一种基于遗传算法和德鲁克稳定性标准的软材料构成模型参数优化的新方法
Luis Cruz-Terán1,2,3, Leopoldo Ruiz-Huerta1,2, Alex Elias-Zuñiga2,4
1Instituto de Ciencias Aplicadas y Tecnología, Universidad Nacional Autónoma de México, Mexico City, Mexico.
Soft robotics
|June 23, 2023
概括
这项研究引入了一种新的遗传算法 (GA),以优化软材料的构成模型参数,改进灵活的设备建模. 该方法使用有限元素分析 (FEA) 准确预测材料行为,确保稳定性.
科学领域:
- 材料科学 材料科学 材料科学
- 计算力学 计算力学 计算力学
背景情况:
- 软材料对于灵活的设备至关重要,需要精确的构成模型.
- 准确的参数确定对于可靠的材料建模至关重要.
研究的目的:
- 为构成模型参数提出一种新的优化方法.
- 为了提高模拟软材料行为的准确性.
主要方法:
- 使用遗传算法 (GA) 进行参数优化.
- 采用了单轴拉伸试验数据和有限元素分析 (FEA).
- 纳入了用于解决方案验证的德鲁克稳定性标准.
主要成果:
- 在实验数据和预测数据 (低RMSE值) 之间实现了高相关性.
- 华纳的模型:RMSE 0.022 (ANSYS),0.024 (ABAQUS). 在这个模型中
- 的模型是:RMSE 0.014 (ANSYS),0.012 (ABAQUS) 的.
- 证明了高可重复性和稳定性.
结论:
- GA-FEA方法有效地优化了软材料的组成参数.
- 该方法确保准确预测材料的行为,并满足稳定性标准.
- 适用于使用华纳和Yeoh型号的Ecoflex 00-30等弹性体.
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