Tool Condition Monitoring (TCM) is vital in CNC grinding to ensure dimensional precision, surface quality, and process efficiency. This study introduces a machine learning-based TCM framework that integrates vibration signal analysis, feature extraction, and metaheuristic optimization. Vibration data from industrial CNC grinding were processed to extract time- and frequency-domain features, which were then applied to tool wear prediction using Support Vector Regression (SVR). To improve accuracy and robustness, the Marine Predators Algorithm (MPA) was employed for hyperparameter tuning. The MPA-optimized SVR achieved the lowest mean squared error (0.0028), outperforming autoregressive (0.0088) and power-law (0.0084) models. These findings demonstrate the potential of combining vibration analysis with metaheuristic-optimized machine learning for intelligent, scalable, and real-time TCM. The proposed framework aligns with Industry 4.0 objectives by reducing downtime, improving reliability, and enabling adaptive monitoring in precision manufacturing.
Authors: Tuan A.Z. Rahman, A.As’arry, K.A.M.Rezali & LeongWen Chek
IoT Systems Lab, MIMOS Berhad, MRANTI Technology Park,Bukit Jalil 57000 Kuala Lumpur, Malaysia
Dept. of Mechanical and Manufacturing Engineering, Faculty of Engineering,Universiti Putra Malaysia 43400 UPM Serdang, Selangor, Malaysia
Full paper: https://journal.svam.my/index.php/javr/article/view/72/33
