Artificial Neural Network for the Prediction of Fatigue Life of Microscale Single-Crystal Copper
Microscale single-crystal copper is widely used in electronics, communications and other fields due to its excellent properties such as high ductility, high toughness and good conductivity. Therefore, it is particularly important to research its fatigue life. In order to explore the influence of siz...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-03-01
|
| Series: | Crystals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4352/13/3/539 |
| _version_ | 1797612488346304512 |
|---|---|
| author | Fanming Zeng Yabin Yan |
| author_facet | Fanming Zeng Yabin Yan |
| author_sort | Fanming Zeng |
| collection | DOAJ |
| description | Microscale single-crystal copper is widely used in electronics, communications and other fields due to its excellent properties such as high ductility, high toughness and good conductivity. Therefore, it is particularly important to research its fatigue life. In order to explore the influence of size effect, loading frequency and shear strain on the main slip surface on the fatigue life of microscale single-crystal copper based on in situ fatigue experimental data of microscale single-crystal copper, this paper used a BP neural network algorithm to construct a single-crystal copper fatigue life prediction network model. The data set included 14 groups of training data, with 11 groups as training sets and 3 groups as testing sets. The input characteristics were length, width, height, loading frequency and shear strain of the main sliding plane of a microscale single-crystal copper sample. The output characteristic was the fatigue life of microscale single-crystal copper. After training, the mean square error (MSE) of the model was 0.03, the absolute value error (MAE) was 0.125, and the correlation coefficient (<i>R</i><sup>2</sup>) was 0.93271, indicating that the BP neural network algorithm can effectively predict the fatigue life of microscale single-crystal copper and has good generalization ability. This model can not only save the experimental time of fatigue life measurement of micro-scale single-crystal copper, but also optimize the properties of the material by taking equidistant points in the range of characteristic parameters. Therefore, the current study demonstrates an applicable and efficient methodology to evaluate the fatigue life of microscale materials in industrial applications. |
| first_indexed | 2024-03-11T06:42:54Z |
| format | Article |
| id | doaj.art-86f061914e9242009167c2e946f6eb02 |
| institution | Directory Open Access Journal |
| issn | 2073-4352 |
| language | English |
| last_indexed | 2024-03-11T06:42:54Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Crystals |
| spelling | doaj.art-86f061914e9242009167c2e946f6eb022023-11-17T10:30:11ZengMDPI AGCrystals2073-43522023-03-0113353910.3390/cryst13030539Artificial Neural Network for the Prediction of Fatigue Life of Microscale Single-Crystal CopperFanming Zeng0Yabin Yan1School of Mechanical Power and Engineering, East China University of Science and Technology, Shanghai 200237, ChinaSchool of Mechanical Power and Engineering, East China University of Science and Technology, Shanghai 200237, ChinaMicroscale single-crystal copper is widely used in electronics, communications and other fields due to its excellent properties such as high ductility, high toughness and good conductivity. Therefore, it is particularly important to research its fatigue life. In order to explore the influence of size effect, loading frequency and shear strain on the main slip surface on the fatigue life of microscale single-crystal copper based on in situ fatigue experimental data of microscale single-crystal copper, this paper used a BP neural network algorithm to construct a single-crystal copper fatigue life prediction network model. The data set included 14 groups of training data, with 11 groups as training sets and 3 groups as testing sets. The input characteristics were length, width, height, loading frequency and shear strain of the main sliding plane of a microscale single-crystal copper sample. The output characteristic was the fatigue life of microscale single-crystal copper. After training, the mean square error (MSE) of the model was 0.03, the absolute value error (MAE) was 0.125, and the correlation coefficient (<i>R</i><sup>2</sup>) was 0.93271, indicating that the BP neural network algorithm can effectively predict the fatigue life of microscale single-crystal copper and has good generalization ability. This model can not only save the experimental time of fatigue life measurement of micro-scale single-crystal copper, but also optimize the properties of the material by taking equidistant points in the range of characteristic parameters. Therefore, the current study demonstrates an applicable and efficient methodology to evaluate the fatigue life of microscale materials in industrial applications.https://www.mdpi.com/2073-4352/13/3/539microscale single-crystal copperBP neural networkfatigue life |
| spellingShingle | Fanming Zeng Yabin Yan Artificial Neural Network for the Prediction of Fatigue Life of Microscale Single-Crystal Copper Crystals microscale single-crystal copper BP neural network fatigue life |
| title | Artificial Neural Network for the Prediction of Fatigue Life of Microscale Single-Crystal Copper |
| title_full | Artificial Neural Network for the Prediction of Fatigue Life of Microscale Single-Crystal Copper |
| title_fullStr | Artificial Neural Network for the Prediction of Fatigue Life of Microscale Single-Crystal Copper |
| title_full_unstemmed | Artificial Neural Network for the Prediction of Fatigue Life of Microscale Single-Crystal Copper |
| title_short | Artificial Neural Network for the Prediction of Fatigue Life of Microscale Single-Crystal Copper |
| title_sort | artificial neural network for the prediction of fatigue life of microscale single crystal copper |
| topic | microscale single-crystal copper BP neural network fatigue life |
| url | https://www.mdpi.com/2073-4352/13/3/539 |
| work_keys_str_mv | AT fanmingzeng artificialneuralnetworkforthepredictionoffatiguelifeofmicroscalesinglecrystalcopper AT yabinyan artificialneuralnetworkforthepredictionoffatiguelifeofmicroscalesinglecrystalcopper |