Enhancing Cutting Sound Quality in Tool Wear Monitoring via Hybrid Domain Loss UNet Network

AI-driven tool wear monitoring models playa crucial role in the manufacturing sector by accurately forecasting and identifying tool degradation. This capability enables the reduction of downtime and ensures the maintenance of cutting quality. Nevertheless, these models encounter obstacles in noisy manufacturing settings, where environmental variables may disrupt sensor data, impacting the precision of wear predictions. As a result, this article presents a novel UNet-based noise reduction model designed to eliminate diverse environmental noise from cutting sounds. This model is trained using hybrid signals in both the time-domain and frequency domain as part of the loss function. It enables the model to capture both temporal and spectral characteristics of the data, allowing for a more comprehensive representation of the signal's behavior. Experiments show that the Signal-to-Noise Ratio (SNR) can be effectively increased by over 3dB compared to the baseline across various cutting workpieces. Additionally, the proposed method exhibits superior robustness against various types and levels of noise. The results demonstrate that the quality of the cutting sound can be enhanced by over 7dB and 4dB respectively, following the application of the noise reduction technique.