Machine Learning-Assisted Optimization of Femtosecond Laser-Induced Superhydrophobic Microstructure Processing
Superhydrophobic surfaces have garnered significant attention due to their pivotal roles in various fields. Femtosecond laser technology provides a feasible means for inducing superhydrophobic microstructures on material surfaces. However, due to the unclear influence mechanisms of process parameter...
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MDPI AG
2025-05-01
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| author | Lifei Wang Yucheng Gu Xiaoqing Tian Jun Wang Yan Jia Junjie Xu Zhen Zhang Shiying Liu Shuo Liu |
| author_facet | Lifei Wang Yucheng Gu Xiaoqing Tian Jun Wang Yan Jia Junjie Xu Zhen Zhang Shiying Liu Shuo Liu |
| author_sort | Lifei Wang |
| collection | DOAJ |
| description | Superhydrophobic surfaces have garnered significant attention due to their pivotal roles in various fields. Femtosecond laser technology provides a feasible means for inducing superhydrophobic microstructures on material surfaces. However, due to the unclear influence mechanisms of process parameters, as well as the high cost and time-consuming nature of experiments, identifying the optimal femtosecond laser processing parameters within the process space remains a significant challenge. To address this issue, a process optimization framework that couples machine learning and genetic algorithms was proposed and successfully applied to the optimization of femtosecond laser-induced groove structures on TC4 alloy surfaces. Firstly, based on 64 sets of experimental data, the effects of the power, scanning speed, and scanning interval on the micro-groove structures and their wetting properties were discussed in detail. Furthermore, by utilizing this small sample dataset, various machine learning algorithms were employed to establish a prediction model for the contact angle, among which support vector regression demonstrated the optimal predictive accuracy. Three additional dimensional variables, i.e., the number of effective pulses, energy deposition rate, and roughness, were also added to the original dataset vectors as extra dimensions to participate in and guide the model training process. The prediction model was further coupled into a genetic algorithm to achieve the quantitative design of femtosecond laser processing. Compared to the best hydrophobicity in the original dataset, the contact angle of the designed process was improved by 5.5%. The proposed method provides an ideal solution for accurately predicting wetting properties and identifying optimal processes, thereby accelerating the development and application of femtosecond laser-induced superhydrophobic microstructures. |
| format | Article |
| id | doaj-art-140fbb5357344ed6acd8feb800a0d6c8 |
| institution | DOAJ |
| issn | 2304-6732 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Photonics |
| spelling | doaj-art-140fbb5357344ed6acd8feb800a0d6c82025-08-20T03:16:35ZengMDPI AGPhotonics2304-67322025-05-0112653010.3390/photonics12060530Machine Learning-Assisted Optimization of Femtosecond Laser-Induced Superhydrophobic Microstructure ProcessingLifei Wang0Yucheng Gu1Xiaoqing Tian2Jun Wang3Yan Jia4Junjie Xu5Zhen Zhang6Shiying Liu7Shuo Liu8Science and Technology on Advanced High Temperature Structural Materials Laboratory, Beijing Institute of Aeronautical Materials, Beijing 100095, ChinaCenter for Advanced Laser Technology, School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, ChinaBinzhou Polytechnic, Binzhou 256603, ChinaShandong Key Laboratory of Advanced Engine Piston Assembly, Binzhou Bohai Piston Co., Ltd., Binzhou 256602, ChinaShandong Key Laboratory of Advanced Engine Piston Assembly, Binzhou Bohai Piston Co., Ltd., Binzhou 256602, ChinaScience and Technology on Advanced High Temperature Structural Materials Laboratory, Beijing Institute of Aeronautical Materials, Beijing 100095, ChinaCenter for Advanced Laser Technology, School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, ChinaShandong Key Laboratory of Advanced Engine Piston Assembly, Binzhou Bohai Piston Co., Ltd., Binzhou 256602, ChinaCenter for Advanced Laser Technology, School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, ChinaSuperhydrophobic surfaces have garnered significant attention due to their pivotal roles in various fields. Femtosecond laser technology provides a feasible means for inducing superhydrophobic microstructures on material surfaces. However, due to the unclear influence mechanisms of process parameters, as well as the high cost and time-consuming nature of experiments, identifying the optimal femtosecond laser processing parameters within the process space remains a significant challenge. To address this issue, a process optimization framework that couples machine learning and genetic algorithms was proposed and successfully applied to the optimization of femtosecond laser-induced groove structures on TC4 alloy surfaces. Firstly, based on 64 sets of experimental data, the effects of the power, scanning speed, and scanning interval on the micro-groove structures and their wetting properties were discussed in detail. Furthermore, by utilizing this small sample dataset, various machine learning algorithms were employed to establish a prediction model for the contact angle, among which support vector regression demonstrated the optimal predictive accuracy. Three additional dimensional variables, i.e., the number of effective pulses, energy deposition rate, and roughness, were also added to the original dataset vectors as extra dimensions to participate in and guide the model training process. The prediction model was further coupled into a genetic algorithm to achieve the quantitative design of femtosecond laser processing. Compared to the best hydrophobicity in the original dataset, the contact angle of the designed process was improved by 5.5%. The proposed method provides an ideal solution for accurately predicting wetting properties and identifying optimal processes, thereby accelerating the development and application of femtosecond laser-induced superhydrophobic microstructures.https://www.mdpi.com/2304-6732/12/6/530femtosecond lasermachine learningsuperhydrophobicitygenetic algorithm |
| spellingShingle | Lifei Wang Yucheng Gu Xiaoqing Tian Jun Wang Yan Jia Junjie Xu Zhen Zhang Shiying Liu Shuo Liu Machine Learning-Assisted Optimization of Femtosecond Laser-Induced Superhydrophobic Microstructure Processing Photonics femtosecond laser machine learning superhydrophobicity genetic algorithm |
| title | Machine Learning-Assisted Optimization of Femtosecond Laser-Induced Superhydrophobic Microstructure Processing |
| title_full | Machine Learning-Assisted Optimization of Femtosecond Laser-Induced Superhydrophobic Microstructure Processing |
| title_fullStr | Machine Learning-Assisted Optimization of Femtosecond Laser-Induced Superhydrophobic Microstructure Processing |
| title_full_unstemmed | Machine Learning-Assisted Optimization of Femtosecond Laser-Induced Superhydrophobic Microstructure Processing |
| title_short | Machine Learning-Assisted Optimization of Femtosecond Laser-Induced Superhydrophobic Microstructure Processing |
| title_sort | machine learning assisted optimization of femtosecond laser induced superhydrophobic microstructure processing |
| topic | femtosecond laser machine learning superhydrophobicity genetic algorithm |
| url | https://www.mdpi.com/2304-6732/12/6/530 |
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