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极端天气条件频繁发生，有效且高效的水下施工方法对于沿海社区的恢复力变得越来越重要。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。这个由未来制造种子补助金 (FMSG) 资助的项目将探索增材制造作为一种自主、先进的施工方法的潜力，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、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，由于材料的地区差异，通常通过试错法来处理。这项研究利用人工智能驱动的材料建模与新颖的智能传感系统相结合，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、可持续和环保的解决方案。该研究还将通过纳入针对研究生、本科生和 K-12 学生的人工智能和水下增材制造主题的课程和外展计划来补充。具体来说，研究团队将积极让代表性不足的 K-12 学生参与基础项目层面，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工艺结构特性。这项努力将取代使用分子动力学模拟的传统、繁琐的试错方法，提供对控制水泥组合物和各种化学添加剂之间相互作用的物理原理的全面理解。该项目将解决化学外加剂对水泥体系的相容性问题和潜在副作用。此外，考虑到材料配方的变化和水下环境条件会影响混凝土的流变性能，该项目将开发一种新型多传感器系统，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，融合实验数据、模拟输出和传感器结果。该研究将结合流体力学、热动力学和领域知识（例如水合曲线）来构建物理引导的机器学习模型。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并提高可靠性。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智力价值和更广泛的影响审查标准进行评估，被认为值得支持。