基于遗传算法和Drucker稳定性准则的软质材料本构模型参数优化新方法。

IF 6.4 2区 计算机科学 Q1 ROBOTICS
Soft Robotics Pub Date : 2023-12-01 Epub Date: 2023-06-23 DOI:10.1089/soro.2022.0145
Luis Cruz-Terán, Leopoldo Ruiz-Huerta, Alex Elias-Zuñiga, Oscar Martínez-Romero, Alberto Caballero-Ruiz
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引用次数: 0

摘要

对软材料开发柔性器件的兴趣日益浓厚,需要创建准确的方法来确定本构模型的参数值,以改进其建模。本文提出了一种优化本构模型参数的新方法,即利用遗传算法(GA)从单轴拉伸试验数据中获得一组解,然后利用有限元分析(FEA)软件模拟力学试验,在考虑Drucker稳定性准则的情况下找到最优解。考虑到Warner和Yeoh模型以及Rivlin的现象学理论,该方法被应用于弹性体Ecoflex 00-30。利用均方根误差(RMSE)确定模型的实验数据与预测数据之间的相关性,其中发现的参数集与实验数据非常接近,Warner模型的RMSE值为0.022 (ANSYS)和0.024 (ABAQUS),而Yeoh模型的RMSE值为0.014 (ANSYS)和0.012 (ABAQUS)。通过有限元分析发现,最佳参数值较好地反映了材料的实验特性。所提出的遗传算法不仅优化了材料参数,而且具有较高的重现性,Warner模型的平均RMSE值为0.024,Yeoh模型的平均RMSE值为0.009,满足Drucker稳定性准则。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Approach for Optimization of Soft Material Constitutive Model Parameters Based on a Genetic Algorithm and Drucker's Stability Criterion.

The growing interest in soft materials to develop flexible devices involves the need to create accurate methodologies to determine parameter values of constitutive models to improve their modeling. In this work, a novel approach for the optimization of constitutive model parameters is presented, which consists of using a genetic algorithm (GA) to obtain a set of solutions from data of uniaxial tensile tests, which are later used to simulate the mechanical test using finite element analysis (FEA) software to find an optimal solution considering Drucker's stability criterion. This approach was applied to the elastomer Ecoflex 00-30 considering the Warner and Yeoh models and Rivlin's phenomenological theory. The correlation between the experimental and the predicted data by the models was determined using the root mean squared error (RMSE), where the found parameter sets provided a close fit to the experimental data with RMSE values of 0.022 (ANSYS) and 0.024 (ABAQUS) for Warner's model, while for Yeoh's model were 0.014 (ANSYS) and 0.012 (ABAQUS). It was found that the best parameter values accurately follow the experimental material behavior using FEA. The proposed GA not only optimizes the material parameters but also has a high reproducibility level with average RMSE values of 0.024 for Warner's model and 0.009 for Yeoh's model, fulfilling Drucker's stability criterion.

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来源期刊
Soft Robotics
Soft Robotics ROBOTICS-
CiteScore
15.50
自引率
5.10%
发文量
128
期刊介绍: Soft Robotics (SoRo) stands as a premier robotics journal, showcasing top-tier, peer-reviewed research on the forefront of soft and deformable robotics. Encompassing flexible electronics, materials science, computer science, and biomechanics, it pioneers breakthroughs in robotic technology capable of safe interaction with living systems and navigating complex environments, natural or human-made. With a multidisciplinary approach, SoRo integrates advancements in biomedical engineering, biomechanics, mathematical modeling, biopolymer chemistry, computer science, and tissue engineering, offering comprehensive insights into constructing adaptable devices that can undergo significant changes in shape and size. This transformative technology finds critical applications in surgery, assistive healthcare devices, emergency search and rescue, space instrument repair, mine detection, and beyond.
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