Thomas Robert, Westphal Erik, Schnell Georg, Seitz Hermann
Chair of Microfluidics, Faculty of Mechanical Engineering and Marine Technology, University of Rostock, Justus-von-Liebig Weg 6, 18059 Rostock, Germany.
Department Life, Light & Matter, University of Rostock, Albert-Einstein-Str. 25, 18059 Rostock, Germany.
Micromachines (Basel). 2024 Apr 2;15(4):491. doi: 10.3390/mi15040491.
In ultrashort-pulsed laser processing, surface modification is subject to complex laser and scanning parameter studies. In addition, quality assurance systems for monitoring surface modification are still lacking. Automated laser processing routines featuring machine learning (ML) can help overcome these limitations, but they are largely absent in the literature and still lack practical applications. This paper presents a new methodology for machine learning classification of self-organized surface structures based on light microscopic images. For this purpose, three application-relevant types of self-organized surface structures are fabricated using a 300 fs laser system on hot working tool steel and stainless-steel substrates. Optical images of the hot working tool steel substrates were used to learn a classification algorithm based on the open-source tool Teachable Machine from Google. The trained classification algorithm achieved very high accuracy in distinguishing the surface types for the hot working steel substrate learned on, as well as for surface structures on the stainless-steel substrate. In addition, the algorithm also achieved very high accuracy in classifying the images of a specific structure class captured at different optical magnifications. Thus, the methodology proposed represents a simple and robust automated classification of surface structures that can be used as a basis for further development of quality assurance systems, automated process parameter recommendation, and inline laser parameter control.
在超短脉冲激光加工中，表面改性需要进行复杂的激光和扫描参数研究。此外，用于监测表面改性的质量保证系统仍然缺乏。具有机器学习（ML）功能的自动化激光加工程序有助于克服这些限制，但在文献中基本上没有，并且仍然缺乏实际应用。本文提出了一种基于光学显微镜图像对自组织表面结构进行机器学习分类的新方法。为此，使用300 fs激光系统在热作工具钢和不锈钢基板上制造了三种与应用相关的自组织表面结构类型。热作工具钢基板的光学图像用于基于谷歌的开源工具Teachable Machine学习分类算法。训练后的分类算法在区分所学热作钢基板的表面类型以及不锈钢基板上的表面结构方面取得了非常高的准确率。此外，该算法在对不同光学放大倍数下捕获的特定结构类别的图像进行分类时也取得了非常高的准确率。因此，所提出的方法代表了一种简单而稳健的表面结构自动分类方法，可作为质量保证系统、自动工艺参数推荐和在线激光参数控制进一步发展的基础。
