| Abstract | In a novel application of 1D Convolutional Neural Networks (1D-CNN), this study pioneers a tri-class classification framework for accurately forecasting the Remaining Useful Life (RUL) of milling tools. By harnessing the 1D-CNN’s innate capability to analyze raw time-series data, we eliminate the traditional bottleneck of extensive feature engineering. Our model undergoes rigorous validation using a leave-one-out cross-validation method, catering to the constraints of a limited dataset. When optimized, the model delivers compelling performance metrics: average accuracy, precision, and recall scores stand at 0.90 ± 0.02, 0.85 ± 0.12, and 0.87 ± 0.08, respectively. What sets this work apart is its dual utility: not only does it excel in tool health assessment, but its output also serves as a diagnostic tool for experimental setups. For instance, anomalies detected in the model’s predictions can act as early warnings for potential sensor malfunctions. Additionally, the model’s performance metrics offer invaluable guidance in optimizing experimental parameters, such as choosing the most efficient sampling rate. In summary, this study not only establishes the robustness of 1D-CNNs in assessing milling tool health but also unveils their untapped potential as diagnostic aids for fine-tuning experimental setups. |
|---|
