Back to Search
Start Over
A deep learning dataset for metal multiaxial fatigue life prediction
- Source :
- Scientific Data, Vol 11, Iss 1, Pp 1-10 (2024)
- Publication Year :
- 2024
- Publisher :
- Nature Portfolio, 2024.
-
Abstract
- Abstract 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.
- Subjects :
- Science
Subjects
Details
- Language :
- English
- ISSN :
- 20524463
- Volume :
- 11
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- Scientific Data
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.5a42b0e90f0f4704af2ac5f3478cfe55
- Document Type :
- article
- Full Text :
- https://doi.org/10.1038/s41597-024-03862-4