Acoustic signal-based cutting tool condition monitoring in woodworking using deep learning

Abstract Effective monitoring of wood cutting processes is crucial for maintaining cutting quality, extending tool life, and reducing energy and material waste. While conventional methods rely on cutting forces, power consumption, or temperature measurements, this study explores acoustic signal analysis as an alternative, leveraging its sensitivity to tool condition, cutting dynamics, and transient high-frequency events. The primary contribution of this study lies in the development of frequency–intensity–time representations derived from acoustic signals generated during particleboard cutting and in the subsequent classification of these patterns using deep learning algorithms. In parallel, cutting power consumption and tool temperature were analyzed as reference monitoring variables, with the aim to prove the hypothesis that deep learning-based classification of acoustic data could achieve comparable to or higher accuracy and reliability. Experimental investigations were conducted using two circular saw blades with different dullness conditions saw blades operating at feed rates of 10 m/min and 15 m/min. The workpiece material was melamine-faced particleboard. Statistical analysis revealed highly significant differences in power consumption between cutting conditions, while temperature measurements did not consistently distinguish between feed rates or tool wear states. Acoustic signals were acquired, pre-processed, and classified using several deep learning architectures. The highest classification accuracy of 92.00% was achieved using ConvNetXt-XL, while other architectures, including ResNet, EfficientNet, VGG19, DenseNet, MobileNet V2, Inception V3, and SqueezeNet achieved accuracies between 73.00% and 91.00%. The obtained results demonstrate the effectiveness of acoustic signals as a reliable monitoring variable for intelligent diagnostics and condition monitoring in wood machining processes.