Tool wear condition monitoring based on a two-layer angle kernel extreme learning machine using sound sensor for milling process (original) (raw)
References
Ai, C. S., Sun, Y. J., He, G. W., Ze, X. B., Li, W., & Mao, K. (2012). The milling tool wear monitoring using the acoustic spectrum. The International Journal of Advanced Manufacturing Technology,61(5–8), 457–463. Google Scholar
Aliustaoglu, C., Ertunc, H. M., & Ocak, H. (2009). Tool wear condition monitoring using a sensor fusion model based on fuzzy inference system. Mechanical Systems and Signal Processing,23(2), 539–546. Google Scholar
Ammouri, A., & Hamade, R. (2014). Current rise criterion: A process-independent method for tool-condition monitoring and prognostics. The International Journal of Advanced Manufacturing Technology,72(1–4), 509–519. Google Scholar
Cai, W. L., Zhang, W. J., Hu, X. F., & Liu, Y. C. (2020). A hybrid information model based on long short-term memory network for tool condition monitoring. Journal of Intelligent Manufacturing,31, 1497–1510. Google Scholar
Chen, B. J., Chen, X. F., Li, B., He, Z. J., Cao, H. R., & Cai, G. (2011). Reliability estimation for cutting tools based on logistic regression model using vibration signals. Mechanical Systems and Signal Processing,25(7), 2526–2537. Google Scholar
Cho, Y., Saul, L. K. (2009). Kernel methods for deep learning, advances in neural information processing systems. In Conference on neural information processing systems 2009, Vancouver, British Columbia, Canada (pp. 342–350).
Chryssolouris, G., Domroese, M., & Beaulieu, P. (1992). Sensor synthesis for control of manufacturing processes. Journal of Engineering for Industry Transactions of the ASME,114(2), 158–174. Google Scholar
Cuka, B., & Kim, D. (2017). Fuzzy logic based tool condition monitoring for end-milling. Robotics and Computer-Integrated Manufacturing,47(10), 22–36. Google Scholar
Drouillet, C., Karandikar, J., Nath, C., Journeaux, A. C., Mansori, M. E., & Kurfess, T. (2016). Tool life predictions in milling using spindle power with the neural network technique. Journal of Manufacturing Processes,22(4), 161–168. Google Scholar
Gao, R., Wang, L., Teti, R., Dornfeld, D., Kumara, S., Mori, M., et al. (2015). Cloud-enabled prognosis for manufacturing. CIRP Annals,64(2), 749–772. Google Scholar
Gao, C., Xue, W., Ren, Y., & Zhou, Y. Q. (2017). Numerical control machine tool fault diagnosis using hybrid stationary subspace analysis and least squares support vector machine with a single sensor. Applied Sciences,7(4), 346. Google Scholar
Ghani, J., Rizal, M., Nuawi, M., Ghazali, M., & Haron, C. (2011). Monitoring online cutting tool wear using low-cost technique and user-friendly GUI. Wear,271(9–10), 2619–2624. Google Scholar
Ghosh, N., Ravi, Y. B., Patra, A., Mukhopadhyay, S., Paul, S., Mohanty, A. R., 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. Google Scholar
Hsieh, W., Lu, M., & Chiou, S. (2012). Application of backpropagation neural network for spindle vibration-based tool wear monitoring in micro-milling. The International Journal of Advanced Manufacturing Technology,61(1–4), 53–61. Google Scholar
Huang, G. B. (2015). What are extreme learning machines? Filling the gap between Frank Rosenblatt’s dream and John von Neumann’s puzzle. Cognitive Computation,7(3), 263–278. Google Scholar
Huang, G., Huang, G. B., & Song, S. (2015a). Trends in extreme learning machines: A review. Neural Networks,61, 32–48. Google Scholar
Huang, P., Ma, C., & Kuo, C. (2015b). A PNN self-learning tool breakage detection system in end milling operations. Applied Soft Computing,37, 114–124. Google Scholar
Huang, Z. W., Zhu, J. M., Lei, J. T., Li, X., & Tian, F. Q. (2020). Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations. Journal of Intelligent Manufacturing,31, 953–966. Google Scholar
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. Journal of Intelligent Manufacturing,29(8), 1873–1890. Google Scholar
Karandikar, J., Mcleay, T., Turner, S., & Schmitz, T. (2015). Tool wear monitoring using Naïve Bayes classifiers. The International Journal of Advanced Manufacturing Technology,77, 1613–1626. Google Scholar
