CAREER: Informed Testing — From Full-Field Characterization of Mechanically Graded Soft Materials to Student Equity in the Classroom

职业：知情测试 – 从机械分级软材料的全场表征到课堂上的学生公平

• 批准号: 2338371
• 负责人: Jonathan Estrada
• 金额: $ 76.31万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 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: 2024-03-01 至 2029-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2338371&HistoricalAwards=false
• 关键词: CAREER; Informed; Testing; Full; Field

Mechanical gradient soft materials (MGSMs) are compliant systems with smooth variations in their properties. MGSMs are observed in nature as stiff ligaments attaching seamlessly to bone, or hard shells or beaks on aquatic creatures. Recent advances in machine learning stand to customize the design of MGSMs for engineering purposes, ranging from soft robotics to impact absorption and biomedical devices. However, certifying that “what we planned is what we built” relies on our ability to peer into these materials and test their properties as they are stretched. This Faculty Early Career Development (CAREER) award supports fundamental research to establish a method which will both cause and quantify three-dimensional deformations inside designed soft materials. Informed testing will reduce the time it takes to determine how mechanical properties vary over a material, speeding up characterization efficiency and feedback loops for generally making personalized soft materials. This research will not only promote the progress of fundamental science but will also advance national health, prosperity, and welfare. By integrating informed testing into the engineering classroom, this research will additionally improve educational outcomes for—and broaden participation of—underrepresented groups. A single test procedure for reliably identifying spatial heterogeneity for materials undergoing large deformations has not yet been developed. Previously, magnetic resonance cartography had been used as a characterization method for soft materials without internal contrast, but the method is currently restricted to moderate deformations of homogeneous materials. This research aims to permit identification of spatial variations of material properties by using continuum mechanics theory and forward finite element simulations to inform experimental boundary conditions for tests. Furthermore, confidence in the identifiability these parameters will be assessed for the first time via an experimental goodness metric. The specific aims of the research are to (1) actuate and measure fully three-dimensional strain fields of MGSMs with peak strain magnitude values newly above 1, (2) assess the usefulness of deformation states using orthogonal strain invariants and the virtual fields method, and (3) determine how kinematic data richness and noise quantifiably alter the identifiability of the complete set of constitutive parameters of interest. The principles of informed testing will additionally be applied to driving assessment strategies in mechanics classrooms using course equity data and will underpin a personalized PrairieLearn-based mechanics mastery platform.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.

机械梯度软材料（MGSM）是一种具有平滑特性变化的柔顺系统。MGSM在自然界中被观察为无缝连接到骨骼的刚性韧带，或水生生物的硬壳或喙。机器学习的最新进展有助于为工程目的定制MGSM的设计，从软机器人到冲击吸收和生物医学设备。然而，要证明“我们计划的就是我们建造的”，取决于我们研究这些材料并测试它们在拉伸时的性能的能力。该学院早期职业发展（CAREER）奖支持基础研究，以建立一种方法，该方法将导致和量化设计软材料内部的三维变形。知情的测试将减少确定材料机械性能如何变化所需的时间，加快表征效率和反馈循环，以便通常制造个性化的软材料。这项研究不仅将促进基础科学的进步，而且将促进国家的健康，繁荣和福利。通过将知情测试整合到工程课堂中，这项研究将进一步改善教育成果，并扩大代表性不足的群体的参与。一个单一的测试程序，可靠地识别材料经历大变形的空间异质性尚未开发。以前，磁共振制图已被用作没有内部对比度的软材料的表征方法，但该方法目前仅限于均匀材料的适度变形。 本研究旨在通过使用连续介质力学理论和正向有限元模拟来识别材料特性的空间变化，从而为测试提供实验边界条件。此外，这些参数的可识别性的置信度将首次通过实验优度度量进行评估。该研究的具体目的是（1）驱动和测量峰值应变幅值新高于1的MGSM的全三维应变场，（2）使用正交应变不变量和虚拟场方法评估变形状态的有用性，以及（3）确定运动学数据的丰富性和噪声如何量化地改变完整的本构参数集的可识别性。知情测试的原则将另外适用于驾驶评估策略在力学教室使用课程公平数据，并将支持一个个性化的PrairieLearn为基础的力学掌握平台。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。