Prediction of Shock Wave Velocity Induced by a Combined Millisecond and Nanosecond Laser Based on Convolution Neural Network
The variation of shock-wave velocity with time induced by a millisecond-nanosecond combined pulse laser (CPL) on silicon is investigated. The convolution neural network (CNN) is used to predict the shock-wave velocity induced by a single ns laser and CPL with a ns laser energy density of 6, 12 and 2...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Photonics |
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| Online Access: | https://www.mdpi.com/2304-6732/10/9/1034 |
| _version_ | 1797577922555412480 |
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| author | Jingyi Li Wei Zhang Ye Li Guangyong Jin |
| author_facet | Jingyi Li Wei Zhang Ye Li Guangyong Jin |
| author_sort | Jingyi Li |
| collection | DOAJ |
| description | The variation of shock-wave velocity with time induced by a millisecond-nanosecond combined pulse laser (CPL) on silicon is investigated. The convolution neural network (CNN) is used to predict the shock-wave velocity induced by a single ns laser and CPL with a ns laser energy density of 6, 12 and 24 J/cm<sup>2</sup>, ms laser energy density of 0 and 226.13 J/cm<sup>2</sup>, and pulse delay of 0, 0.4 and 0.8 ms. The four-layer CNN model was applied, ns laser energy density, ms laser energy density, pulse delay and time were set as the input parameter, while the shock-wave velocity was set as the output parameter. The correlation coefficient (<i>R</i><sup>2</sup>), mean absolute error (<i>MAE</i>) and root mean square error (<i>RMSE</i>) of the CNN model on the test data set was 0.9865, 3.54 and 3.01, respectively. This indicated that the CNN model shows a high reliability in the prediction of CPL-induced shock-wave velocity with limited experimental data. |
| first_indexed | 2024-03-10T22:14:52Z |
| format | Article |
| id | doaj.art-f6d86a15055f4f74936dc0209eb2f21e |
| institution | Directory Open Access Journal |
| issn | 2304-6732 |
| language | English |
| last_indexed | 2024-03-10T22:14:52Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Photonics |
| spelling | doaj.art-f6d86a15055f4f74936dc0209eb2f21e2023-11-19T12:30:08ZengMDPI AGPhotonics2304-67322023-09-01109103410.3390/photonics10091034Prediction of Shock Wave Velocity Induced by a Combined Millisecond and Nanosecond Laser Based on Convolution Neural NetworkJingyi Li0Wei Zhang1Ye Li2Guangyong Jin3Jilin Key Laboratory of Solid-State Laser Technology and Application, School of Physics, Changchun University of Science and Technology, Changchun 130022, ChinaJilin Key Laboratory of Solid-State Laser Technology and Application, School of Physics, Changchun University of Science and Technology, Changchun 130022, ChinaSchool of Physics, Changchun University of Science and Technology, Changchun 130022, ChinaJilin Key Laboratory of Solid-State Laser Technology and Application, School of Physics, Changchun University of Science and Technology, Changchun 130022, ChinaThe variation of shock-wave velocity with time induced by a millisecond-nanosecond combined pulse laser (CPL) on silicon is investigated. The convolution neural network (CNN) is used to predict the shock-wave velocity induced by a single ns laser and CPL with a ns laser energy density of 6, 12 and 24 J/cm<sup>2</sup>, ms laser energy density of 0 and 226.13 J/cm<sup>2</sup>, and pulse delay of 0, 0.4 and 0.8 ms. The four-layer CNN model was applied, ns laser energy density, ms laser energy density, pulse delay and time were set as the input parameter, while the shock-wave velocity was set as the output parameter. The correlation coefficient (<i>R</i><sup>2</sup>), mean absolute error (<i>MAE</i>) and root mean square error (<i>RMSE</i>) of the CNN model on the test data set was 0.9865, 3.54 and 3.01, respectively. This indicated that the CNN model shows a high reliability in the prediction of CPL-induced shock-wave velocity with limited experimental data.https://www.mdpi.com/2304-6732/10/9/1034convolution neural networkshock wave velocitycombined pulse laser |
| spellingShingle | Jingyi Li Wei Zhang Ye Li Guangyong Jin Prediction of Shock Wave Velocity Induced by a Combined Millisecond and Nanosecond Laser Based on Convolution Neural Network Photonics convolution neural network shock wave velocity combined pulse laser |
| title | Prediction of Shock Wave Velocity Induced by a Combined Millisecond and Nanosecond Laser Based on Convolution Neural Network |
| title_full | Prediction of Shock Wave Velocity Induced by a Combined Millisecond and Nanosecond Laser Based on Convolution Neural Network |
| title_fullStr | Prediction of Shock Wave Velocity Induced by a Combined Millisecond and Nanosecond Laser Based on Convolution Neural Network |
| title_full_unstemmed | Prediction of Shock Wave Velocity Induced by a Combined Millisecond and Nanosecond Laser Based on Convolution Neural Network |
| title_short | Prediction of Shock Wave Velocity Induced by a Combined Millisecond and Nanosecond Laser Based on Convolution Neural Network |
| title_sort | prediction of shock wave velocity induced by a combined millisecond and nanosecond laser based on convolution neural network |
| topic | convolution neural network shock wave velocity combined pulse laser |
| url | https://www.mdpi.com/2304-6732/10/9/1034 |
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