SNM: Manufacturing Autonomy for Directed Evolution of Materials (MADE-Materials) for Robust, Scalable Nanomanufacturing

SNM：材料定向进化（MADE-Materials）的制造自主权，实现稳健、可扩展的纳米制造

基本信息

批准号：
1727894
负责人：
David Hoelzle
金额：
$ 149.47万
依托单位：
Ohio State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-01 至 2022-11-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1727894&HistoricalAwards=false
关键词：
SNM Manufacturing Autonomy Directed Evolution

项目摘要

The development and manufacturing of cutting edge materials typically involves time-consuming materials and process design phases, followed by extensive testing of samples to adjust process conditions as the manufacturing scales up from the lab, to pilot plan, to industrial process scale. These distinct steps drive up the cost barrier to introduction of new and improved materials into the industrial pipeline and increase the cost of domestically manufactured advanced nanomaterials. Fortunately, recent developments in numerical modeling, additive manufacturing, and rapid testing of materials suggest that a new approach to material development and nanomanufacturing, where the previously distinct and time-consuming phases could be carried out nearly instantaneously to arrive at optimal material structure as well as process conditions for its manufacture. The focus of this award is to revamp the traditional, open-loop synthesis of nanostructured materials by: 1) using a versatile 3-D printing approach to manufacture these nanomaterials and nanostructures, 2) incorporate material property characterization directly into the printing process, and 3) use an artificial intelligence (AI) algorithm to adjust on the fly process conditions to achieve desired material properties. These concepts and components will be integrated into technical coursework, hands-on research opportunities, and outreach workshops to a broad range of students and the public. The co-PIs plan to leverage existing outreach and educational activities through their group's collaboration with a local museum, as well as curricular and extracurricular activities. The approach and framework is an investigation of process modeling, materials synthesis and characterization, and system design to autonomously discover new material configurations and reduce manufacturing defects and uncertainty. This AI framework will "understand" process-structure-property relationships, manufacturing constraints, and, importantly, statistical variations in material properties and manufacturing quality. The intellectual merit of this study is the discovery of general nanomanufacturing tools and feedstocks, with supervisory genetic algorithms, that autonomously correct for defects and compensate for innate manufacturing inaccuracies by a search for alternative designs; this is in contrast to standard tools that minimize uncertainty (e.g. environmental controls) or rely on post-fabrication characterization with human intervention. The framework will be tested using nanoscale additive manufacturing (AM) as the fundamental manufacturing tool and nanostructured metamaterials as the application. The paradigm and nanoscale metamaterials made via this approach have far-reaching impacts on scalable nanomanufacturing for integrated systems. The paradigm of systems that autonomously evolve parameters to meet construct specifications is extensible to macroscale additive manufacturing and pharmaceuticals where the process parameter space and chemistries available is vast, and design is not intuitive. Additive nanomanufacturing has the potential to transform metamaterial design by enabling design in 3-dimensions (3D) with multiple materials, creating complex composite metastructures.

尖端材料的开发和制造通常涉及耗时的材料和工艺设计阶段，然后随着制造规模从实验室扩大到试点计划，再到工业工艺规模，对样品进行广泛的测试以调整工艺条件。这些独特的步骤提高了将新的和改进的材料引入工业管道的成本障碍，并增加了国内制造的先进纳米材料的成本。幸运的是，数值建模、增材制造和材料快速测试方面的最新发展表明，材料开发和纳米制造的新方法，以前的独特且耗时的阶段几乎可以立即进行，以达到最佳的材料结构作为其制造的工艺条件。该奖项的重点是通过以下方式改进纳米结构材料的传统开环合成：1) 使用多功能 3D 打印方法来制造这些纳米材料和纳米结构，2) 将材料特性表征直接纳入打印过程，以及3) 使用人工智能 (AI) 算法动态调整工艺条件，以实现所需的材料特性。这些概念和组成部分将融入到面向广大学生和公众的技术课程、实践研究机会和外展研讨会中。联合PI计划通过其小组与当地博物馆的合作以及课程和课外活动来利用现有的外展和教育活动。该方法和框架是对工艺建模、材料合成和表征以及系统设计的研究，以自主发现新材料配置并减少制造缺陷和不确定性。该人工智能框架将“理解”工艺-结构-属性关系、制造约束，以及重要的是材料属性和制造质量的统计变化。这项研究的智力价值是发现了通用纳米制造工具和原料，具有监督遗传算法，可以通过寻找替代设计自动纠正缺陷并补偿固有的制造误差；这与最小化不确定性（例如环境控制）或依赖人工干预的制造后表征的标准工具形成鲜明对比。该框架将使用纳米级增材制造（AM）作为基本制造工具和纳米结构超材料作为应用进行测试。通过这种方法制造的范式和纳米级超材料对集成系统的可扩展纳米制造具有深远的影响。自主演化参数以满足构造规范的系统范例可以扩展到宏观增材制造和制药，其中可用的工艺参数空间和化学物质巨大，并且设计不直观。增材纳米制造有潜力通过多种材料进行 3 维 (3D) 设计，创建复杂的复合元结构，从而改变超材料设计。

项目成果

期刊论文数量（15）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Polymeric Photonic Crystal Fibers for Textile Tracing and Sorting

用于纺织品追踪和分类的聚合物光子晶体纤维

DOI：
10.1002/admt.202201099
发表时间：
2023
期刊：
Advanced Materials Technologies
影响因子：
6.8
作者：
Iezzi, Brian;Coon, Austin;Cantley, Lauren;Perkins, Bradford;Doran, Erin;Wang, Tairan;Rothschild, Mordechai;Shtein, Max
通讯作者：
Shtein, Max

Higher-Order Spatial Iterative Learning Control for Additive Manufacturing