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Tool wear condition monitoring based on a two-layer angle kernel extreme learning machine using sound sensor for milling process

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Abstract

Tool condition monitoring (TCM) in numerical control machines plays an essential role in ensuring high manufacturing quality. The TCM process is conducted according to the data obtained from one or more of a variety of sensors, among which acoustic sensors offer numerous practical advantages. However, acoustic sensor data suffer from strong noise, which can severely limit the accuracy of predictions regarding tool condition. The present work addresses this issue by proposing a novel TCM method that employs only a few appropriate feature parameters of acoustic sensor signals in conjunction with a two-layer angle kernel extreme learning machine. The two-layer network structure is applied to enhance the learning of features associated with complex nonlinear data, and two angle kernel functions without hyperparameters are employed to avoid the complications associated with the use of preset hyperparameters in conventional kernel functions. The proposed TCM method is experimentally demonstrated to achieve superior TCM performance relative to other state-of-the-art methods based on sound sensor data.

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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 

  • Li, L., Wang, Y., & Lin, K. (2020). Preventive maintenance scheduling optimization based on opportunistic production-maintenance synchronization. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-020-01588-9.

    Article  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 

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Acknowledgements

The authors are grateful for support from the National Natural Science Foundation of China (Grant Nos. 51405346 and 71471139), the Zhejiang Provincial Natural Science Foundation of China (Grant No. LY17E050005), and the Wenzhou City Public Industrial Science and Technology Project of China (Grant Nos. 2018G0116). We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.

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  1. College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, China

    Yuqing Zhou, Bintao Sun, Weifang Sun & Zhi Lei

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  1. Yuqing Zhou
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Correspondence to Weifang Sun.

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Zhou, Y., Sun, B., Sun, W. et al. Tool wear condition monitoring based on a two-layer angle kernel extreme learning machine using sound sensor for milling process. J Intell Manuf 33, 247–258 (2022). https://doi.org/10.1007/s10845-020-01663-1

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