Intelligent Tool-Wear Prediction Based on Informer Encoder and Bi-Directional Long Short-Term Memory
Abstract
:1. Introduction
- This study proposed a new and effective tool-wear monitoring and evaluation method. This is the first time that a combination of an informer encoder and the Bi-LSTM model has been used for tool-wear monitoring. The experimental results show that this method is superior to other methods in terms of related evaluation indexes.
- The informer encoder was employed as the global feature extractor for multichannel long-term feature sequences, and computational efficiency was enhanced by employing sparse self-attention.
- The Bi-LSTM module was used to enhance the ability to capture the feature dependence of long-distance time series.
2. Model Theory
2.1. Scaled Dot-Product Attention
2.2. Prob-Sparse Self-Attention
2.3. Informer Encoder
2.3.1. Multi-Head Attention
2.3.2. Position-Wise Feedforward Networks
2.3.3. Residual Connections and Layer Normalization
2.4. Distilling Layer
2.5. Bi-Directional Long Short-Term Memory
3. Methods: The IE-Bi-LSTM Model
4. Experimental Results
4.1. Dataset Descriptions
4.2. Data Preprocessing
4.3. Experimental Environment and Hyperparameter Configuration
4.4. Experimental Environment and Hyperparameter Configuration
4.5. Experimental Results and Analysis
4.6. Model Module Analysis
5. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Equipment | Type |
---|---|
CNC milling machine | Roders Tech RFM760 |
Dynamometer | Kistler 9265B |
Charge amplifier | Kistler 5019A |
Acoustic emission sensor | Kistler AE sensor |
Cutters | 3-flute ball carbide milling cutters |
Data acquisition card | DAQ NI PCI 1200 |
Abrasion measuring apparatus | LEICA MZ12 microscope |
Parameter | Value |
---|---|
Spindle | 10,400/(r/min) |
Feed rate | 1555 (mm/min) |
Depth of cut (y direction, radial) | 0.125 (mm) |
Depth of cut (z direction, axial) | 0.2 (mm) |
Sampling rate | 50 (KHz) |
Workpiece material | Stainless steel (HRC52) |
Parameters | Learning Rate | Epoch | Batch Size | Dropout |
---|---|---|---|---|
Values | 0.001 | 100 | 32 | 0.2 |
Parameters | Warmup | FC neurons | H | Activation |
Values | 20 | 64/64/1 | 3 | ReLu |
Models | Datasets | |||||
---|---|---|---|---|---|---|
C1 | C4 | C6 | ||||
MAE | RMSE | MAE | RMSE | MAE | RMSE | |
RNN [21] | 13.1 | 15.6 | 16.7 | 20 | 25.5 | 32.91 |
Bi-LSTM [40] | 12.8 | 14.6 | 10.9 | 14.2 | 14.7 | 17.7 |
CNN-LSTM [41] | 11.18 | 13.77 | 9.39 | 11.85 | 11.34 | 14.33 |
Deep-LSTM [21] | 8.3 | 12.1 | 8.7 | 10.2 | 15.2 | 18.9 |
HLLSTM [40] | 6.6 | 8 | 6 | 7.5 | 7.1 | 8.8 |
TBNN [31] | 4.294 | 6.116 | / | / | 7.772 | 9.553 |
CTNN [42] | 3.634 | 5.358 | / | / | 7.531 | 9.209 |
LF-GRU [43] | 4.2 | 5.4 | 6.9 | 8.3 | 5.8 | 8.2 |
DH-GRU [44] | 3.7 | 4.66 | 7.07 | 8.73 | 5.08 | 6.94 |
IE-SBIGRU [36] | 3.694 | 5.056 | 5.189 | 6.884 | 3.398 | 4.527 |
Proposed model | 2.68 | 3.23 | 3.09 | 3.91 | 3.37 | 4.27 |
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Xie, X.; Huang, M.; Liu, Y.; An, Q. Intelligent Tool-Wear Prediction Based on Informer Encoder and Bi-Directional Long Short-Term Memory. Machines 2023, 11, 94. https://doi.org/10.3390/machines11010094
Xie X, Huang M, Liu Y, An Q. Intelligent Tool-Wear Prediction Based on Informer Encoder and Bi-Directional Long Short-Term Memory. Machines. 2023; 11(1):94. https://doi.org/10.3390/machines11010094
Chicago/Turabian StyleXie, Xingang, Min Huang, Yue Liu, and Qi An. 2023. "Intelligent Tool-Wear Prediction Based on Informer Encoder and Bi-Directional Long Short-Term Memory" Machines 11, no. 1: 94. https://doi.org/10.3390/machines11010094
APA StyleXie, X., Huang, M., Liu, Y., & An, Q. (2023). Intelligent Tool-Wear Prediction Based on Informer Encoder and Bi-Directional Long Short-Term Memory. Machines, 11(1), 94. https://doi.org/10.3390/machines11010094