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

机械梯度软材料(MGSM)是性能变化平稳的柔顺体系。MGSM在自然界中被观察为无缝附着在骨骼上的僵硬韧带，或者水生生物的坚硬贝壳或喙。机器学习的最新进展是为工程目的定制MGSM的设计，范围从软机器人到冲击吸收和生物医学设备。然而，证明“我们计划的就是我们建造的”取决于我们观察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(CALEAR)奖支持基础研究，以建立一种方法，该方法将导致并量化设计的软材料内的三维变形。知情测试将减少确定材料的机械性能如何变化所需的时间，加快表征效率和一般个性化软材料的反馈循环。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代表不足的群体的教育结果，并扩大他们的参与。用于可靠地识别经历大变形的材料的空间非均质性的单一测试程序尚未开发出来。以前，磁共振地图学一直被用作软材料的表征方法，没有内部对比，但目前该方法仅限于均匀材料的适度变形。这项研究的目的是利用连续介质力学理论和正向有限元模拟来确定材料性能的空间变化，以便为试验提供实验边界条件。此外，这些参数的可辨识性的可信度将首次通过实验良好性度量进行评估。这项研究的具体目的是：(1)驱动和测量峰值应变幅值新高于1的MGSM的三维应变场；(2)使用正交应变不变量和虚场方法评估变形状态的有用性；(3)确定运动学数据的丰富性和噪声如何定量地改变感兴趣的整套本构参数的可识别性。知情测试的原则还将应用于使用课程公平数据的力学课堂中的驾驶评估策略，并将支持基于PrairieLearn的个性化力学掌握平台。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.