A deep learning dataset for metal multiaxial fatigue life prediction

Multiaxial fatigue failure of metals, a common issue in industrial production, often leads to significant losses. Recently, many researchers have applied deep learning methods to predict the multiaxial fatigue life of metals, achieving promising results. Due to the high costs of fatigue testing, tra...

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Bibliographic Details
Published in:Scientific data 2024-09, Vol.11 (1), p.1027-10, Article 1027
Main Authors: Chen, Shuonan, Bai, Yongtao, Zhou, Xuhong, Yang, Ao
Format: Article
Language:English
Subjects:
639/166/988
639/301/1023/1026
Aluminum
Artificial intelligence
Civil engineering
Data Descriptor
Datasets
Deep learning
Humanities and Social Sciences
Information processing
Materials fatigue
Mechanical properties
Metal fatigue
Metals
multidisciplinary
Neural networks
Predictions
Science
Science (multidisciplinary)
Steel alloys
Tensile strength
Titanium alloys
Yield stress
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Description
Summary:Multiaxial fatigue failure of metals, a common issue in industrial production, often leads to significant losses. Recently, many researchers have applied deep learning methods to predict the multiaxial fatigue life of metals, achieving promising results. Due to the high costs of fatigue testing, training data for deep learning is scarce and labor-intensive to collect. This study meets this need by creating a large-scale, high-quality dataset for multiaxial fatigue life prediction, consisting of 1167 samples from 40 materials collected from literature. The dataset includes key mechanical properties (elastic modulus, yield strength, tensile strength, Poisson’s ratio) and 48 loading paths, along with additional relevant information (composition ratios, processing conditions). Common deep learning models validated the dataset’s effectiveness. This dataset aims to support researchers applying deep learning to fatigue life prediction, addressing the long-standing issue of data scarcity, thereby advancing the intersection of artificial intelligence and metal fatigue research.
ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-024-03862-4