复合材料的疲劳寿命预测使用应变分布图像和深度卷积神经网络
Yuta Mizuno1, Atsushi Hosoi2,3, Hiroyuki Koshita4
1Department of Applied Mechanics and Aerospace Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo, 169-8555, Japan.
Scientific reports
|October 25, 2024
概括
这项研究将数字图像相关性与卷积神经网络 (CNN) 结合起来,以准确地预测短纤维增强塑料 (SFRP) 的疲劳寿命. 在视觉检查之前,CNN模型已经确定了材料的断裂点.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 人工智能的人工智能
背景情况:
- 复合材料损坏,就像短纤维增强塑料 (SFRP) 一样,是复杂的,需要准确的建模来预测疲劳寿命.
- 现有的方法可能无法完全捕捉到对于可靠的疲劳评估至关重要的复杂损伤演变过程.
研究的目的:
- 用先进的计算技术开发和验证SFRP中疲劳寿命预测的新方法.
- 研究将数字图像相关性 (DIC) 与卷积神经网络 (CNN) 结合用于损伤分析的有效性.
主要方法:
- 使用数字图像相关性 (DIC) 方法,一种非破坏性测试技术,以捕获材料变形.
- 使用一个卷积神经网络 (CNN) 与Xception架构用于疲劳寿命预测和损伤分析.
- 应用梯度加权类激活映射 (Grad-CAM) 用于模型解释性和关键损伤区域的可视化.
主要成果:
- 在相同的SFRP标本上训练和测试CNN模型时,获得了高预测准确度.
- 当使用不同的标本进行培训和测试时,观察到精度较低,这表明需要更大的数据集.
- 美国有线电视新闻网 (CNN) 的模型成功地从早期阶段确定了断裂点,作为一个关键的兴趣区域,在视觉检测之前.
结论:
- 综合的DIC-CNN方法显示出在复合材料中准确预测疲劳寿命的巨大潜力.
- CNN模型可以比传统的视觉检查更早地检测到关键损坏点,为损坏观察提供了一个新的范式.
- 建议增加样本数量的进一步研究,以提高模型的通用性和稳定性.
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