卷积神经网络用于估计从小冲击测试中单轴拉伸测试等效性能
Maciej Kaliciak1, Tadeusz Uhl2, Marek Nowak3
1Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, 30-059 Krakow, Poland.
Materials (Basel, Switzerland)
|December 11, 2025
概括
神经网络可以从小型冲击测试 (SPT) 数据准确预测单轴拉伸测试 (UTT) 属性. 这种深度学习方法克服了SPT偏见,使结构钢的可靠材料属性评估成为可能.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算智能是一种计算智能.
背景情况:
- 小冲击测试 (SPT) 是一种有价值的技术,用于使用最小样本评估结构材料的机械性能.
- 然而,SPT测量通常含有系统偏差,导致对单轴拉伸测试 (UTT) 属性的不准确估计.
- 在准确地将SPT数据翻译成UTT等价材料行为方面存在方法上的差距.
研究的目的:
- 研究神经网络在从SPT测量中预测UTT等效行为的有效性.
- 解决和减少与SPT方法相关的固有系统偏差.
- 建立可靠的方法来自动评估结构钢的性能.
主要方法:
- 创建了一个实验数据库,将三种炉钢 (10H2M,13HMF,15HM) 的 SPT 和 UTT 数据配对在新的和退化服务条件下.
- 卷积神经网络 (CNN) 用实验数据集进行了训练和评估,以进行曲线对曲线的预测.
- 该研究的重点是CNN利用局部曲线特征来减少偏差的能力.
主要成果:
- 美国有线电视新闻网 (CNN) 模型在从SPT数据中预测UTT等价性质方面表现出了强度.
- 这些发现证实,CNN可以有效地减少SPT测量中固有的系统偏差.
- 来自CNN的UTT物业估计的精度与传统的UTT测量相当.
结论:
- 卷积神经网络显示了将SPT数据转换为UTT等价材料属性的巨大潜力.
- 这种深度学习方法成功地弥合了SPT和UTT测量之间的方法差距.
- 该研究通过提供可靠的数据翻译方法来支持使用中的结构钢的自动评估.
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