Scanning Algorithm Optimization for Achieving Low-Roughness Surfaces Using Ultrashort Laser Pulses: A Comparative Study

Abstract

Femtosecond laser-assisted material surface modification is a rapidly growing field with numerous applications, including tribology, micromechanics, optofluidics, and medical implant treatment. For many of these applications, precise control of surface roughness after laser treatment is crucial, as it directly affects the final properties of the work surface. However, achieving low mean surface roughness values (<100 nm) is challenging due to the fundamental principles of laser light–matter interactions. The complex physical processes that occur during laser material interactions make it difficult to achieve the desired surface roughness, and only advanced scanning methods can potentially solve this issue. In our study, we analyzed laser scanning algorithms to determine the optimal method for producing surfaces with minimal roughness. We investigated how scanning parameters such as the overlap of modifications, the amount of successive line shift, and laser-scanner synchronization impact surface roughness. Using a numerical model, we obtained results that showed good agreement with experimentally acquired data. Our detailed theoretical and experimental analysis of different scanning methods can provide valuable information for the future optimization of minimal-roughness micromachining.