Comparative assessment of tool life models for solid end mills in machining applications

Optimizing cutting parameters is essential for reducing tool wear, extending tool life, and ensuring efficient machining processes. This can be achieved using different tool life models, such as Taylor’s tool life model or Extended Taylor’s tool life models or Colding’s tool life model, which can accurately optimize cutting parameters and enable informed decision-making for process improvements. Although Taylor’s tool life model is widely used in both industrial and academic settings, it is not regarded as the most precise model. Therefore, in this study, Taylor’s tool life model along with its extended versions are compared to Colding’s tool life model to assess their respective accuracies in predicting tool wear and optimizing machining parameters while dry machining two different workpiece materials: K1045 and Mild Steel. The Extended Taylor’s equation incorporating equivalent chip thickness demonstrated superior accuracy in predicting tool wear for K1045, with an error percentage of 6.13%. In contrast, Colding’s model exhibited the lowest error percentage (2.88%) for Mild Steel. In comparison, Taylor’s conventional tool life model showed higher deviations in prediction accuracy for both materials, highlighting its limitations in estimating tool wear accurately. The results suggest that incorporating additional machining parameters, such as equivalent chip thickness in the Extended Taylor’s model, enhances predictive accuracy, particularly for harder materials like K1045. Conversely, Colding’s model, which considers a broader range of machining factors, performed better in predicting tool wear for Mild Steel. These findings indicate that no single model consistently outperforms the others across different materials. The Extended Taylor’s model with equivalent chip thickness provided the most accurate predictions for K1045, whereas Colding’s model offered the best accuracy for Mild Steel, as reflected in their respective error percentages. This highlights the importance of selecting a tool life model based on material properties and machining conditions to ensure optimal performance. The study provides valuable insights for machining industries, enabling more informed decision-making when optimizing cutting parameters, reducing tool wear, and improving process efficiency. Future research could explore the integration of empirical models with data-driven approaches, such as AI-based predictive modelling, to further enhance the accuracy and adaptability of tool life estimation in diverse machining environments.