Complex 3D Surface Structuring by Means of Laser Chemical Machining with Modulated Laser Power
JOURNAL OF LASER APPLICATIONS（2025）
BIAS Bremer Inst Angew Strahltechn GmbH
Abstract
Laser chemical machining (LCM) utilizes the thermal induced chemical dissolution to remove the material far below the melting temperature. Material removal during LCM with modulated laser power depends on the modulation frequency applied. The spatial frequency of the depth's oscillation corresponds to the spatial frequency of the laser power up to the threshold frequency. Above this threshold, the removal depth remains constant. The aim of this work is to investigate whether modulated laser power could be used for surface structuring and its influence on the wetting behavior. Therefore, line scans with different overlaps were generated to create a surface structure on titanium grade 1 using a rectangular function of the output power with frequencies below the threshold. In addition to the modulation frequency and the line overlap, the phase angle between two neighboring lines was also studied. Subsequently, a wide range of surface structures was achieved, including wavelike and braidlike structures. The wettability investigation highlighted the considerable impact of the line overlap on the wetting behavior of the surfaces produced. While higher overlaps caused an isotropic wetting, lower overlaps induced an anisotropic wetting. These results, thus, demonstrate for the first time the possibility of achieving anisotropic wetting behavior by LCM processing.
MoreTranslated text
Key words
surface structuring,laser chemical processing,power modulation
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined