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Prediction of Surface Topography of Laser Interference Textured Steel Using Machine Learning and Surfalize.

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Title: Prediction of Surface Topography of Laser Interference Textured Steel Using Machine Learning and Surfalize.
Authors: Schell, Frederic1 frederic.schell@iws.fraunhofer.de, Zwahr, Christoph1, Lasagni, Andrés F.1,2
Source: Journal of Laser Micro / Nanoengineering. Nov2025, Vol. 20 Issue 3, p180-185. 6p.
Subjects: Surface topography, Machine learning, Ensemble learning, Random forest algorithms, Python programming language, Surface roughness measurement
Abstract: Understanding how laser process parameters influence surface topography is crucial for precise laser surface texturing. While the complex relationship between laser process and topographical parameters is difficult to model analytically, it lends itself well to machine learning. The requirement for large datasets of topographic parameters has generated a need for software solutions based in Python and equipped with batch functionality. In this work, we demonstrate the application of the self-developed Python library Surfalize to analyze a large dataset of direct laser interference patterning textured surfaces in terms of roughness parameters and train different machine learning models to predict topographical features from process parameters. The results show that both the random forest regressors and gradient boost machines exhibit the best predictive accuracy across a wide range of parameters, reaching R² values above 0.9 for amplitude related features such as the structure depth and arithmetic mean height. On the other hand, k-nearest neighbors and support vector machines perform significantly worse. Moreover, parameters from the functional family are predicted with less accuracy than amplitude or hybrid parameters. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Laser Micro / Nanoengineering is the property of Japan Laser Processing Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Prediction of Surface Topography of Laser Interference Textured Steel Using Machine Learning and Surfalize.
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  Data: <searchLink fieldCode="AR" term="%22Schell%2C+Frederic%22">Schell, Frederic</searchLink><relatesTo>1</relatesTo><i> frederic.schell@iws.fraunhofer.de</i><br /><searchLink fieldCode="AR" term="%22Zwahr%2C+Christoph%22">Zwahr, Christoph</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Lasagni%2C+Andrés+F%2E%22">Lasagni, Andrés F.</searchLink><relatesTo>1,2</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Laser+Micro+%2F+Nanoengineering%22">Journal of Laser Micro / Nanoengineering</searchLink>. Nov2025, Vol. 20 Issue 3, p180-185. 6p.
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  Data: <searchLink fieldCode="DE" term="%22Surface+topography%22">Surface topography</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Python+programming+language%22">Python programming language</searchLink><br /><searchLink fieldCode="DE" term="%22Surface+roughness+measurement%22">Surface roughness measurement</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Understanding how laser process parameters influence surface topography is crucial for precise laser surface texturing. While the complex relationship between laser process and topographical parameters is difficult to model analytically, it lends itself well to machine learning. The requirement for large datasets of topographic parameters has generated a need for software solutions based in Python and equipped with batch functionality. In this work, we demonstrate the application of the self-developed Python library Surfalize to analyze a large dataset of direct laser interference patterning textured surfaces in terms of roughness parameters and train different machine learning models to predict topographical features from process parameters. The results show that both the random forest regressors and gradient boost machines exhibit the best predictive accuracy across a wide range of parameters, reaching R² values above 0.9 for amplitude related features such as the structure depth and arithmetic mean height. On the other hand, k-nearest neighbors and support vector machines perform significantly worse. Moreover, parameters from the functional family are predicted with less accuracy than amplitude or hybrid parameters. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Journal of Laser Micro / Nanoengineering is the property of Japan Laser Processing Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.2961/jlmn.2025.03.2001
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 6
        StartPage: 180
    Subjects:
      – SubjectFull: Surface topography
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Ensemble learning
        Type: general
      – SubjectFull: Random forest algorithms
        Type: general
      – SubjectFull: Python programming language
        Type: general
      – SubjectFull: Surface roughness measurement
        Type: general
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      – TitleFull: Prediction of Surface Topography of Laser Interference Textured Steel Using Machine Learning and Surfalize.
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            NameFull: Zwahr, Christoph
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            NameFull: Lasagni, Andrés F.
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            – D: 01
              M: 11
              Text: Nov2025
              Type: published
              Y: 2025
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            – TitleFull: Journal of Laser Micro / Nanoengineering
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