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{
"metadata": {
"edited_by": 98,
"owner": 1410,
"_oai": {
"id": "oai:materialscloud.org:2284"
},
"description": "In this work, we present a comprehensive dataset designed to facilitate the prediction of metal fatigue life using deep learning techniques. The dataset includes detailed experimental data from 40 different metallic materials, comprising a total of 1195 data points under 48 distinct loading paths. Each data point is stored in a CSV file, capturing the loading path as a time-series with axial and tangential stress or strain values.The primary purpose of this dataset is to support the development and validation of deep learning models aimed at accurately predicting the fatigue life of metals under various loading conditions. This dataset includes stress-controlled and strain-controlled data, ensuring a broad representation of experimental scenarios. Additionally, an Excel file accompanies the dataset, providing detailed mechanical properties of each material, such as elastic modulus, tensile strength, yield strength, and Poisson's ratio, along with references to the original experimental sources.This dataset is intended for researchers in materials science and mechanical engineering, offering a robust foundation for training and testing deep learning algorithms in fatigue analysis. By making this dataset publicly available, we aim to foster collaboration and further advancements in the field of metal fatigue prediction. Researchers are encouraged to utilize and contribute to the dataset, thereby enhancing its scope and applicability.",
"mcid": "2024.119",
"id": "2284",
"license": "Creative Commons Attribution 4.0 International",
"license_addendum": null,
"references": [
{
"citation": "Chen, S., Bai, Y., Zhou, X., Yang, A., Scientific Data, in preparation",
"type": "Journal reference",
"comment": "Paper where the data is discussed"
},
{
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"type": "Journal reference",
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"comment": "Source of the collected data",
"doi": "10.1111/j.1460-2695.2004.00740.x"
},
{
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"doi": "10.1016/j.ijfatigue.2005.05.014"
},
{
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"type": "Journal reference",
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"comment": "Source of the collected data",
"doi": "10.1016/j.ijfatigue.2019.105202"
},
{
"citation": "Feng, E. S., Wang, X. G., & Jiang, C. International Journal of Fatigue, 122, 1-8 (2019)",
"type": "Journal reference",
"url": "https://doi.org/10.1016/j.ijfatigue.2019.01.003",
"comment": "Source of the collected data",
"doi": "10.1016/j.ijfatigue.2019.01.003"
},
{
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"type": "Website",
"url": "https://ntrs.nasa.gov/citations/19880015632",
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},
{
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"type": "Journal reference",
"url": "https://doi.org/10.1016/j.ijpvp.2021.104393",
"comment": "Source of the collected data",
"doi": "10.1016/j.ijpvp.2021.104393"
},
{
"citation": "Karolczuk, A., Kluger, K., & Palin-Luc, T. International Journal of Fatigue, 147, 106174 (2021)",
"type": "Journal reference",
"url": "https://doi.org/10.1016/j.ijfatigue.2021.106174",
"comment": "Source of the collected data",
"doi": "10.1016/j.ijfatigue.2021.106174"
},
{
"citation": "Zhao, T., & Jiang, Y. InternationAl journal of fatigue, 30(5), 834-849 (2008)",
"type": "Journal reference",
"url": "https://doi.org/10.1016/j.ijfatigue.2007.07.005",
"comment": "Source of the collected data",
"doi": "10.1016/j.ijfatigue.2007.07.005"
},
{
"citation": "Gates, N. R., & Fatemi, A. International Journal of Fatigue, 100, 322-336 (2017)",
"type": "Journal reference",
"url": "https://doi.org/10.1016/j.ijfatigue.2017.03.042",
"comment": "Source of the collected data",
"doi": "10.1016/j.ijfatigue.2017.03.042"
},
{
"citation": "Su, W., & Zhu, H. M. Materials Testing, 64(11), 1572-1585 (2022)",
"type": "Journal reference",
"url": "https://doi.org/10.1515/mt-2022-0172",
