Machine learning assisted real-time acoustic monitoring of laser cleaning in Heritage conservation

Laser-assisted cleaning has become an indispensable tool in heritage conservation due to its precision, control, and environmentally friendly nature. However, the complexity of deposition layers and the fragile condition of original surfaces necessitate careful monitoring to avoid irreversible damage. This work explores the integration of machine learning-assisted real-time acoustic monitoring in laser cleaning processes to enhance conservation efforts. By combining acoustic signals generated during laser-material interaction with machine learning, we elevate the precision and reliability of laser cleaning in the delicate context of cultural heritage restoration.