Thermal imaging–assisted neural network modeling for accurate tool wear prediction in dry turning of grey cast iron

Accurate tool wear monitoring is crucial for machining efficiency and product quality, yet traditional statistical methods often fail to capture complex non-linear interactions. This study addresses these limitations by employing Artificial Intelligence (AI), specifically a Multi-Layer Perceptron (MLP) artificial neural network, for tool wear prediction. Experiments involved dry turning of grey cast iron, acquiring real-time temperature data with an infrared thermal camera and measuring flank wear (VB). Cutting parameters (depth of cut, feed rate, cutting speed) were varied using a factorial Design of Experiments. Statistical analyses revealed that while correlations between wear and temperature exist, linear regression models exhibited high variability (R from 11.6% to over 95%), underscoring the inability of conventional approaches to fully model the complex relationships. The developed network, however, demonstrated high predictive capabilities. For fixed cutting conditions, it achieved an R of 0.999 and an MSE of 5.55E-05. For generalized prediction across a wider range of conditions, the optimal network configuration yielded an R of 0.998 and an MSE of 8.62E-05. This work confirms infrared thermography's reliability and justifies the use of neural networks to overcome the shortcomings of traditional statistical analyses. The AI-driven approach effectively models intricate variable relationships, offering a robust and precise solution for real-time tool wear monitoring and optimization in industrial machining, establishing links unnoticed by human perception.