{"title":"通过时空神经网络对金属材料疲劳裂纹增长进行图像驱动预测","authors":"","doi":"10.1016/j.engfracmech.2024.110442","DOIUrl":null,"url":null,"abstract":"<div><p>This study proposes an image-driven model based on the SimVP spatiotemporal neural network (STNN) to predict the fatigue crack growth (FCG) in aluminum alloys. This methodology represents a novel usage of STNNs for FCG analysis. It does not require repetitive modeling, extensive computations, or conventional mechanical assumptions. The datasets used during this study were gathered from fatigue experiments with a variety of crack positions, angles, and load levels; they contained a total of 17,925 image frames obtained from DIC measurements. Subsequently, the displacement fields were interpolated onto uniform grids and then augmented, so they could be fitted into an STNN. The proposed method was validated using specimens with edge and central cracks subjected to loads equal to 15.0 % and 20.0 % of the ultimate load. The generalization capability of the proposed method was studied by predicting the FCG under load levels and crack angles outside the training set. In addition, its predictive capability was investigated for both short and long step sizes by employing datasets in which the image data were collected at varying intervals. The overall structural similarity index measurement values were greater than 0.968, and the root mean square errors were held within 0.025 mm. The predicted displacement fields, crack lengths, and crack growth rates agreed well with experimental measurements.</p></div>","PeriodicalId":11576,"journal":{"name":"Engineering Fracture Mechanics","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Image-driven prediction of fatigue crack growth in metal materials via spatiotemporal neural network\",\"authors\":\"\",\"doi\":\"10.1016/j.engfracmech.2024.110442\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This study proposes an image-driven model based on the SimVP spatiotemporal neural network (STNN) to predict the fatigue crack growth (FCG) in aluminum alloys. This methodology represents a novel usage of STNNs for FCG analysis. It does not require repetitive modeling, extensive computations, or conventional mechanical assumptions. The datasets used during this study were gathered from fatigue experiments with a variety of crack positions, angles, and load levels; they contained a total of 17,925 image frames obtained from DIC measurements. Subsequently, the displacement fields were interpolated onto uniform grids and then augmented, so they could be fitted into an STNN. The proposed method was validated using specimens with edge and central cracks subjected to loads equal to 15.0 % and 20.0 % of the ultimate load. The generalization capability of the proposed method was studied by predicting the FCG under load levels and crack angles outside the training set. In addition, its predictive capability was investigated for both short and long step sizes by employing datasets in which the image data were collected at varying intervals. The overall structural similarity index measurement values were greater than 0.968, and the root mean square errors were held within 0.025 mm. The predicted displacement fields, crack lengths, and crack growth rates agreed well with experimental measurements.</p></div>\",\"PeriodicalId\":11576,\"journal\":{\"name\":\"Engineering Fracture Mechanics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2024-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Engineering Fracture Mechanics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0013794424006052\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MECHANICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Fracture Mechanics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0013794424006052","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MECHANICS","Score":null,"Total":0}
Image-driven prediction of fatigue crack growth in metal materials via spatiotemporal neural network
This study proposes an image-driven model based on the SimVP spatiotemporal neural network (STNN) to predict the fatigue crack growth (FCG) in aluminum alloys. This methodology represents a novel usage of STNNs for FCG analysis. It does not require repetitive modeling, extensive computations, or conventional mechanical assumptions. The datasets used during this study were gathered from fatigue experiments with a variety of crack positions, angles, and load levels; they contained a total of 17,925 image frames obtained from DIC measurements. Subsequently, the displacement fields were interpolated onto uniform grids and then augmented, so they could be fitted into an STNN. The proposed method was validated using specimens with edge and central cracks subjected to loads equal to 15.0 % and 20.0 % of the ultimate load. The generalization capability of the proposed method was studied by predicting the FCG under load levels and crack angles outside the training set. In addition, its predictive capability was investigated for both short and long step sizes by employing datasets in which the image data were collected at varying intervals. The overall structural similarity index measurement values were greater than 0.968, and the root mean square errors were held within 0.025 mm. The predicted displacement fields, crack lengths, and crack growth rates agreed well with experimental measurements.
期刊介绍:
EFM covers a broad range of topics in fracture mechanics to be of interest and use to both researchers and practitioners. Contributions are welcome which address the fracture behavior of conventional engineering material systems as well as newly emerging material systems. Contributions on developments in the areas of mechanics and materials science strongly related to fracture mechanics are also welcome. Papers on fatigue are welcome if they treat the fatigue process using the methods of fracture mechanics.