Real-time acoustic monitoring of laser paint removal based on deep learning

The acoustic signals generated during the laser paint removal process contain valuable information that reflects the state of paint removal. However, it is often overshadowed by complex environmental noise, posing significant challenges for real-time monitoring of paint removal based on acoustic signals. This paper introduces a real-time acoustic monitoring method for laser paint removal using deep learning techniques for the first time. Initially, the original acoustic signals from both clean and unclean paint removal processes are collected and denoised to extract time-domain, frequency-domain, and time-frequency-domain features. The mel frequency cepstral coefficients (MFCC) from the time-frequency domain are then used as inputs to train a convolutional neural network (CNN). The trained CNN model achieves a real-time discrimination accuracy of 97% and an AUC-ROC score of 99%, outperforming classical deep learning models of back propagation neural network (BP), support vector machine (SVM), and recurrent feedforward neural network (RF) that use time and frequency domain features as input. Furthermore, a real-time paint removal monitoring system based on this CNN model was developed, utilizing the NVIDIA Jetson Nano as the core controller. The system demonstrated continuous monitoring capabilities over a period of 1 hour, with a single judgment time of about 60 ms and an accuracy of 94.3%, thereby achieving real-time online monitoring.