Improving Tool Wear Prediction with Synthetic Features from Conditional Generative Adversarial Networks

Amid the current digital transformation wave, predictive maintenance (PdM) using machine learning has become prevalent due to its potential to reduction for total cost and turnaround time in the manufacturing and MRO (maintenance, repair, and operations) sectors. Predicting machinery tool wear or remaining useful life is one of the essential applications in modern PdM. As a result, many deep learning based prediction methods emerged in recent years’ literature and continuously advancing the state-of-the-art. In this paper, we propose a tool wear prediction framework that orchestrates a range of machine learning techniques to enhance prediction accuracy. These techniques include sequence modeling with convolutional neural networks, synthetic data generation with conditional generative adversarial networks, and customized regularization strategy. Experiments were performed with two public milling datasets to evaluate the effectiveness of the proposed framework. Results show that our prediction model, sCNN-Ex, outperforms state-of-the-art tool wear prediction methods.