进更有效，可持续和环保的解决方案。这项研究还将通过为研究生，本科和K-12学生提供有关人工智能和水下添加剂制造主题的课程和外展计划的补充。具体而言，研究团队将积极参与基本项目级别的代表性不足的K-12学生，并激发他们进一步探索STEM领域。 该项目的具体目标是破译水下混凝土添加剂制造中复杂的过程结构 - 特制。这项努力将使用分子动力学模拟取代传统的，乏味的反复试验方法，从而充分了解有关固定成分与各种化学添加剂之间相互作用的物理原理。该项目将解决化学混合物对水泥系统的兼容性问题和潜在副作用。此外，考虑不断变化的材料制剂和水下环境条件，该项目将影响具体的流变学特性，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，从而提供准确的实时监测方法。该团队将开发一种高级数据融合方法，该方法将实验数据，仿真输出和传感器结果合并。该研究将结合流体力学，热动力学和域知识（例如水合曲线），以构建物理指导的机器学习模型。该模型将对添加剂制造过程有全面的了解，从而导致精确的参数控制和提高水下混凝土添加剂制造的可靠性。该项目的成果将提高对添加剂制造过程中过程结构 - 实验关系关系的基本理解，并加速技术开发。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评估标准通过评估来获得支持的。