Data for "Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading"
Creators
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1.
Czech Technical University in Prague
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2.
Comtes Fht (Czechia)
- 3. VSB - Technical University of Ostrava
Description
Title of dataset: Data for "Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading".
Name/institution/contact information: Dr. Michal Bartošák, Czech Technical University in Prague - Faculty of Mechanical Engineering, email: michal.bartosak@fs.cvut.cz.
Date of data collection: The data were collected between 2021 and 2024.
File name structure: The data consists of two files: "316L_fatigue_and_defects.xls," which contains fatigue lifetime data and defect characteristics, and an associated description file, "read_me.txt."
See "https://doi.org/10.1016/j.ijfatigue.2024.108608" for the associated article and a detailed description of the methods.
Files
Dataset.zip
Files
(26.6 kB)
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Additional details
Related works
- Is supplement to
- Journal article: 10.1016/j.ijfatigue.2024.108608 (DOI)
Funding
- Czech Science Foundation
- Multiaxial Creep-Ratcheting and Creep-Fatigue Behaviour of High Temperature Components: A Data-Driven Approach 24-11308S
- European Union
- Center of Advanced Aerospace Technology CZ.02.1.01/0.0/0.0/16_019/0000826
- Ministry of Industry and Trade
- The institutional support of the research organisation 3/2023