Prediction of Surface Roughness in Functional Laser Surface Texturing Utilizing Machine Learning

Abstract

Functional laser surface texturing (LST) arose in recent years as a very powerful tool for tailoring the surface properties of parts and components to their later application. As a result, self-cleaning surfaces with an improved wettability, efficient engine components with optimized tribological properties, and functional implants with increased biocompatibility can be achieved today. However, with increasing capabilities in functional LST, the prediction of resulting surface properties becomes more and more important in order to reduce the development time of those functionalities. Consequently, advanced approaches for the prediction of the properties of laser-processed surfaces—the so-called predictive modelling—are required. This work introduces the concept of predictive modelling with respect to LST by means of direct laser writing (DLW). Fundamental concepts for the prediction of surface properties are presented employing machine learning approaches, theoretical concepts, and statistical methods. The modelling takes into consideration the used laser parameters, the analysis of topographical, and other process-relevant information in order to predict the resulting surface roughness. For this purpose, two different algorithms, namely artificial neural network and random forest, were trained with experimental data for stainless steel and Stavax surfaces. Statistical results indicate that both models can predict the desired surface topography with high accuracy, despite the use of a small dataset for the training process. The approaches can be used to further optimize the laser process regarding the process efficiency, overall throughput, and other process outcomes.

Keywords

• surface roughness
• artificial neural network
• direct laser writing
• random forest
• prediction