NGUYEN, VanThien ;NGUYEN, VietHung ;PHAM, VanTrinh . Deep Stacked Auto-Encoder Network Based Tool Wear Monitoring in the Face Milling Process. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 66, n.4, p. 227-234, april 2020. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/article/deep-stacked-auto-encoder-network-based-tool-wear-monitoring-in-the-face-mill-machining-process/>. Date accessed: 16 apr. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2019.6285.
Nguyen, V., Nguyen, V., & Pham, V. (2020). Deep Stacked Auto-Encoder Network Based Tool Wear Monitoring in the Face Milling Process. Strojniški vestnik - Journal of Mechanical Engineering, 66(4), 227-234. doi:http://dx.doi.org/10.5545/sv-jme.2019.6285
@article{sv-jmesv-jme.2019.6285,
author = {VanThien Nguyen and VietHung Nguyen and VanTrinh Pham},
title = {Deep Stacked Auto-Encoder Network Based Tool Wear Monitoring in the Face Milling Process},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {66},
number = {4},
year = {2020},
keywords = {face-mill machining; tool wear; stacked autoencoder (SAE); deep learning network (DLN); cast iron},
abstract = {Tool wear identification plays an important role in improving product quality and productivity in the manufacturing industry. The actual tool wear status with input cutting parameters may cause different levels of spindle vibration during the machining process. This research proposes an architecture comprising a deep learning network (DLN) to identify the actual wear state of machining tool. Firstly, data on spindle vibration signals are obtained from an acceleration sensor. The data are then pre-processed using the fast Fourier transform (FFT) method to reveal the relevant outstanding features in the frequency domain. Finally, the DLN is constructed based on stacked auto-encoders (SAE) and softmax, which is trained with the input data on the vibration features of the respective tool wear state. This DLN architecture is then used to identify the actual wear statuses of machining tool. The experimental results from the collected data show that the proposed DLN architecture is capable of identifying actual tool wear with high accuracy.},
issn = {0039-2480}, pages = {227-234}, doi = {10.5545/sv-jme.2019.6285},
url = {https://www.sv-jme.eu/article/deep-stacked-auto-encoder-network-based-tool-wear-monitoring-in-the-face-mill-machining-process/}
}
Nguyen, V.,Nguyen, V.,Pham, V. 2020 April 66. Deep Stacked Auto-Encoder Network Based Tool Wear Monitoring in the Face Milling Process. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 66:4
%A Nguyen, VanThien %A Nguyen, VietHung %A Pham, VanTrinh %D 2020 %T Deep Stacked Auto-Encoder Network Based Tool Wear Monitoring in the Face Milling Process %B 2020 %9 face-mill machining; tool wear; stacked autoencoder (SAE); deep learning network (DLN); cast iron %! Deep Stacked Auto-Encoder Network Based Tool Wear Monitoring in the Face Milling Process %K face-mill machining; tool wear; stacked autoencoder (SAE); deep learning network (DLN); cast iron %X Tool wear identification plays an important role in improving product quality and productivity in the manufacturing industry. The actual tool wear status with input cutting parameters may cause different levels of spindle vibration during the machining process. This research proposes an architecture comprising a deep learning network (DLN) to identify the actual wear state of machining tool. Firstly, data on spindle vibration signals are obtained from an acceleration sensor. The data are then pre-processed using the fast Fourier transform (FFT) method to reveal the relevant outstanding features in the frequency domain. Finally, the DLN is constructed based on stacked auto-encoders (SAE) and softmax, which is trained with the input data on the vibration features of the respective tool wear state. This DLN architecture is then used to identify the actual wear statuses of machining tool. The experimental results from the collected data show that the proposed DLN architecture is capable of identifying actual tool wear with high accuracy. %U https://www.sv-jme.eu/article/deep-stacked-auto-encoder-network-based-tool-wear-monitoring-in-the-face-mill-machining-process/ %0 Journal Article %R 10.5545/sv-jme.2019.6285 %& 227 %P 8 %J Strojniški vestnik - Journal of Mechanical Engineering %V 66 %N 4 %@ 0039-2480 %8 2020-04-17 %7 2020-04-17
Nguyen, VanThien, VietHung Nguyen, & VanTrinh Pham. "Deep Stacked Auto-Encoder Network Based Tool Wear Monitoring in the Face Milling Process." Strojniški vestnik - Journal of Mechanical Engineering [Online], 66.4 (2020): 227-234. Web. 16 Apr. 2024
