A Comparative Study of CNN, LSTM, BiLSTM, and GRU Architectures for Tool Wear Prediction in Milling Processes
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Laboratorio de Sistemas Inteligentes, EPIME, Universidad Nacional Tecnológica de Lima Sur (UNTELS), Peru
Submission date: 2023-06-13
Final revision date: 2023-10-10
Acceptance date: 2023-10-11
Online publication date: 2023-10-18
Corresponding author
Alberto M. Coronado   

Laboratorio de Sistemas Inteligentes, EPIME, Universidad Nacional Tecnológica de Lima Sur (UNTELS), Av. Bolivar S/N, sector 3 grupo 1, mz. A, sublote , 15834, Lima, Peru
Journal of Machine Engineering 2023;23(4):122-136
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.
KALPAKJIAN S., SCHMID S.R., 2013, Manufacturing Engineering and Technology, Prentice Hall International, Pearson/Prentice Hall.
XU H., ZHANG C., HONG G.S., ZHOU J., HONG J., WOON K.S., 2018, Gated Recurrent Units Based Neural Network for Tool Condition Monitoring, International Joint Conference on Neural Networks (IJCNN), IEEE, https://doi.org/10.1109/ijcnn.....
KURADA S., BRADLEY C., 1997, A Review of Machine Vision Sensors for Tool Condition Monitoring, Comput. Ind., 34/1, 55–72.
JAVED K., GOURIVEAU R., Li X., ZERHOUNI N., 2018, Tool Wear Monitoring And Prognostics Challenges: A Comparison of Connectionist Methods Toward an Adaptive Ensemble Model, J. Intel. Manuf., 29/8, 1873–1890.
REHORN A.G., JIANG J., ORBAN P.E., 2005, State-of-the-art Methods and Results in Tool Condition Monitoring: A Review, Int. J. Adv. Manuf. Technol., 26/7–8, 693–710.
ZHOU Y., XUE W., 2018, Review of Tool Condition Monitoring Methods in Milling Processes, Int. J. Adv. Manuf. Technol., 96/5–8, 2509–2523.
KARANDIKAR J., McLEAY T., TURNER S., SCHMITZ T., 2015, Tool Wear Monitoring Using Naïve Bayes Classifiers, Int. J. Adv. Manuf. Technol., 77/9–12, 1613–1626.
VETRICHELVAN G., SUNDARAM S., KUMARAN S.S., VELMURUGAN P., 2015, An Investigation of Tool Wear Using Acoustic Emission and Genetic Algorithm, J. Vib. Control, 21/15, 3061–3066.
TETI R., 2002, Machining of Composite Materials, CIRP Ann., 51/2, 611–634.
KUNTOGLU M., et al., 2020, A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends, Sensors, 21/1, 108.
SICK B., 2002, On-line and Indirect Tool Wear Monitoring in Turning with Artificial Neural Networks: a Review of More Than a Decade of Research, Mechanical Systems and Signal Processing, 16/4. 487–546, https://doi.org/10.1006/mssp.2....
DUTTA S., KANWAT A., PAL S.K., SEN R., 2013, Correlation Study of Tool Flank Wear with Machined Surface Texture in end Milling, Measurement:, 46/10, 4249–4260, https://doi.org/10.1016/j.meas....
GHOSH N., et al., 2007, Estimation of Tool Wear During CNC milling Using Neural Network-Based Sensor Fusion, Mechanical Systems and Signal Processing, 21/1, 466–479, https://doi.org/10.1016/j.ymss....
DROUILLET C., KARANDIKAR J., NATH C., JOURNEAUX A.-C., El MANSORI M.., KURFESS T., 2016, Tool Life Predictions in Milling Using Spindle Power with the Neural Network Technique, Journal of Manufacturing Processes:, 22, 161–168, https://doi.org/10.1016/j.jmap....
LI X., LIU X., YUE C., LIANG S.Y., WANG L., 2022, Systematic Review on Tool Breakage Monitoring Techniques in Machining Operations, International Journal of Machine Tools and Manufacture, 176, https://doi.org/10.1016/j.ijma....
ZHAO R., YAN R., CHEN Z., MAO K., WANG P., GAO R.X., 2019, Deep Learning and its Applications to Machine Health Monitoring, Mech. Syst. Signal Process., 115, 213–237.
