Deep Learning Approach for Enhanced Transferability and Learning Capacity in Tool Wear Estimation
This work addresses tool wear monitoring for manufacturing systems, but it appears incremental as it builds on existing deep learning methods for a specific domain.
The study tackled tool wear estimation in manufacturing by proposing a deep learning approach that considers cutting parameters, and it outperformed conventional methods in transferability and rapid learning capabilities.
As an integral part of contemporary manufacturing, monitoring systems obtain valuable information during machining to oversee the condition of both the process and the machine. Recently, diverse algorithms have been employed to detect tool wear using single or multiple sources of measurements. In this study, a deep learning approach is proposed for estimating tool wear, considering cutting parameters. The model's accuracy and transferability in tool wear estimation were assessed with milling experiments conducted under varying cutting parameters. The results indicate that the proposed method outperforms conventional methods in terms of both transferability and rapid learning capabilities.