The multi-sensor-based measurement of machining signals and data fusion to develop predictive tool wear models for TiAlN-PVD coated carbide inserts during end milling of Inconel 617

Machining signals from the Force dynamometer, Acoustic Emission (AE), and Accelerometer are acquired and fused to develop Machine Learning (ML) models for tool wear monitoring of TiAlN-PVD coated carbide inserts. Milling experiments were performed on Inconel 617 with varied process parameter combinations until the tool flank wear met the failure criterion. Support Vector Regression, Random Forest Regression, and Long Short-Term Memory models are developed and compared based on a combination of sensor data fusion for tool wear predictions. It is observed that the Random Forest Regression approach can predict the tool wear with 94% accuracy compared to Support Vector Regression (85%) and Long Short-Term Memory (84%) models while using three-sensor data fusion. Further, the two-sensor data combination was used to test the relative efficacy of all the three developed machine learning tool wear models and found that the force dynamometer and the AE sensor fared better for Random Forest Regression and Long Short-Term Memory models in comparison to Support Vector Regression. For Support Vector Regression-based tool wear predictive models, force dynamometer and accelerometer data fusion performed better.