Work

Classifying paint colour using acoustic data from laser ablation

Year: 2025

Type: article

Abstract: We present an approach for identifying the paint colour and tone on a surface by using the acoustic signal collected during laser ablation. We trained convolutional neural networks to classify the colour (from 8 different colours) and the tone (the percentage of black in the paint). The colour was predicted with ∼91% accuracy and the tone with an R-value of 0.95. This technique has significant potential for supporting real-time optimisation in laser-material processing, particularly for high-precision laser cleaning, as well as broader applications where direct visual observation of the sample is not feasible.

Source: Optical Materials Express

DOI: 10.1364/ome.558459

Authors: James A. Grant-Jacob, Michalis N. Zervas, Ben Mills

Language: English

Topic: Laser Material Processing Techniques

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,843

Funder: Engineering and Physical Sciences Research Council