Reflection Coefficient Estimation of Femtosecond Laser Surface Processing Using Support Vector Regression
An image analysis-based Reflection Coefficient (RC) estimation method of femtosecond laser surface processing for the blackening of X-ray imaging sensor shell is proposed. The Support Vector Regression (SVR) is used for RC computation and both an offline and an online steps are considered in this me...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Photonics Journal |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9918074/ |
| _version_ | 1828327140219682816 |
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| author | Haoting Liu Jianyue Ge Shaohua Yang Ling Zhang Yafei Xue Jinhui Lan |
| author_facet | Haoting Liu Jianyue Ge Shaohua Yang Ling Zhang Yafei Xue Jinhui Lan |
| author_sort | Haoting Liu |
| collection | DOAJ |
| description | An image analysis-based Reflection Coefficient (RC) estimation method of femtosecond laser surface processing for the blackening of X-ray imaging sensor shell is proposed. The Support Vector Regression (SVR) is used for RC computation and both an offline and an online steps are considered in this method. Regarding offline step, the typical laser process parameters are set to perform surface processing and Scanning Electron Microscope (SEM) images are recorded. Then a series of image features are computed and both the computed image features and typical laser parameters are used to train SVR: the training dataset includes the laser line space, laser beam diameter, natural logarithm of laser power divided by laser frequency, and image features of Gray-Level Co-occurrence Matrix (GLCM); the supervising data are laser ablation diameters. As for online step, when SEM image data are recorded after laser processing, the trained SVR is used to predict laser ablation diameter and then the RC can be computed by laser ablation model. Many experiment results have verified the effectiveness of our proposed method, and the RC estimation accuracy can be better than 90.0%. |
| first_indexed | 2024-04-13T19:50:24Z |
| format | Article |
| id | doaj.art-c093fefdf5ab4936a509f70323af3f1e |
| institution | Directory Open Access Journal |
| issn | 1943-0655 |
| language | English |
| last_indexed | 2024-04-13T19:50:24Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj.art-c093fefdf5ab4936a509f70323af3f1e2022-12-22T02:32:34ZengIEEEIEEE Photonics Journal1943-06552022-01-011461910.1109/JPHOT.2022.32142389918074Reflection Coefficient Estimation of Femtosecond Laser Surface Processing Using Support Vector RegressionHaoting Liu0https://orcid.org/0000-0003-2537-6138Jianyue Ge1https://orcid.org/0000-0002-0798-807XShaohua Yang2https://orcid.org/0000-0001-8487-5899Ling Zhang3Yafei Xue4Jinhui Lan5https://orcid.org/0000-0003-0412-9621Beijing Engineering Research Center of Industrial Spectrum Imaging, University of Science and Technology, Beijing, ChinaBeijing Engineering Research Center of Industrial Spectrum Imaging, University of Science and Technology, Beijing, ChinaNorthwest Institute of Nuclear Technology, Xi'an, ChinaInstitute of Semiconductors, Chinese Academy of sciences, Beijing, ChinaChina-Ukraine Institute of Welding, Guangdong Academy of Sciences, Guangzhou, ChinaBeijing Engineering Research Center of Industrial Spectrum Imaging, University of Science and Technology, Beijing, ChinaAn image analysis-based Reflection Coefficient (RC) estimation method of femtosecond laser surface processing for the blackening of X-ray imaging sensor shell is proposed. The Support Vector Regression (SVR) is used for RC computation and both an offline and an online steps are considered in this method. Regarding offline step, the typical laser process parameters are set to perform surface processing and Scanning Electron Microscope (SEM) images are recorded. Then a series of image features are computed and both the computed image features and typical laser parameters are used to train SVR: the training dataset includes the laser line space, laser beam diameter, natural logarithm of laser power divided by laser frequency, and image features of Gray-Level Co-occurrence Matrix (GLCM); the supervising data are laser ablation diameters. As for online step, when SEM image data are recorded after laser processing, the trained SVR is used to predict laser ablation diameter and then the RC can be computed by laser ablation model. Many experiment results have verified the effectiveness of our proposed method, and the RC estimation accuracy can be better than 90.0%.https://ieeexplore.ieee.org/document/9918074/Femtosecond laserreflectance coefficientimage featuremachine learningsurface treatment |
| spellingShingle | Haoting Liu Jianyue Ge Shaohua Yang Ling Zhang Yafei Xue Jinhui Lan Reflection Coefficient Estimation of Femtosecond Laser Surface Processing Using Support Vector Regression IEEE Photonics Journal Femtosecond laser reflectance coefficient image feature machine learning surface treatment |
| title | Reflection Coefficient Estimation of Femtosecond Laser Surface Processing Using Support Vector Regression |
| title_full | Reflection Coefficient Estimation of Femtosecond Laser Surface Processing Using Support Vector Regression |
| title_fullStr | Reflection Coefficient Estimation of Femtosecond Laser Surface Processing Using Support Vector Regression |
| title_full_unstemmed | Reflection Coefficient Estimation of Femtosecond Laser Surface Processing Using Support Vector Regression |
| title_short | Reflection Coefficient Estimation of Femtosecond Laser Surface Processing Using Support Vector Regression |
| title_sort | reflection coefficient estimation of femtosecond laser surface processing using support vector regression |
| topic | Femtosecond laser reflectance coefficient image feature machine learning surface treatment |
| url | https://ieeexplore.ieee.org/document/9918074/ |
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