LEAP-HI: AI-Optimized 3D Printing of Super-Soft Materials for Personalized Sensing

LEAP-HI：人工智能优化的超软材料 3D 打印，实现个性化传感

• 批准号: 2053760
• 负责人: Baskar Ganapathysubramanian
• 金额: $ 200万
• 依托单位: Iowa State University
• 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: 2021-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2053760&HistoricalAwards=false
• 关键词: LEAP; HI; AI; Optimized; 3D

A common need in the medical community is an ability to monitor local tissue continuously, but current sensing technology lacks the personalization necessary to contour sensors for the unique anatomy of different individuals. This Leading Engineering for America's Prosperity, Health, and Infrastructure (LEAP-HI) research seeks to develop low-cost personalized sensors which can be fabricated on demand to enhance the health and well-being of Americans from all walks of life. The approach leverages fundamental research into new materials in tandem with advanced machine learning and artificial intelligence to ‘3D print’ personalized sensors with applications in health care—from prosthetics to diagnostics and therapeutics—that could impact millions of people. These advances spanning materials science, engineering, and computation will improve the economic competitiveness of the United States’ innovation and help train the next generation of scientists and engineers through a tight synergy between experimental and computational research. A series of initiatives spanning diversity, education, and outreach to further will advance key aspects of the work across age groups. This includes (a) modules on cybermanufacturing in a new cyber physical systems minor at Iowa State University, (b) working with the local chapters of Society for Advancement of Chicanos/Hispanics, Native Americans in Science, and the National Society of Black Engineers to draw students into research, (c) K-12 engagement through activities at the Wolf Museum of Exploration and Innovation (MOXI) for children, (d) working with Center for Industrial Research and Service (CIRAS) to disseminate best practices and training modules in cyber manufacturing, and (e) working with the communications offices of both the University of California at Santa Barbara and Iowa State University to disseminate the research to the public to promote the need for Engineering Leadership in the United States.This research will overcome the limitations of conventional sensor technologies for personalized health monitoring by developing new materials and processing techniques to 3D print polymers with mechanical properties that are matched to human tissue. Our strategy will yield fundamental insights into the translation of advanced materials to manufacturing by: (1) Designing and synthesizing materials that inherently provide processability for 3D printing while maintaining biocompatibility and mechanical function; (2) Advanced multi-scale simulations of the printing process through modeling and predictive dynamics at the device scale to understand macroscopic progression during the build process, and at the microstructure scale to tailor the material properties as a function of material deposition and curing; (3) Data fusion from sensor response and physics-aware machine learning models to enable real-time prediction and control of the 3D printing process; and (4) Creating ultra-soft structured sensors that enable unique sensing modalities for societal impact in personalized human health. These research aims to harness the data revolution by exploiting computational and machine-learning models to design more effective polymers and sensors in a convergent fashion. This research will yield experimental and computational methodologies that can also be applied to other engineering disciplines.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.

医学界的一个共同需求是能够连续监测局部组织，但当前的传感技术缺乏针对不同个体的独特解剖结构绘制传感器轮廓所需的个性化功能。这项美国繁荣、健康和基础设施领先工程 (LEAP-HI) 研究旨在开发低成本的个性化传感器，这些传感器可以按需制造，以提高美国各行各业的健康和福祉。该方法利用对新材料的基础研究，结合先进的机器学习和人工智能，“3D 打印”个性化传感器，并应用于医疗保健领域（从假肢到诊断和治疗），这可能会影响数百万人。这些跨越材料科学、工程和计算的进步将提高美国创新的经济竞争力，并通过实验和计算研究之间的紧密协同帮助培养下一代科学家和工程师。一系列涵盖多样性、教育和推广的举措将进一步推进跨年龄组工作的关键方面。这包括 (a) 爱荷华州立大学新的网络物理系统辅修课程中的网络制造模块，(b) 与奇卡诺/西班牙裔进步协会、美洲原住民科学协会和国家黑人工程师协会的当地分会合作，吸引学生参与研究，(c) 通过沃尔夫探索与创新博物馆 (MOXI) 儿童活动参与 K-12 项目，(d) 与工业研究和服务中心合作 (CIRAS) 传播网络制造方面的最佳实践和培训模块，以及 (e) 与加州大学圣塔芭芭拉分校和爱荷华州立大学的通信办公室合作，向公众传播研究成果，以促进美国对工程领导力的需求。这项研究将通过开发新材料和加工技术，以机械方式 3D 打印聚合物，从而克服传统传感器技术在个性化健康监测方面的局限性。 与人体组织相匹配的特性。我们的战略将通过以下方式为先进材料向制造的转化提供基本见解：(1) 设计和合成材料，这些材料本质上为 3D 打印提供可加工性，同时保持生物相容性和机械功能； (2) 通过在设备尺度上建模和预测动力学，对打印过程进行高级多尺度模拟，以了解构建过程中的宏观进展，并在微观结构尺度上根据材料沉积和固化来定制材料特性； (3) 传感器响应和物理感知机器学习模型的数据融合，实现 3D 打印过程的实时预测和控制； (4) 创建超软结构传感器，实现独特的传感模式，对个性化人类健康产生社会影响。这些研究旨在通过利用计算和机器学习模型来利用数据革命，以融合的方式设计更有效的聚合物和传感器。这项研究将产生也可应用于其他工程学科的实验和计算方法。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。

项目成果

Scalable adaptive algorithms for next-generation multiphase flow simulations

• DOI: 10.1109/ipdps54959.2023.00065
• 发表时间: 2022-09
• 期刊: 2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
• 作者: K. Saurabh;Masado Ishii;Makrand A. Khanwale;H. Sundar;B. Ganapathysubramanian

Distributed Multigrid Neural Solvers on Megavoxel Domains

• DOI: 10.1145/3458817.3476218
• 发表时间: 2021-04
• 期刊: SC21: International Conference for High Performance Computing, Networking, Storage and Analysis
• 作者: Aditya Balu;Sergio Botelho;Biswajit Khara;Vinay Rao;C. Hegde;S. Sarkar;Santi S. Adavani;A. Krishnamurthy;B. Ganapathysubramanian

Triboelectric Nanogenerators: Enhancing Performance by Increasing the Charge-Generating Layer Compressibility

• DOI: 10.1021/acsmacrolett.2c00535
• 发表时间: 2022-11-15
• 期刊: ACS MACRO LETTERS
• 影响因子: 7.015
• 作者: Jang, Junho;Choi, Chungryong;Kim, Jin Kon
• 通讯作者: Kim, Jin Kon

Differentiable Spline Approximations

• 发表时间: 2021-10
• 期刊: ArXiv
• 作者: Minsu Cho;Aditya Balu;Ameya Joshi;Anjana Prasad;Biswajit Khara;S. Sarkar;B. Ganapathysubramanian;A. Krishnamurthy;C. Hegde

NURBS-Diff: A Differentiable Programming Module for NURBS

NURBS-Diff：NURBS 的可微分编程模块

• DOI: 10.1016/j.cad.2022.103199
• 发表时间: 2022
• 期刊: Computer-Aided Design
• 影响因子: 4.3
• 作者: Deva Prasad, Anjana;Balu, Aditya;Shah, Harshil;Sarkar, Soumik;Hegde, Chinmay;Krishnamurthy, Adarsh
• 通讯作者: Krishnamurthy, Adarsh