Tool digital twin based on knowledge embedding for precision CNC machine tools: Wear prediction for collaborative multi-tool

Abstract

Tool wear prediction is vital for enhancing machining accuracy and ensuring production safety. However, challenges arise from non-processing data interference and missing tool wear samples, complicating the construction of accurate prediction models. Additionally, the complexity of collaborative multi-tool operations on precision computer numerical control (CNC) machine tools, where varying tool types and complex working conditions exist, further exacerbates the difficulty of achieving precise wear prediction. To address these challenges, this paper introduces a digital twin architecture for tool wear prediction, based on knowledge embedding. The proposed architecture is designed to predict the wear of multiple tools, incorporating modules for processing data screening, missing value completion, wear state classification, and so on. On the basis of obtaining high-quality sensing data and complete tool wear values, the wear state and machining process knowledge are embedded into the prediction process. A tool wear prediction model is then constructed based on a Kolmogorov-Arnold integrated time convolutional network (KA-TCN), so as to achieve accurate prediction of multi-tool wear. The effectiveness of the method is validated using data from two grinding wheel wear test platforms and two milling datasets, PHM2010 and NASA. Experimental results demonstrate that the knowledge embedded KA-TCN model outperforms existing approaches, improving prediction accuracy by over 22.4 % on the milling dataset, and by 76.4 % in grinding wheel wear prediction compared to classical methods.