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复合结构的多尺度分析与人工神经网络支持微模型压力确定
Wacław Kuś1, Waldemar Mucha1, Iyasu Tafese Jiregna1
1Department of Computational Mechanics and Engineering, Silesian University of Technology, 44-100 Gliwice, Poland.
这项研究将机器学习应用于复合材料分析,大大减少了多尺度模拟的计算时间. 人工神经网络有效地预测异质结构中的压力,提高准确性和速度.
科学领域:
- 计算力学是计算力学.
- 材料科学是一种材料科学.
- 人工智能的人工智能是人工智能.
背景情况:
- 对复合材料而言,多尺度有限元分析至关重要.
- 宏观模型中的均质性质产生了不准确的压力预测.
- 在异质微结构中精确的应力分析是计算密集的.
研究的目的:
- 为了减少复合结构的多尺度分析的计算时间.
- 应用机器学习来有效地预测异质材料中的应力.
- 为了提高复合材料中应力定位的精度.
主要方法:
- 利用计算的多尺度方法来分析复合结构.
- 雇佣人工神经网络 (ANN),通过精心准备的数据进行训练.
- 用一个数字示例验证了该方法,该方法是用一个短的玻璃纤维增强环氧树脂.
主要成果:
- 机器学习显著减少了多尺度分析的计算时间.
- ANNs学习了宏观和微观行为之间的关系.
- 多尺度方法的效率大大提高了.
结论:
- 机器学习为加速复合材料的多尺度模拟提供了一个强大的工具.
- 这种方法提高了复杂材料结构中应力分布的预测.
- 经过验证的方法为传统耗时分析提供了更有效的替代方案.
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