Laser Cleaning Surface Roughness Estimation Using Enhanced GLCM Feature and IPSO-SVR
In order to evaluate the effect of laser cleaning, a new method of workpiece surface roughness estimation is proposed. First, a Cartesian robot and visible-light camera are used to collect a large number of surface images of a workpiece after laser cleaning. Second, various features including the Ta...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
|
| Series: | Photonics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2304-6732/9/8/510 |
| _version_ | 1797442675644825600 |
|---|---|
| author | Jianyue Ge Haoting Liu Shaohua Yang Jinhui Lan |
| author_facet | Jianyue Ge Haoting Liu Shaohua Yang Jinhui Lan |
| author_sort | Jianyue Ge |
| collection | DOAJ |
| description | In order to evaluate the effect of laser cleaning, a new method of workpiece surface roughness estimation is proposed. First, a Cartesian robot and visible-light camera are used to collect a large number of surface images of a workpiece after laser cleaning. Second, various features including the Tamura coarseness, Alexnet abstract depth, single blind/referenceless image spatial quality evaluator (BRISQUE), and enhanced gray level co-occurrence matrix (EGLCM) are computed from the images above. Third, the improved particle swarm optimization (IPSO) is used to improve the training parameters of support vector regression (SVR). The learning factor of SVR adopts the strategy of dynamic nonlinear asynchronous adaptive adjustment to improve its optimization-processing ability. Finally, both the image features and the IPSO-SVR are considered for the surface roughness estimation. Extensive experiment results show that the accuracy of the IPSO-SVR surface roughness estimation model can reach 92.0%. |
| first_indexed | 2024-03-09T12:46:21Z |
| format | Article |
| id | doaj.art-f6707cc7f9ab415fb51213d7ea812906 |
| institution | Directory Open Access Journal |
| issn | 2304-6732 |
| language | English |
| last_indexed | 2024-03-09T12:46:21Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Photonics |
| spelling | doaj.art-f6707cc7f9ab415fb51213d7ea8129062023-11-30T22:12:53ZengMDPI AGPhotonics2304-67322022-07-019851010.3390/photonics9080510Laser Cleaning Surface Roughness Estimation Using Enhanced GLCM Feature and IPSO-SVRJianyue Ge0Haoting Liu1Shaohua Yang2Jinhui Lan3Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaBeijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaNorthwest Institute of Nuclear Technology, Xi’an 710024, ChinaBeijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, ChinaIn order to evaluate the effect of laser cleaning, a new method of workpiece surface roughness estimation is proposed. First, a Cartesian robot and visible-light camera are used to collect a large number of surface images of a workpiece after laser cleaning. Second, various features including the Tamura coarseness, Alexnet abstract depth, single blind/referenceless image spatial quality evaluator (BRISQUE), and enhanced gray level co-occurrence matrix (EGLCM) are computed from the images above. Third, the improved particle swarm optimization (IPSO) is used to improve the training parameters of support vector regression (SVR). The learning factor of SVR adopts the strategy of dynamic nonlinear asynchronous adaptive adjustment to improve its optimization-processing ability. Finally, both the image features and the IPSO-SVR are considered for the surface roughness estimation. Extensive experiment results show that the accuracy of the IPSO-SVR surface roughness estimation model can reach 92.0%.https://www.mdpi.com/2304-6732/9/8/510pretrained Alexnetsurface roughness estimationELGCMIPSO-SVRtransfer learning |
| spellingShingle | Jianyue Ge Haoting Liu Shaohua Yang Jinhui Lan Laser Cleaning Surface Roughness Estimation Using Enhanced GLCM Feature and IPSO-SVR Photonics pretrained Alexnet surface roughness estimation ELGCM IPSO-SVR transfer learning |
| title | Laser Cleaning Surface Roughness Estimation Using Enhanced GLCM Feature and IPSO-SVR |
| title_full | Laser Cleaning Surface Roughness Estimation Using Enhanced GLCM Feature and IPSO-SVR |
| title_fullStr | Laser Cleaning Surface Roughness Estimation Using Enhanced GLCM Feature and IPSO-SVR |
| title_full_unstemmed | Laser Cleaning Surface Roughness Estimation Using Enhanced GLCM Feature and IPSO-SVR |
| title_short | Laser Cleaning Surface Roughness Estimation Using Enhanced GLCM Feature and IPSO-SVR |
| title_sort | laser cleaning surface roughness estimation using enhanced glcm feature and ipso svr |
| topic | pretrained Alexnet surface roughness estimation ELGCM IPSO-SVR transfer learning |
| url | https://www.mdpi.com/2304-6732/9/8/510 |
| work_keys_str_mv | AT jianyuege lasercleaningsurfaceroughnessestimationusingenhancedglcmfeatureandipsosvr AT haotingliu lasercleaningsurfaceroughnessestimationusingenhancedglcmfeatureandipsosvr AT shaohuayang lasercleaningsurfaceroughnessestimationusingenhancedglcmfeatureandipsosvr AT jinhuilan lasercleaningsurfaceroughnessestimationusingenhancedglcmfeatureandipsosvr |