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"doi": "10.1515/mt-2022-0172"
},
{
"citation": "Lin, H., Nayeb\u2010Hashemi, H., & Pelloux, R. M. Fatigue & Fracture of Engineering Materials & Structures, 16(7), 723-742 (1993)",
"type": "Journal reference",
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"comment": "Source of the collected data",
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},
{
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"comment": "Source of the collected data",
"doi": "10.1016/S1566-1369(03)80006-7"
},
{
"citation": "Wang, Y. Y., & Yao, W. X. International Journal of fatigue, 28(4), 401-408 (2006)",
"type": "Journal reference",
"url": "https://doi.org/10.1016/j.ijfatigue.2005.07.007",
"comment": "Source of the collected data",
"doi": "10.1016/j.ijfatigue.2005.07.007"
},
{
"citation": "Ma, T. H., Chang, L., Guo, S., Kong, L. R., He, X. H., & Zhou, C. Y. International Journal of Fatigue, 140, 105818 (2020)",
"type": "Journal reference",
"url": "https://doi.org/10.1016/j.ijfatigue.2020.105818",
"comment": "Source of the collected data",
"doi": "10.1016/j.ijfatigue.2020.105818"
},
{
"citation": "Wu, Z. R., Hu, X. T., & Song, Y. D. International Journal of Fatigue, 59, 170-175 (2014)",
"type": "Journal reference",
"url": "https://doi.org/10.1016/j.ijfatigue.2013.08.028",
"comment": "Source of the collected data",
"doi": "10.1016/j.ijfatigue.2013.08.028"
},
{
"citation": "Yu, Q., Zhang, J., Jiang, Y., & Li, Q. International Journal of Fatigue, 33(3), 437-447 (2011)",
"type": "Journal reference",
"url": "https://doi.org/10.1016/j.ijfatigue.2010.09.020",
"comment": "Source of the collected data",
"doi": "10.1016/j.ijfatigue.2010.09.020"
},
{
"citation": "Xiong, Y., Yu, Q., & Jiang, Y. Materials Science and Engineering: A, 546, 119-128 (2012)",
"type": "Journal reference",
"url": "https://doi.org/10.1016/j.msea.2012.03.039",
"comment": "Source of the collected data",
"doi": "10.1016/j.msea.2012.03.039"
},
{
"citation": "Albinmousa, J., Adinoyi, M. J., & Merah, N. Fatigue & Fracture of Engineering Materials & Structures, 42(10), 2276-2289 (2019)",
"type": "Journal reference",
"url": "https://doi.org/10.1111/ffe.13048",
"comment": "Source of the collected data",
"doi": "10.1111/ffe.13048"
},
{
"citation": "Kim, K. S., Nam, K. M., Kwak, G. J., & Hwang, S. M. International journal of fatigue, 26(7), 683-689 (2004)",
"type": "Journal reference",
"url": "https://doi.org/10.1016/j.ijfatigue.2003.11.005",
"comment": "Source of the collected data",
"doi": "10.1016/j.ijfatigue.2003.11.005"
},
{
"citation": "Kim, K. S., Park, J. C., & Lee, J. W. J. Eng. Mater. Technol, 121(3): 286-293 (8 pages) (1999)",
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"url": "https://doi.org/10.1115/1.2812377",
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"doi": "10.1115/1.2812377"
},
{
"citation": "Cheng Z. Doctoral dissertation, Nanjing: Nanjing University of Aeronautics and Astronautics (2010)",
"type": "Journal reference",
"url": "https://doi.org/10.7666/d.d167264",
"comment": "Source of the collected data",
"doi": "10.7666/d.d167264"
},
{
"citation": "Zhang X. Master's thesis, Nanning: Guangxi University (2013)",
"type": "Journal reference",
"url": "https://doi.org/10.7666/d.Y2407972",
"comment": "Source of the collected data",
"doi": "10.7666/d.Y2407972"
},
{
"citation": "Gang, Z. Master's thesis, Nanning: Guangxi University (2015)",
"type": "Journal reference",
"url": "https://doi.org/10.7666/d.Y2887183",
"comment": "Source of the collected data",
"doi": "10.7666/d.Y2887183"
},
{
"citation": "Arora, P., Gupta, S. K., Bhasin, V., Singh, R. K., Sivaprasad, S., & Tarafder, S. International Journal of Fatigue, 85, 98-113 (2016)",
"type": "Journal reference",
"url": "https://doi.org/10.1016/j.ijfatigue.2015.12.002",
"comment": "Source of the collected data",
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},
{
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},
{
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"type": "Journal reference",
"url": "https://doi.org/10.1016/j.ijfatigue.2006.10.028",