Koike, R., Ohnishi, K., & Aoyama, T. (2016). A sensorless approach for tool fracture detection in milling by integrating multi-axial servo information. CIRP Annals,65(1), 385–388. Google Scholar
Konstantinos, S., & Athanasios, K. (2014). Reliability assessment of cutting tool life based on surrogate approximation methods. The International Journal of Advanced Manufacturing Technology,71(5), 1197–1208. Google Scholar
Kothuru, A., Nooka, S. P., & Liu, R. (2019). Application of deep visualization in CNN-based tool condition monitoring for end milling. Procedia Manufacturing,34, 995–1004. Google Scholar
Lee, B. (1999). Application of the discrete wavelet transform to the monitoring of tool failure in end milling using the spindle motor current. The International Journal of Advanced Manufacturing Technology,15(4), 238–243. Google Scholar
Lei, Z., Zhou, Y. Q., Sun, B. T., & Sun, W. F. (2019). An intrinsic timescale decomposition-based kernel extreme learning machine method to detect tool wear conditions in the milling process. The International Journal of Advanced Manufacturing Technology,106(3–4), 1203–1212. Google Scholar
Liu, C., Wang, G. F., & Li, Z. M. (2015). Incremental learning for online tool condition monitoring using ellipsoid artmap network model. Applied Soft Computing,35, 186–198. Google Scholar
Liu, M. K., Tseng, Y. H., & Tran, M. Q. (2019). Tool wear monitoring and prediction based on sound signal. The International Journal of Advanced Manufacturing Technology,103(1–4), 3361–3373. Google Scholar
Madhusudana, C. K., Kumar, H., & Narendranath, S. (2017). Face milling tool condition monitoring using sound signal. International Journal of System Assurance Engineering and Management,8(S2), 1643–1653. Google Scholar
Mathew, M., Pai, P., & Rocha, L. (2008). An effective sensor for tool wear monitoring in face milling: acoustic emission. Sadhana,33(3), 227–233. Google Scholar
Pechenin, V., Khaimovich, A., Kondratiev, A., & Bolotov, M. (2017). Method of controlling cutting tool wear based on signal analysis of acoustic emission for milling. Procedia Engineering,176, 246–252. Google Scholar
Prickett, P., & Johns, C. (1999). An overview of approaches to end milling tool monitoring. International Journal of Machine Tools and Manufacture,39(1), 105–122. Google Scholar
Ritou, M., Garnier, S., Furet, B., & Hascoet, J. (2014). Angular approach combined to mechanical model for tool breakage detection by eddy current sensors. Mechanical Systems and Signal Processing,44(1–2), 211–220. Google Scholar
Rizal, M., Ghani, J., Nuawi, M., & Che, H. (2014). A review of sensor system and application in milling process for tool condition monitoring. Research Journal of Applied Engineering & Technology,7(10), 2083–2097. Google Scholar
Salimiasl, A., & Özdemir, A. (2016). Analyzing the performance of artificial neural network (ANN)-, fuzzy logic (FL)-, and least square (LS)-based models for online tool condition monitoring. The International Journal of Advanced Manufacturing Technology,87(1–4), 1–14. Google Scholar
Sevilla, P., Herrera, G., Robles, J., & Jáuregui, J. (2011). Tool breakage detection in CNC high-speed milling based in feed-motor current signals. The International Journal of Advanced Manufacturing Technology,53(9–12), 1141–1148. Google Scholar
Sevilla, P., Jauregui, J., Herrera, G., & Robles, J. (2013). Efficient method for detecting tool failures in high-speed machining process. Journal of Engineering Manufacturing,227(4), 473–482. Google Scholar
Sevilla, P., Robles, J., Jauregui, J., & Jimenez, D. (2015a). FPGA-based reconfigurable system for tool condition monitoring in high-speed machining process. Measurement,64, 81–88. Google Scholar
Sevilla, P., Robles, J., Jauregui, J., & Lee, F. (2015b). Tool failure detection method for high-speed milling using vibration signal and reconfigurable bandpass digital filtering. The International Journal of Advanced Manufacturing Technology,81(5–8), 1–8. Google Scholar
