A parallel network model: Intelligent monitoring of tool wear under variable working conditions

Abstract

Accurate monitoring of tool wear states and wear values is crucial for reducing machine tool failures and ensuring machining accuracy and efficiency. However, wear monitoring faces significant challenges due to the imbalance of wear samples and the dynamic changes in the coupling relationships among multi-source sensing signals. Additionally, varying processing conditions further complicate the accurate tracking of wear. To address these challenges, an evolutionary spatio-temporal parallel network model is proposed. The model first employs a cyclic consistency classification enhancement network to accurately identify the real-time wear state of the tool. Then, it utilizes a parallel network to uncover the spatio-temporal coupling relationships within multi-source sensing data. Based on this, an evolutionary monitoring mechanism drives the continuous evolution and update of the model, adapting to real-time wear state and working condition changes, thus achieving precise tool wear monitoring under variable working conditions. Our self-built grinding wheel wear dataset and PHM2010 milling public dataset are used to verify the effectiveness of the method. Experimental results demonstrate that the proposed method improves prediction accuracy by 55.85 %, 10.26 %, and 50.14 % over existing methods on the C1, C4, and C6 datasets of PHM2010, respectively, while achieving a remarkable accuracy advantage of over 96.63 % in grinding wheel wear prediction.