"comment": "Source of the collected data",
"doi": "10.1016/j.ijfatigue.2006.10.028"
},
{
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"url": "https://doi.org/10.1115/1.3008041",
"comment": "Source of the collected data",
"doi": "10.1115/1.3008041"
},
{
"citation": "Fatemi, A., & Socie, D. F. Fatigue & Fracture of Engineering materials & structures, 11(3), 149-165 (1988)",
"type": "Journal reference",
"url": "https://doi.org/10.1111/j.1460-2695.1988.tb01169.x",
"comment": "Source of the collected data",
"doi": "10.1111/j.1460-2695.1988.tb01169.x"
},
{
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"type": "Journal reference",
"comment": "Source of the collected data"
},
{
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"type": "Journal reference",
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"comment": "Source of the collected data",
"doi": "10.1520/STP36242S"
},
{
"citation": "Han, C., Chen, X., & Kim, K. S. International Journal of Fatigue, 24(9), 913-922 (2002)",
"type": "Journal reference",
"url": "https://doi.org/10.1016/S0142-1123(02)00013-0",
"comment": "Source of the collected data",
"doi": "10.1016/S0142-1123(02)00013-0"
},
{
"citation": "Skibicki, D., & Pejkowski, \u0141. International Journal of Fatigue, 102, 18-36 (2017)",
"type": "Journal reference",
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"comment": "Source of the collected data",
"doi": "10.1016/j.ijfatigue.2017.04.011"
},
{
"citation": "Shang, D. G., Sun, G. Q., Chen, J. H., Cai, N., & Yan, C. L. Materials Science and Engineering: A, 432(1-2), 231-238 (2006)",
"type": "Journal reference",
"url": "https://doi.org/10.1016/j.msea.2006.06.014",
"comment": "Source of the collected data",
"doi": "10.1016/j.msea.2006.06.014"
},
{
"citation": "Bonacuse, P. J., & Kalluri, S. J. Eng. Mater. Technol. Apr 1995, 117(2): 191-199 (9 pages) (1995)",
"type": "Journal reference",
"url": "https://doi.org/10.1115/1.2804529",
"comment": "Source of the collected data",
"doi": "10.1115/1.2804529"
},
{
"citation": "Socie D F, Shield T W. J. Eng. Mater. Technol. Jul 1984, 106(3): 227-232 (6 pages) (1984)",
"type": "Journal reference",
"url": "https://doi.org/10.1115/1.3225707",
"comment": "Source of the collected data",
"doi": "10.1115/1.3225707"
}
],
"doi": "10.24435/materialscloud:ad-xk",
"keywords": [
"Metal fatigue",
"Multiaxial fatigue",
"Deep learning",
"Fatigue life prediction"
],
"contributors": [
{
"affiliations": [
"School of Civil Engineering, Chongqing University, Chongqing 400045, China"
],
"familyname": "Chen",
"email": "441014578@qq.com",
"givennames": "Shuonan"
},
{
"affiliations": [
"Research Center of Steel Structure Engineering, Chongqing University, Chongqing 400045, China"
],
"familyname": "Bai*",
"email": "bai.yongtao@cqu.edu.cn",
"givennames": "Yongtao"
},
{
"affiliations": [
"Research Center of Steel Structure Engineering, Chongqing University, Chongqing 400045, China"
],
"familyname": "Zhou*",
"email": "zhouxuhong@126.com",
"givennames": "Xuhong"
},
{
"affiliations": [
"School of Civil Engineering, Chongqing University, Chongqing 400045, China"
],
"familyname": "Yang",
"givennames": "Ao"
}
],
"conceptrecid": "2251",
"version": 2,
"publication_date": "Aug 07, 2024, 17:25:39",
"is_last": true,
"status": "published",
"_files": [
{
"size": 1781307,
"checksum": "md5:42b948230f41dcbd60f7004e26e56100",
"description": "The compressed file contains two folders, two CSV files and one Excel files. \"Specific Information of the Materials\" provides detailed information on the collected materials. Two CSV files are intended for use with deep learning algorithms, with one corresponding to stress-controlled loading experiment samples and the other to strain-controlled samples. The specific loading paths for each sample are stored as time-series data in CSV files.",
"key": "A Deep Learning Dataset for Metal Multiaxial Fatigue Life Prediction_2nd_revised.zip"
}
],
"title": "A deep learning dataset for metal multiaxial fatigue life prediction"
},
"id": "2284",
"updated": "2024-08-07T15:25:39.963265+00:00",
"created": "2024-08-06T17:12:48.660054+00:00",
"revision": 4
}