Shao, H. D., Jiang, H. K., Li, X. Q., & Wu, S. P. (2018). Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine. Knowledge- Based Systems,140, 1–14. Google Scholar
Shawe, T., & Sun, S. (2011). A review of optimization methodologies in support vector machines. Neurocomputing,74(17), 3609–3618. Google Scholar
Siddhpura, A., & Paurobally, R. (2013). A review of flank wear prediction methods for tool condition monitoring in a turning process. The International Journal of Advanced Manufacturing Technology,65(1–4), 371–393. Google Scholar
Song, G., & Dai, Q. (2017). A novel double deep ELMs ensemble system for time series forecasting. Knowledge- Based Systems,134, 31–49. Google Scholar
Stavropoulos, P., Papacharalampopoulos, A., Vasiliadis, E., & Chryssolouris, G. (2016). Tool wear predictability estimation in milling based on multi-sensorial data. The International Journal of Advanced Manufacturing Technology,82(1–4), 509–521. Google Scholar
Torabi, A. J., Meng, J. E., Li, X., Lim, B. S., & Peen, G. (2016). Application of clustering methods for online tool condition monitoring and fault diagnosis in high-speed milling processes. IEEE Systems Journal,10(2), 721–732. Google Scholar
Vetrichelvan, G., Sundaram, S., Kumaran, S. S., & Velmurugan, P. (2014). An investigation of tool wear using acoustic emission and genetic algorithm. Journal of Vibration and Control,21, 3061–3066. Google Scholar
Wang, G., Yang, Y., Zhang, Y., & Xie, Q. (2014). Vibration sensor based tool condition monitoring using ν, support vector machine and locality preserving projection. Sensors and Actuators, A: Physical,209, 24–32. Google Scholar
Wang, G. F., Zhang, Y. C., Liu, C., Xie, Q. L., & Xu, Y. G. (2019). A new tool wear monitoring method based on multi-scale PCA. Journal of Intelligent Manufacturing,30, 113–122. Google Scholar
Wang, J., Xie, J., Zhao, R., Zhang, L., & Duan, L. (2017). Multisensory fusion based virtual tool wear sensing for ubiquitous manufacturing. Robotics & Computer Integrated Manufacturing,45, 47–58. Google Scholar
Wang, M., & Wang, J. (2012). CHMM for tool condition monitoring and remaining useful life prediction. The International Journal of Advanced Manufacturing Technology,59(5–8), 463–471. Google Scholar
Wang, P., & Gao, R. X. (2016). Stochastic tool wear prediction for sustainable manufacturing. Procedia CIRP,48, 236–241. Google Scholar
Wu, X. F., Liu, Y. H., & Bi, S. Z. (2019). Intelligent recognition of tool wear type based on convolutional neural networks. Computer Integrated Manufacturing Systems,25(8), 1–16. (in China). Google Scholar
Yen, C., Lu, M., & Chen, J. (2013). Applying the self-organization feature map (som) algorithm to ae-based tool wear monitoring in micro-cutting. Mechanical Systems and Signal Processing,34(1–2), 353–366. Google Scholar
Zhang, C., Yao, X., Zhang, J., & Jin, H. (2016). Tool condition monitoring and remaining useful life prognostic based on a wireless sensor in dry milling operations. Sensors,16(795), 1–20. Google Scholar
Zhang, H., Zhao, J., Wang, F., & Li, A. (2015). Cutting forces and tool failure in high-speed milling of titanium alloy TC21 with coated carbide tools. Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture,229(1), 20–27. Google Scholar
Zhao, R., Wang, D. Z., Yan, R. Q., Mao, K. Z., Shen, F., & Wang, J. J. (2018). Machine health monitoring using local feature-based gated recurrent unit networks. IEEE Transactions on Industrial Electronics,65(2), 1539–1548. Google Scholar
Zhou, Y. Q., Liu, X. F., Li, F. P., Sun, B. T., & Xue, W. (2015). An online damage identification approach for numerical control machine tools based on data fusion using vibration signals. Journal of Vibration and Control,21(15), 2925–2936. Google Scholar
Zhou, Y. Q., & Xue, W. (2018). Review of tool condition monitoring methods in milling processes. The International Journal of Advanced Manufacturing Technology,96, 2509–2523. Google Scholar
Zhu, K. P., & Vogel, B. (2014). Sparse representation and its applications in micro-milling condition monitoring: noise separation and tool condition monitoring. The International Journal of Advanced Manufacturing Technology,70(1–4), 185–199. Google Scholar