SBIR Phase I: Ultra Low-Cost Mechanical Metamaterials to Enable Mobility and Interactivity for Cyber-Physical Devices

SBIR 第一阶段：超低成本机械超材料，实现网络物理设备的移动性和交互性

• 批准号: 1913784
• 负责人: Jesse Silverberg
• 金额: $ 22.5万
• 依托单位: MULTISCALE SYSTEMS, INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-01 至 2020-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1913784&HistoricalAwards=false
• 关键词: SBIR; Phase; Ultra; Low; Cost

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is the development of origami-inspired mechanical metamaterials. These techniques can generate tessellated patterns that augment and enhance common materials. With the capacity for simultaneously making conventional materials lighter, stronger, and multi-functional, this material design motif has the potential to impact a wide range of technologies in hardware, manufacturing, energy-efficiency, robotics, and aerospace. This potential can be tapped by embedding carefully designed geometric patterns into materials. The current unmet challenge for realizing this potential impact is the conspicuous absence of a standard library of metamaterial designs. This project addresses the challenge by exploring the feasibility of a complete kinematic set and to determine whether the resulting physical properties are suitable for broader engineering applications. If successful, the metamaterials designed and validated by this effort will lay the foundation for replacing, augmenting, or enhancing machines of all types with mechanical metamaterial-based technology.This Small Business Innovation Research (SBIR) Phase I project utilizes an artificial intelligence-enhanced optimization scheme to automatically generate mechanical metamaterial designs meeting user-defined target properties. To generate the proposed set of kinematically-complete mechanical metamaterials the software-based approach to metamaterial design will be required to mesh with empirical validations. A combination of supervised and unsupervised machine learning techniques will be used to guarantee the metamaterial designer routinely generates useful, robust, and high-performing schematics that qualitatively and quantitatively improve as the pipeline for metamaterial design is repeatedly executed. The core algorithm improves the ability to produce high-impact metamaterial-based technology while taking less time to converge on optimized design schematics. As such, the barrier to market penetration of advanced material technology becomes lower, faster, and is driven by advances in machine learning.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.

这项小型企业创新研究（SBIR）I期项目的更广泛的影响/商业潜力是折纸启发的机械超材料的开发。 这些技术可以生成镶嵌模式，从而增强和增强常见材料。 该材料设计图案同时使传统材料同时使常规材料更轻，更强且多功能，因此有可能影响硬件，制造，能源效率，机器人技术和航空航天的广泛技术。 可以通过将精心设计的几何图案嵌入材料中来利用这种潜力。 当前意识到这种潜在影响的挑战是没有标准的超材料设计库。 该项目通过探索完整的运动学集合的可行性来应对挑战，并确定所得的物理特性是否适合更广泛的工程应用程序。 如果成功的话，通过这项工作设计和验证的超材料将奠定基础，以基于机械超材料的技术更换，增强或增强所有类型的机器。本小型企业创新研究（SBIR）I阶段项目利用人工智能增强的优化方案来自动地生成机械化的机械化材料设计属性属性。 为了生成所提出的一组运动学结合的机械超材料，将需要基于软件的超材料设计方法才能与经验验证相结合。 有监督和无监督的机器学习技术的结合将用于确保超材料设计师通常会产生有用，健壮且高性能的示意图，这些示意图在定性和定量上改进，因为超材料设计的管道被反复执行。 核心算法提高了生产高影响力超材料技术的能力，同时花费更少的时间来收集优化的设计原理图。因此，高级材料技术的市场渗透障碍变得较低，更快，并且受机器学习进展的驱动。该奖项反映了NSF的法定任务，并且使用基金会的智力优点和更广泛的影响评估标准，认为值得通过评估来获得支持。