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过程设计阶段，然后对样品进行广泛的测试，以调整过程条件，因为制造业从实验室到试点计划，再到工业过程量表。这些独特的步骤推动了将新的和改进的材料引入工业管道的成本障碍，并增加了国内生产的先进纳米材料的成本。幸运的是，数值建模，添加剂制造和材料快速测试的最新发展表明，一种新的材料开发和纳米制造方法，即几乎可以立即进行以前不同且耗时的阶段，以达到最佳的材料结构以及其制造过程的最佳过程。该奖项的重点是改善纳米结构材料的传统的开环合成：1）使用多功能的3-D打印方法来制造这些纳米材料和纳米结构，2）将材料特性直接纳入印刷过程，3）使用人工智能（AI）AlgorithM，以实现flyerity属性属性。这些概念和组成部分将纳入技术课程，动手研究机会，并向广泛的学生和公众推广讲习班。 Co-Pis计划通过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。方法和框架是对过程建模，材料合成和表征的研究，以及自主发现新材料配置并减少制造缺陷和不确定性的系统设计。该AI框架将“了解”过程结构 - 质地关系，制造约束，重要的是，材料属性和制造质量的统计变化。这项研究的智力优点是通过监督遗传算法发现了一般的纳米制造工具和原料，这些算法可以自主纠正缺陷，并通过搜索替代设计来弥补先天制造业的不准确性；这与最大程度地减少不确定性（例如环境控制）或依赖于人为干预后表征的不确定性（例如环境控制）的标准工具相反。该框架将使用纳米级添加剂制造（AM）作为基本制造工具和纳米结构的超材料作为应用程序进行测试。通过这种方法制造的范式和纳米级变质物对集成系统的可扩展纳米制造产生了深远的影响。自主进化参数以满足构造规范的系统的范式可扩展到宏观添加剂制造和药品，其中可用的工艺参数空间和化学成分很广，并且设计不直观。加性纳米制造具有通过多种材料在3维（3D）中设计的设计，从而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

Synthesis of model predictive control and iterative learning control for topography regulation in additive manufacturing