Abstract
Milling tool availability and its useful life estimation is essential for optimisation, reliability and cost reduction in milling operations. This work presents DeepTool, a deep learning-based system that predicts the service life of the tool and detects the onset of its wear. DeepTool showcases a comprehensive feature extraction process, and a self-collected dataset of sensor data from milling tests carried out under different cutting settings to extract relevant information from the sensor signals. The main contributions of this study are:•Self-Collected Dataset: Makes use of an extensive, self-collected dataset to record precise sensor signals during milling.•Advanced Predictive Modeling: Employs hybrid autoencoder-LSTM and encoder-decoder LSTM models to estimate tool wear onset and predict its remaining useful life with over 95 % R2 accuracy score.•Comprehensive Feature Extraction: Employs an efficient feature extraction technique from the gathered sensor data, emphasising both time-domain and frequency-domain aspects associated with tool wear.