FAILURE PREDICTION OF BOLTED CONNECTION OF COMPOSITE MATERIALS BASED ON DEEP LEARNING (MT)
Aiming at the problem of failure strength analysis and prediction of bolted composite connection, the strong nonlinear mapping ability of deep learning neural network was used to non-linear fit the influence of different parameters on the failure load of composite bolting, and the influence weight o...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2023-01-01
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| Series: | Jixie qiangdu |
| Subjects: | |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2023.02.026 |
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| _version_ | 1850073203118964736 |
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| author | PENG Fan ZOU SiNong REN YiRu |
| author_facet | PENG Fan ZOU SiNong REN YiRu |
| author_sort | PENG Fan |
| collection | DOAJ |
| description | Aiming at the problem of failure strength analysis and prediction of bolted composite connection, the strong nonlinear mapping ability of deep learning neural network was used to non-linear fit the influence of different parameters on the failure load of composite bolting, and the influence weight of each parameter was allocated. A prediction model was constructed based on limited training samples to predict the peak failure load of bolted composite joints. Using finite element software, the data set of peak failure load of bolted laminates was calculated to construct the deep learning neural network. Through the test, it is determined that the development effect of deep learning model is the best when the number of hidden layers is two. The mean square error between the predicted value and the finite element simulation value is taken as the loss function, and the learning rate is set at 0.01. When the mean square error is the minimum, the training is stopped, and the best deep learning prediction model is obtained. The model is used to predict the maximum value of all the prediction results of peak load failure and the corresponding parameter combination, and compared with the simulation results of the same parameters, the difference between them is 1.4%. Compared with the prediction methods of finite element simulation and empirical formula fitting, the deep learning prediction method has obvious advantages in time efficiency. |
| format | Article |
| id | doaj-art-633a8d8975af4577a41557659a98e2d4 |
| institution | DOAJ |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2023-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-633a8d8975af4577a41557659a98e2d42025-08-20T02:46:54ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692023-01-0144745336351525FAILURE PREDICTION OF BOLTED CONNECTION OF COMPOSITE MATERIALS BASED ON DEEP LEARNING (MT)PENG FanZOU SiNongREN YiRuAiming at the problem of failure strength analysis and prediction of bolted composite connection, the strong nonlinear mapping ability of deep learning neural network was used to non-linear fit the influence of different parameters on the failure load of composite bolting, and the influence weight of each parameter was allocated. A prediction model was constructed based on limited training samples to predict the peak failure load of bolted composite joints. Using finite element software, the data set of peak failure load of bolted laminates was calculated to construct the deep learning neural network. Through the test, it is determined that the development effect of deep learning model is the best when the number of hidden layers is two. The mean square error between the predicted value and the finite element simulation value is taken as the loss function, and the learning rate is set at 0.01. When the mean square error is the minimum, the training is stopped, and the best deep learning prediction model is obtained. The model is used to predict the maximum value of all the prediction results of peak load failure and the corresponding parameter combination, and compared with the simulation results of the same parameters, the difference between them is 1.4%. Compared with the prediction methods of finite element simulation and empirical formula fitting, the deep learning prediction method has obvious advantages in time efficiency.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2023.02.026Carbon fiber compositesBolted connectionPeak failure loadDeep learningNeural network |
| spellingShingle | PENG Fan ZOU SiNong REN YiRu FAILURE PREDICTION OF BOLTED CONNECTION OF COMPOSITE MATERIALS BASED ON DEEP LEARNING (MT) Jixie qiangdu Carbon fiber composites Bolted connection Peak failure load Deep learning Neural network |
| title | FAILURE PREDICTION OF BOLTED CONNECTION OF COMPOSITE MATERIALS BASED ON DEEP LEARNING (MT) |
| title_full | FAILURE PREDICTION OF BOLTED CONNECTION OF COMPOSITE MATERIALS BASED ON DEEP LEARNING (MT) |
| title_fullStr | FAILURE PREDICTION OF BOLTED CONNECTION OF COMPOSITE MATERIALS BASED ON DEEP LEARNING (MT) |
| title_full_unstemmed | FAILURE PREDICTION OF BOLTED CONNECTION OF COMPOSITE MATERIALS BASED ON DEEP LEARNING (MT) |
| title_short | FAILURE PREDICTION OF BOLTED CONNECTION OF COMPOSITE MATERIALS BASED ON DEEP LEARNING (MT) |
| title_sort | failure prediction of bolted connection of composite materials based on deep learning mt |
| topic | Carbon fiber composites Bolted connection Peak failure load Deep learning Neural network |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2023.02.026 |
| work_keys_str_mv | AT pengfan failurepredictionofboltedconnectionofcompositematerialsbasedondeeplearningmt AT zousinong failurepredictionofboltedconnectionofcompositematerialsbasedondeeplearningmt AT renyiru failurepredictionofboltedconnectionofcompositematerialsbasedondeeplearningmt |