Enhancing Laser Surface Texturing through Advanced Machine Learning Techniques
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866915936446971904 |
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| author | Zwahr, Christoph Schell, Frederic Steege, Tobias Lasagni, Andrés Fabián |
| author_facet | Zwahr, Christoph Schell, Frederic Steege, Tobias Lasagni, Andrés Fabián |
| contents | Laser material processing has emerged as a versatile and indispensable tool in various industries, including manufacturing, healthcare, and materials science. However, the interaction of a lasers with surfaces is highly dependent on a large number of factors, including properties of the laser source such as pulse duration, wavelength and pulse form, as well as properties of the material such as surface roughness, heat capacity and thermal conductivity. Therefore, the optimization of laser texturing processes in regards to specific target geometries while maintaining texture quality and process efficiency is a time consuming task that requires experienced operators with expert knowledge of the process and its components. The complex and nonlinear relationships between the various process, laser and material parameters and the resulting surface topography or functionality are challenging to model analytically. Therefore, the fabrication of large numbers of different parameter variations are typically required to enable empirical modeling and process optimization. Machine learning offers a promising approach to overcoming these challenges, particularly when the interrelations between process parameters are not well understood. It enables effective process optimization, surface property prediction, and automated monitoring-tasks that previously required expert knowledge. This chapter demonstrates the application of machine learning to Laser Surface Texturing techniques. Using algorithms such as neural networks and random forests, surface roughness can be predicted based on laser parameters and material data. This facilitates faster process optimization, reduces experimental effort, and enables predictive visualization - all while maintaining high accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_12451 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Enhancing Laser Surface Texturing through Advanced Machine Learning Techniques Zwahr, Christoph Schell, Frederic Steege, Tobias Lasagni, Andrés Fabián Materials Science Applied Physics Optics Laser material processing has emerged as a versatile and indispensable tool in various industries, including manufacturing, healthcare, and materials science. However, the interaction of a lasers with surfaces is highly dependent on a large number of factors, including properties of the laser source such as pulse duration, wavelength and pulse form, as well as properties of the material such as surface roughness, heat capacity and thermal conductivity. Therefore, the optimization of laser texturing processes in regards to specific target geometries while maintaining texture quality and process efficiency is a time consuming task that requires experienced operators with expert knowledge of the process and its components. The complex and nonlinear relationships between the various process, laser and material parameters and the resulting surface topography or functionality are challenging to model analytically. Therefore, the fabrication of large numbers of different parameter variations are typically required to enable empirical modeling and process optimization. Machine learning offers a promising approach to overcoming these challenges, particularly when the interrelations between process parameters are not well understood. It enables effective process optimization, surface property prediction, and automated monitoring-tasks that previously required expert knowledge. This chapter demonstrates the application of machine learning to Laser Surface Texturing techniques. Using algorithms such as neural networks and random forests, surface roughness can be predicted based on laser parameters and material data. This facilitates faster process optimization, reduces experimental effort, and enables predictive visualization - all while maintaining high accuracy. |
| title | Enhancing Laser Surface Texturing through Advanced Machine Learning Techniques |
| topic | Materials Science Applied Physics Optics |
| url | https://arxiv.org/abs/2604.12451 |