Evolutionary Inverse Material Identification: Bespoke Characterization of Soft Materials Using a Metaheuristic Algorithm.
Evolutionary Inverse Material Identification: Bespoke Characterization of Soft Materials Using a Metaheuristic Algorithm.
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DOI：
10.3389/frobt.2021.790571
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发表时间：
2021
影响因子：
3.4
通讯作者：
Valdastri P
中科院分区：
文献类型：
--
作者：
Di Lecce M;Onaizah O;Lloyd P;Chandler JH;Valdastri P
对软机器人技术的兴趣日益增长，导致对精确可靠的材料建模的需求增加。由于软机器人经历高变形，高度非线性行为是可能的。已经提出了几种能够捕捉这种非线性行为的分析模型，然而，针对特定材料和应用准确校准它们可能具有挑战性。材料表征可能需要多个实验测试台，这可能是昂贵和繁琐的。在这项工作中，我们提出了一个替代框架的参数拟合建立超弹性材料模型，其目的是提高其效用的建模软连续体机器人。我们定义了一个最小化问题，以减少实验变形的软连续体机器人和其等效的有限元模拟之间的拟合误差。使用四种常用的超弹性材料模型（Neo Hookean、Mooney-Rivlin、Yeoh和Ogden）对软材料进行表征。为了满足所定义问题的复杂性，我们使用进化算法来导航搜索空间，并确定所选材料模型和特定驱动方法的最佳参数，将这种方法命名为进化逆材料识别（EIMI）。我们通过表征该领域中常用的两种聚合物：Dragon Skin™ 10 MEDIUM和Ecoflex™ 00-50来测试所提出的方法。为了确定一组特定的模型参数的有限元模拟的优良性，我们定义了一个函数，该函数测量有限元模拟的网格与实验数据之间的距离。我们的表征框架表明，在不同的应变范围内的基准定义的基础上，传统的模型拟合方法相比，改善大于6%。此外，使用我们的方法获得的不同模型之间的低可变性表明对模型和应变范围选择的依赖性降低，使其非常适合于特定应用的软机器人建模。
The growing interest in soft robotics has resulted in an increased demand for accurate and reliable material modelling. As soft robots experience high deformations, highly nonlinear behavior is possible. Several analytical models that are able to capture this nonlinear behavior have been proposed, however, accurately calibrating them for specific materials and applications can be challenging. Multiple experimental testbeds may be required for material characterization which can be expensive and cumbersome. In this work, we propose an alternative framework for parameter fitting established hyperelastic material models, with the aim of improving their utility in the modelling of soft continuum robots. We define a minimization problem to reduce fitting errors between a soft continuum robot deformed experimentally and its equivalent finite element simulation. The soft material is characterized using four commonly employed hyperelastic material models (Neo Hookean; Mooney–Rivlin; Yeoh; and Ogden). To meet the complexity of the defined problem, we use an evolutionary algorithm to navigate the search space and determine optimal parameters for a selected material model and a specific actuation method, naming this approach as Evolutionary Inverse Material Identification (EIMI). We test the proposed approach with a magnetically actuated soft robot by characterizing two polymers often employed in the field: Dragon Skin™ 10 MEDIUM and Ecoflex™ 00-50. To determine the goodness of the FEM simulation for a specific set of model parameters, we define a function that measures the distance between the mesh of the FEM simulation and the experimental data. Our characterization framework showed an improvement greater than 6% compared to conventional model fitting approaches at different strain ranges based on the benchmark defined. Furthermore, the low variability across the different models obtained using our approach demonstrates reduced dependence on model and strain-range selection, making it well suited to application-specific soft robot modelling.
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影响因子：
3.4
作者：
Lloyd P;Koszowska Z;Di Lecce M;Onaizah O;Chandler JH;Valdastri P
通讯作者：
Valdastri P
影响因子：
3.4
作者：
Chandler JH;Chauhan M;Garbin N;Obstein KL;Valdastri P
通讯作者：
Valdastri P
影响因子：
7.9
作者：
Jeon S;Hoshiar AK;Kim K;Lee S;Kim E;Lee S;Kim JY;Nelson BJ;Cha HJ;Yi BJ;Choi H
通讯作者：
Choi H
DOI：
10.1016/j.jmbbm.2013.01.008
发表时间：
2013-04-01
影响因子：
3.9
作者：
Fu, Y. B.;Chui, C. K.;Teo, C. L.
通讯作者：
Teo, C. L.
影响因子：
7.9
作者：
Cianchetti, Matteo;Ranzani, Tommaso;Menciassi, Arianna
通讯作者：
Menciassi, Arianna