HUANG C.-G., YIN X., HUANG H.-Z., LI Y.-F., 2020, An Enhanced Deep Learning-Based Fusion Prognostic Method for RUL Prediction, IEEE Trans. Reliab., 69/3,. 1097–1109.
LIU H., LIU Z., JIA W., LIN X., ZHANG S., 2020, A Novel Transformer-Based Neural Network Model for Tool Wear Estimation, Meas. Sci. Technol., 31/6, 065106.
QIAO H., WANG T., WANG P., QIAO S., ZHANG L., 2018, A Time-Distributed Spatiotemporal Feature Learning Method for Machine Health Monitoring with Multi-Sensor Time Series, Sensors, 18/9, https://doi.org/10.3390/s18092....
ZHAO R., YAN R., WANG J., MAO K., 2017, Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks, Sensors , 17/2, https://doi.org/10.3390/s17020....
WANG J., YAN J., LI C., GAO R.X., ZHAO R., 2019, Deep Heterogeneous GRU Model for Predictive Analytics in Smart Manufacturing: Application to Tool wear Prediction, Comput. Ind., 111, 1–14.
ZHAO R., WANG D., YAN R., MAO K., SHEN F., WANG J., 2018, Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks, IEEE Trans. Ind. Electron., 65/2, 1539–1548.
LI W., FU H., Han Z., Zhang X., Jin H., 2022, Intelligent tool wear prediction based on Informer encoder and stacked bidirectional gated recurrent unit, Robot. Comput. Integr. Manuf., 77, 102368, https://doi.org/10.1016/j.rcim....
WANG J., LI Y., ZHAO R., GAO R.X., 2020, Physics Guided Neural Network for Machining Tool Wear Prediction, J. Manuf. Syst., 57, 298–310.
ZHAO R., WANG J., YAN R., MAO K., 2016, Machine Health Monitoring with LSTM Networks, 10th International Conference on Sensing Technology (ICST), IEEE, https://doi.org/10.1109/icsens....
XU X., TAO Z., MING W., AN Q., CHEN M., 2020, Intelligent Monitoring and Diagnostics Using a Novel Integrated Model Based on Deep Learning and Multi-Sensor Feature Fusion, Measurement, 165/108086.
ZEGARRA F.C., VARGAS-MACHUCA J., CORONADO A.M., 2021, Comparison of CNN and CNN-LSTM Architectures for Tool Wear Estimation, IEEE Engineering International Research Conference (EIRCON), https://doi.org/10.1109/eircon....
LI X. et al., 2009, Fuzzy Neural Network Modelling for Tool Wear Estimation in Dry Milling Operation, PHM_CONF, 1/1, Accessed: Aug. 07, 2021. Available: http://papers.phmsociety.org/i... view/1403.
LeCUN Y., et al., 1989, Handwritten Digit Recognition with a Back-Propagation Network, in Advances in Neural Information Processing Systems, Available: https://proceedings.neurips.cc... 9556518c2fcb54-Paper.pdf.
HOCHREITER S., SCHMIDHUBER J., 1997, Long Short-Term Memory, Neural Comput., 9/8, 1735–1780.
SCHUSTER M., PALIWAL K.K., 1997, Bidirectional Recurrent Neural Networks, IEEE Trans. Signal Process., 45/11, 2673–2681.
CHO K., et al., 2014, Learning Phrase Representations Using RNN Encoder-Decoder for Statistical Machine Translation, arXiv Preprint arXiv:1406.1078, Available: http://arxiv.org/abs/1406.1078.
ZEGARRA F.C., VARGAS-MACHUCA J., CORONADO A.M., 2021, Tool wear and Remaining Useful Life (RUL) Prediction Based on Reduced Feature Set and Bayesian Hyperparameter Optimization, Prod. Eng., 16/4, 465–480.
ZEGARRA F.C., VARGAS-MACHUCA J., ROMAN-GONZALEZ A., CORONADO A.M., 2023, An Application of Machine Learning Methods to Cutting Tool Path Clustering and RUL Estimation in Machining, Journal of Machine Engineering, 23, https://doi.org/10.36897/jme/1....
CASUSOL A.J., ZEGARRA F.C., VARGAS-MACHUCA J., CORONADO A.M., 2021, Optimal Window Size for the Extraction of Features for Tool Wear Estimation, IEEE XXVIII International Conference on Electronics, Electrical Engineering and Computing (INTERCON), IEEE, Aug., https://doi.org/10.1109/interc.... 9532759.
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