State identification of a 5-axis ultra-precision CNC machine tool using energy consumption data assisted by multi-output densely connected 1D-CNN model
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DOI: 10.1007/s10845-022-02030-y
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- Yanan Pan & Renke Kang & Zhigang Dong & Wenhao Du & Sen Yin & Yan Bao, 2022. "On-line prediction of ultrasonic elliptical vibration cutting surface roughness of tungsten heavy alloy based on deep learning," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 675-685, March.
- Andrew Glaeser & Vignesh Selvaraj & Sooyoung Lee & Yunseob Hwang & Kangsan Lee & Namjeong Lee & Seungchul Lee & Sangkee Min, 2021. "Applications of deep learning for fault detection in industrial cold forging," International Journal of Production Research, Taylor & Francis Journals, vol. 59(16), pages 4826-4835, August.
- Kendrik Yan Hong Lim & Pai Zheng & Chun-Hsien Chen, 2020. "A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1313-1337, August.
- Yunhan Kim & Taekyum Kim & Byeng D. Youn & Sung-Hoon Ahn, 2022. "Machining quality monitoring (MQM) in laser-assisted micro-milling of glass using cutting force signals: an image-based deep transfer learning," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1813-1828, August.
- Jinjiang Wang & Lunkuan Ye & Robert X. Gao & Chen Li & Laibin Zhang, 2019. "Digital Twin for rotating machinery fault diagnosis in smart manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 57(12), pages 3920-3934, June.
- Weili Cai & Wenjuan Zhang & Xiaofeng Hu & Yingchao Liu, 2020. "A hybrid information model based on long short-term memory network for tool condition monitoring," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1497-1510, August.
- Saideep Nannapaneni & Sankaran Mahadevan & Abhishek Dubey & Yung-Tsun Tina Lee, 2021. "Online monitoring and control of a cyber-physical manufacturing process under uncertainty," Journal of Intelligent Manufacturing, Springer, vol. 32(5), pages 1289-1304, June.
- Lu Liu & Siyuan Tian & Dingyu Xue & Tao Zhang & YangQuan Chen, 2019. "Industrial feedforward control technology: a review," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2819-2833, December.
- Li, Xiang & Zhang, Wei & Ding, Qian, 2019. "Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 208-218.
- Xiang Li & Wei Zhang & Qian Ding & Jian-Qiao Sun, 2020. "Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 433-452, February.
- Nikhil M. Thoppil & V. Vasu & C. S. P. Rao, 2021. "Health indicator construction and remaining useful life estimation for mechanical systems using vibration signal prognostics," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(5), pages 1001-1010, October.
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- Sekar Kidambi Raju & Subhash Kannan, 2024. "Enhanced building energy harvesting through integrated piezoelectric materials and smart road traffic routing," Letters in Spatial and Resource Sciences, Springer, vol. 17(1), pages 1-31, December.
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Keywords
Ultra-precision CNC machine tool; Densely connected convolutional neural network; Axis detection; Smart manufacturing;All these keywords.
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