Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (4): 989-997.doi: 10.13229/j.cnki.jdxbgxb.20210778
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Fei WU(),Hao-ye NONG,Chen-hao MA
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1 | Kurada S, Bradley C. A review of machine vision sensors for tool condition monitoring[J]. Computers in Industry, 1997, 34(1): 55-72. |
2 | Mobley R K. An Introduction to Predictive Maintenance [M]. New York: Elsevier, 2002. |
3 | Gao D, Liao Z R, Lv Z, et al. Multi-scale statistical signal processing of cutting force in cutting tool condition monitoring[J]. The International Journal of Advanced Manufacturing Technology, 2015, 80(9): 1843-1853. |
4 | Antić A, Šimunović G, Šarić T, et al. A model of tool wear monitoring system for turning[J]. Tehnicki vjesnik/Technical Gazette, 2013, 20(2): 247-254. |
5 | Moia D F G, Thomazella I H, Aguiar P R, et al. Tool condition monitoring of aluminum oxide grinding wheel in dressing operation using acoustic emission and neural networks[J]. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2015, 37(2): 627-640. |
6 | 吴雪峰, 刘亚辉, 毕淞泽. 基于卷积神经网络刀具磨损类型的智能识别[J]. 计算机集成制造系统, 2020, 26(10): 2762-2771. |
Wu Xue-feng, Liu Ya-hui, Bi Song-ze. Intelligent recognition of tool wear type based on convolutional neural networks[J]. Computer Integrated Manufacturing Systems, 2020, 26(10): 2762-2771. | |
7 | Mohanraj T, Shankar S, Rajasekar R, et al. Tool condition monitoring techniques in milling process — a review[J]. Journal of Materials Research and Technology, 2020, 9(1): 1032-1042. |
8 | Cheng M H, Jiao L, Shi X C, et al. An intelligent prediction model of the tool wear based on machine learning in turning high strength steel[J]. Journal of Engineering Manufacture, 2020, 234(13): 1580-1597. |
9 | Kong D D, Chen Y J, Li N. Gaussian process regression for tool wear prediction[J]. Mechanical Systems and Signal Processing, 2018, 104: 556-574. |
10 | Du M H, Wang P X, Wang J H, et al. Intelligent turning tool monitoring with neural network adaptive learning[J]. Complexity, 2019(5A): 1-21. |
11 | Kong D D, Chen Y J, Li N, et al. Tool wear estimation in end milling of titanium alloy using NPE and a novel WOA-SVM model[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(7): 5219-5232. |
12 | Yen C L, Lu M C, Chen J. Applying the self-organizing feature map (SOM) algorithm to AE-based tool wear monitoring in micro-cutting[J]. Mechanical Systems and Signal Processing, 2013, 34(1/2): 353-366. |
13 | Ertunc H M, Loparo K A, Ocak H. Tool wear condition monitoring in drilling operations using hidden Markov models (HMMs)[J]. International Journal of Machine Tools and Manufacture, 2001, 41(9): 1363-1384. |
14 | 郭亮, 高宏力, 张一文, 等. 基于深度学习理论的轴承状态识别研究[J]. 振动与冲击, 2016, 35(12): 166-170, 195. |
Guo Liang, Gao Hong-li, Zhang Yi-wen, et al. Research on bearing condition monitoring based on deep learning[J]. Journal of Vibration and Shock, 2016, 35(12): 166-170, 195. | |
15 | Huang Z, Zhu J, Lei J, et al. Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations[J]. Journal of Intelligent Manufacturing, 2020, 31(4): 953-966. |
16 | Ma J, Luo D, Liao X, et al. Tool wear mechanism and prediction in milling TC18 titanium alloy using deep learning[J]. Measurement, 2021, 173: No. 108554. |
17 | Kong D, Chen Y, Li N. Monitoring tool wear using wavelet package decomposition and a novel gravitational search algorithm⁃least square support vector machine model[J]. Journal of Mechanical Engineering Science, 2019, 234(3): No. 095440621988731. |
18 | Xie Y, Zhang C, Liu Q. Tool wear status recognition and prediction model of milling cutter based on deep learning[J]. IEEE Access, 2020(9): 1616-1625. |
19 | Cheng M H, Jiao L, Shi X C, et al. An intelligent prediction model of the tool wear based on machine learning in turning high strength steel[J]. Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture, 2020, 234(13): 1580-1597. |
20 | EN 10084-2008. Case hardening steels⁃technical delivery conditions [S]. |
21 | . Tool-life testing with single-point turning tools [S]. |
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