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一个深度学习数据集用于金属多轴疲劳预测寿命
Shuonan Chen1, Yongtao Bai2, Xuhong Zhou3
1School of Civil Engineering, Chongqing University, Chongqing, 400045, China.
Scientific data
|September 19, 2024
概括
本研究引入了用于金属疲劳寿命预测的大数据集,解决了深度学习应用中的数据稀缺问题. 该数据集支持人工智能驱动的进步,了解材料疲劳和故障分析.
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:
- 金属中的多轴疲劳故障造成了重大工业损失.
- 深度学习对预测金属疲劳寿命有希望,但需要广泛的训练数据.
- 目前用于疲劳测试的数据收集是昂贵和劳动密集的.
研究的目的:
- 创建一个全面的,高质量的数据集,用于多轴疲劳寿命预测.
- 减轻疲劳分析中的深度学习模型培训数据的稀缺性.
- 为了促进人工智能和金属疲劳交集的研究.
主要方法:
- 从现有文献中获取了来自40种不同材料的1167个样本的数据集.
- 包括关键的机械性能,如弹性模量,屈服强度,抗拉强度和Poisson比率.
- 整合了48个不同的加载路径和额外的数据,如组成比率和加工条件.
主要成果:
- 创建的数据集使用常用深度学习模型进行了验证,证明了其有效性.
- 该数据集为训练和测试用于疲劳寿命预测的AI模型提供了坚实的基础.
- 在金属疲劳研究领域成功应对数据稀缺的挑战.
结论:
- 开发的数据集是人工智能和金属疲劳研究人员的宝贵资源.
- 它使基于深度学习的预测能够更准确,更有效地预测多轴疲劳寿命.
- 这项工作推动了人工智能在材料科学中的应用,用于预测材料故障.
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