Early detection of tool wear in electromechanical broaching machines by monitoring main stroke servomotors

This paper aims to provide researchers and engineers with evidence that sensorless machine variable monitoring can achieve tool wear monitoring in broaching in real production environments, reducing production errors, enhancing product quality, and facilitating zero-defect manufacturing. Additionally, broaching plays a crucial role in improving the quality of manufacturing products and processes. These aspects are especially pertinent in aeronautical manufacturing, which serves as the experimental case in this study. The research presents findings that establish a correlation between the variables of a broaching machine’s servomotors and the condition of the broaching tools. The authors propose an effective method for measuring broaching tool wear without external sensors and provide a detailed explanation of the methodology, enabling reproducibility of similar results. The results stem from three trials conducted on an electromechanical vertical broaching machine, utilizing cemented carbide grade broaching tools to broach a superalloy Inconel 718 test piece. The machine data collected facilitated the training of a set of machine learning models, accurately estimating tool wear on the broaches. Each model demonstrates high predictive accuracy, with a coefficient of determination surpassing 0.9. [Display omitted] •Servomotor torque in broaching machines is strongly correlated to tool wear.•Machine learning models trained on servomotor data can accurrately estimate broaching tool wear.•A sensorless approach is possible for broaching tool indirect monitoring.