DeepTool: A deep learning framework for tool wear onset detection and remaining useful life prediction

Pooja Kamat; Satish Kumar; Ketan Kotecha

doi:10.1016/j.mex.2024.102965

DeepTool: A deep learning framework for tool wear onset detection and remaining useful life prediction

MethodsX. 2024 Sep 19:13:102965. doi: 10.1016/j.mex.2024.102965. eCollection 2024 Dec.

Authors

Pooja Kamat¹, Satish Kumar^{1

2}, Ketan Kotecha^{1

2}

Affiliations

¹ Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, Maharashtra, India.
² Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Pune, Maharashtra, India.

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.

Keywords: Autoencoder: LSTM; DeepTool: A Deep Learning Framework for Tool Wear Onset Detection and Remaining Useful Life Prediction; LSTM Encoder-Decoder; Milling; Remaining useful life; Tool-wear.