Selective laser cleaning of microbeads using deep learning.

ABSTRACT: Laser cleaning is widely used industrially to remove surface contaminants with high precision. Conventional methods, however, lack real-time monitoring and feedback loops, often necessitating over-machining to ensure complete contaminant removal, which leads to inefficient energy use and potential substrate damage. In this work, we demonstrate a concept of selective laser cleaning via the application of femtosecond laser pulses and polystyrene microbeads with a diameter of 15 μm. These microbeads model challenging scenarios in high-precision optical work and delicate surface treatments across laboratory and production settings. To enable adaptive, real-time cleaning, we integrated a neural network that predicts the sample's appearance after each laser pulse into a feedback loop, tailoring

SUBMITTER: Liu Y 

PROVIDER: S-EPMC12043982 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature