- Tytuł:
- A comparative study of CNN, LSTM, BiLSTM, AND GRU architectures for tool wear prediction in milling processes
- Autorzy:
-
Zegarra, Fabio C.
Vargas-Machuca, Juan
Coronado, Alberto M. - Powiązania:
- https://bibliotekanauki.pl/articles/28407329.pdf
- Data publikacji:
- 2023
- Wydawca:
- Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
- Tematy:
-
tool wear
feature extraction
preprocessing
recurrent neural network - Opis:
- Accurately predicting machine tool wear requires models capable of capturing complex, nonlinear interactions in multivariate time series inputs. Recurrent neural networks (RNNs) are well-suited to this task, owing to their memory mechanisms and capacity to construct highly complex models. In particular, LSTM, BiLSTM, and GRU architectures have shown promise in wear prediction. This study demonstrates that RNNs can automatically extract relevant information from time series data, resulting in highly precise wear models with minimal feature engineering. Notably, this approach avoids the need for excessively large window sizes of data points during model training, which would increase model complexity and processing time. Instead, this study proposes a procedure that achieves low prediction errors with window sizes as small as 100 data points. By employing Bayesian hyperparameter optimization and two preprocessing techniques (detrend and offset), RMSE errors consistently fall below 10. A key difference in this study is the use of boxplots to provide a better representation of result variability, as opposed to solely reporting the best values. The proposed approach matches more complex state of-the-art. methods and offers a powerful tool for wear prediction in engineering applications.
- Źródło:
-
Journal of Machine Engineering; 2023, 23, 4; 122--136
1895-7595
2391-8071 - Pojawia się w:
- Journal of Machine Engineering
- Dostawca treści:
- Biblioteka Nauki
Artykuł