Automatic process parameters tuning and surface roughness estimation for laser cleaning
文献类型：期刊论文
| 作者 | Liu, Haoting2; Li, Jiacheng2; Yang, Yong3; Lan, Jinhui2; Xue, Yafei1 |
| 刊名 | IEEE Access
 |
| 出版日期 | 2020 |
| 卷号 | 8页码:20904-20919 |
| 关键词 | Laser cleaning process parameters surface roughness image feature thermophysical property |
| ISSN号 | 21693536 |
| DOI | 10.1109/ACCESS.2020.2970086 |
| 产权排序 | 2 |
| 英文摘要 | An image analysis-based two-stage process parameters tuning and Surface Roughness (SR) estimation algorithm is proposed for the laser cleaning application. A Cartesian coordinate robot is utilized to collect image and implement cleaning. Before cleaning, in order to tune the proper laser parameters, first, the environment lighting is controlled for the metal image collection. Second, lots of classification features are computed for the images above. The Gray-Level Co-occurrence Matrix (GLCM) texture features, the concavo-convex region features, the histogram symmetry difference feature, and the imaging thermophysical property features are computed. Third, the initial laser parameters are created randomly and an iteration computation is performed: a Support Vector Machine (SVM) is used to forecast the cleaning effect; its inputs include the classification features and the initial laser parameters; its output is the cleaning effect degree. If the SVM output cannot fulfill user's demand, the laser parameters will be updated randomly. This iteration will be implemented constantly until the SVM output becomes valid. Then the laser cleaning will be performed. When estimating SR for the cleaned metal, multiple image features are calculated for the images after cleaning. The features include the Tamura coarseness, some GLCM features, and the convex region feature. To improve the prediction precision, different feature combinations are used for different cleaning effects. The linear function and the 3-order polynomial function are considered for the SR estimation. After tests, the accuracies of SVM, the SR prediction function, and the integrated SR control and estimation algorithm can be 90.0%, 80.0% and 80.0% approximately. © 2013 IEEE. |
| 语种 | 英语 |
| 出版者 | Institute of Electrical and Electronics Engineers Inc. |
| 源URL | [http://ir.opt.ac.cn/handle/181661/93275]  |
| 专题 | 西安光学精密机械研究所_瞬态光学技术国家重点实验室 |
| 通讯作者 | Xue, Yafei |
| 作者单位 | 1.Guangdong Provincial Key Laboratory of Advanced Welding Technology, Guangdong Welding Institute (China-Ukraine E. O. Paton Institute of Welding), Guangzhou; 510650, China 2.Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing; 100083, China; 3.State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an; 710119, China; |
| 推荐引用方式 GB/T 7714 | Liu, Haoting,Li, Jiacheng,Yang, Yong,et al. Automatic process parameters tuning and surface roughness estimation for laser cleaning[J]. IEEE Access,2020,8:20904-20919. |
| APA | Liu, Haoting,Li, Jiacheng,Yang, Yong,Lan, Jinhui,&Xue, Yafei.(2020).Automatic process parameters tuning and surface roughness estimation for laser cleaning.IEEE Access,8,20904-20919. |
| MLA | Liu, Haoting,et al."Automatic process parameters tuning and surface roughness estimation for laser cleaning".IEEE Access 8(2020):20904-20919. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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