TY - JOUR AU - Nguyen, VanThien AU - Nguyen, VietHung AU - Pham, VanTrinh PY - 2020 TI - Deep Stacked Auto-Encoder Network Based Tool Wear Monitoring in the Face Milling Process JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2019.6285 KW - face-mill machining; tool wear; stacked autoencoder (SAE); deep learning network (DLN); cast iron N2 - Tool wear identification plays an important role in improving product quality and productivity in the manufacturing industry. The actual tool wear status with input cutting parameters may cause different levels of spindle vibration during the machining process. This research proposes an architecture comprising a deep learning network (DLN) to identify the actual wear state of machining tool. Firstly, data on spindle vibration signals are obtained from an acceleration sensor. The data are then pre-processed using the fast Fourier transform (FFT) method to reveal the relevant outstanding features in the frequency domain. Finally, the DLN is constructed based on stacked auto-encoders (SAE) and softmax, which is trained with the input data on the vibration features of the respective tool wear state. This DLN architecture is then used to identify the actual wear statuses of machining tool. The experimental results from the collected data show that the proposed DLN architecture is capable of identifying actual tool wear with high accuracy. UR - https://www.sv-jme.eu/article/deep-stacked-auto-encoder-network-based-tool-wear-monitoring-in-the-face-mill-machining-process/
@article{{sv-jme}{sv-jme.2019.6285},
author = {Nguyen, V., Nguyen, V., Pham, V.},
title = {Deep Stacked Auto-Encoder Network Based Tool Wear Monitoring in the Face Milling Process},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {66},
number = {4},
year = {2020},
doi = {10.5545/sv-jme.2019.6285},
url = {https://www.sv-jme.eu/article/deep-stacked-auto-encoder-network-based-tool-wear-monitoring-in-the-face-mill-machining-process/}
}
TY - JOUR AU - Nguyen, VanThien AU - Nguyen, VietHung AU - Pham, VanTrinh PY - 2020/04/17 TI - Deep Stacked Auto-Encoder Network Based Tool Wear Monitoring in the Face Milling Process JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 66, No 4 (2020): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2019.6285 KW - face-mill machining, tool wear, stacked autoencoder (SAE), deep learning network (DLN), cast iron N2 - Tool wear identification plays an important role in improving product quality and productivity in the manufacturing industry. The actual tool wear status with input cutting parameters may cause different levels of spindle vibration during the machining process. This research proposes an architecture comprising a deep learning network (DLN) to identify the actual wear state of machining tool. Firstly, data on spindle vibration signals are obtained from an acceleration sensor. The data are then pre-processed using the fast Fourier transform (FFT) method to reveal the relevant outstanding features in the frequency domain. Finally, the DLN is constructed based on stacked auto-encoders (SAE) and softmax, which is trained with the input data on the vibration features of the respective tool wear state. This DLN architecture is then used to identify the actual wear statuses of machining tool. The experimental results from the collected data show that the proposed DLN architecture is capable of identifying actual tool wear with high accuracy. UR - https://www.sv-jme.eu/article/deep-stacked-auto-encoder-network-based-tool-wear-monitoring-in-the-face-mill-machining-process/
Nguyen, VanThien, Nguyen, VietHung, AND Pham, VanTrinh. "Deep Stacked Auto-Encoder Network Based Tool Wear Monitoring in the Face Milling Process" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 66 Number 4 (17 April 2020)
Strojniški vestnik - Journal of Mechanical Engineering 66(2020)4, 227-234
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
Tool wear identification plays an important role in improving product quality and productivity in the manufacturing industry. The actual tool wear status with input cutting parameters may cause different levels of spindle vibration during the machining process. This research proposes an architecture comprising a deep learning network (DLN) to identify the actual wear state of machining tool. Firstly, data on spindle vibration signals are obtained from an acceleration sensor. The data are then pre-processed using the fast Fourier transform (FFT) method to reveal the relevant outstanding features in the frequency domain. Finally, the DLN is constructed based on stacked auto-encoders (SAE) and softmax, which is trained with the input data on the vibration features of the respective tool wear state. This DLN architecture is then used to identify the actual wear statuses of machining tool. The experimental results from the collected data show that the proposed DLN architecture is capable of identifying actual tool wear with high accuracy.