Developing a Predictive Wear Model for Intelligent Tool Change Systems

Abstract The article addresses the challenge of reducing machining errors under tight tolerances, which can negatively affect workpiece quality. It highlights the need for modelling and compensating individual error types, particularly those caused by tool wear. Traditionally, tool wear compensation relies on experimentally determined absolute wear values, but nonlinearity in wear introduces discrepancies between modelled and actual machining processes. To address this, the article introduces a novel tool wear model integrated into an Intelligent Tool Change System. The model represents changes in tool edge reduction over time, allowing for tool position correction relative to the workpiece and signalling alarm states. It incorporates a first-order inertial adaptive model, enabling accurate forecasting of tool wear. These predictions are based on real-time geometric measurements collected during cutting by an Automatic Measurement Unit. The measurements are analyzed in the time domain to provide current process corrections and determine the tool lifecycle. A key feature of the model is its self-tuning capability, which adjusts parameters dynamically to handle limited data availability, improving prediction accuracy and reducing the complexity of parameter settings. The model's predictions were validated by comparing predicted wear values against actual measurements. Additionally, the integrated model was compared with a linear prediction model, demonstrating superior accuracy. To evaluate the model's performance, the article uses the normalized root mean square error (NRMSE) as the assessment metric. Results confirm that the first-order inertial adaptive model not only enhances accuracy over adaptive linear model but also provides reliable wear predictions, supporting effective tool change strategies in machining processes. This innovative approach offers significant improvements in managing machining errors and optimizing tool usage.