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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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Abstract

Ultra-precision machine tools are the foundation for ultra-precision manufacturing. In the era of Industry 4.0, monitoring the machine tool’s working condition is critical to control the machining quality. In a conventional setting, numerous sensors are retrofitted to the machine to monitor its condition effectively. This process could potentially increase the cost of the widespread application of Industry 4.0 technologies. In contrast to the method of retrofitting the machine tool, in this work, we propose an intelligent monitoring system that utilizes the equipment’s power consumption data to assess and determine the equipment states. The work also discusses the development of a G-code interpreter application used to develop an equipment working status matrix. The G-code interpreter application can generate the training data and extract features for the Deep Learning/Machine learning models. The feature extraction process can also be customized by providing template functions to the application. A densely connected convolutional neural network with multiple outputs was then developed to identify the machine state and predict the feedrate simultaneously. The model was able to identify the working component of the machine with an accuracy of \(\sim \) 94% and was able to predict the feed rate with a standard deviation (\(\sigma \)) of 21.900 from the energy consumption data. The overarching goal of the research work is to predict energy consumed, and augment anomaly detection for an ultra-precision CNC machine tool. The work presented here involves the identification of the equipment state and prediction of the equipment feedrate, and it will serve as a precursor.

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References

  • Aydın, İ, Karaköse, M., & Akın, E. (2015). Combined intelligent methods based on wireless sensor networks for condition monitoring and fault diagnosis. Journal of Intelligent Manufacturing, 26(4), 717–729.

    Article  Google Scholar 

  • Benkedjouh, T., Medjaher, K., Zerhouni, N., & Rechak, S. (2015). Health assessment and life prediction of cutting tools based on support vector regression. Journal of intelligent manufacturing, 26(2), 213–223.

    Article  Google Scholar 

  • Cai, W., Zhang, W., Hu, X., & Liu, Y. (2020). A hybrid information model based on long short-term memory network for tool condition monitoring. Journal of Intelligent Manufacturing, 31(6), 1497–1510.

    Article  Google Scholar 

  • Du, M., Wang, P., Wang, J., Cheng, Z., & Wang, S. (2019). Intelligent turning tool monitoring with neural network adaptive learning. Complexity, 2019, 21–42.

    Article  Google Scholar 

  • Escobar, C. A., McGovern, M. E., & Morales-Menendez, R. (2021). Quality 4.0: A review of big data challenges in manufacturing. Journal of Intelligent Manufacturing, 32(8), 2319–2334.

    Article  Google Scholar 

  • Feng, Y., Hong, Z., Li, Z., Zheng, H., & Tan, J. (2020). Integrated intelligent green scheduling of sustainable flexible workshop with edge computing considering uncertain machine state. Journal of Cleaner Production, 246, 119070.

    Article  Google Scholar 

  • Gadelmawla, E. (2017). Computer vision algorithms for measurement and inspection of external screw threads. Measurement, 100, 36–49.

    Article  Google Scholar 

  • Gao, R. X., Wang, L., Helu, M., & Teti, R. (2020). Big data analytics for smart factories of the future. CIRP annals, 69(2), 668–692.

    Article  Google Scholar 

  • Glaeser, A., Selvaraj, V., Lee, S., Hwang, Y., Lee, K., Lee, N., & Min, S. (2021). Applications of deep learning for fault detection in industrial cold forging. International Journal of Production Research, 59(16), 4826–4835.

    Article  Google Scholar 

  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning (pp. 12–13). MIT Press.

  • Hayashi, M., Yoshioka, H., & Shinno, H. (2008). An adaptive control of ultraprecision machining with an in-process micro-sensor. Journal of Advanced Mechanical Design, Systems, and Manufacturing, 2(3), 322–331.

    Article  Google Scholar 

  • He, Y., Wu, P., Wang, Y., Tao, F., & Hon, B. K. (2020). An opc ua based framework for predicting energy consumption of machine tools. Procedia CIRP, 90, 568–572.

    Article  Google Scholar 

  • Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700–4708).

  • Iliyas Ahmad, M., Yusof, Y., Daud, M. E., Latiff, K., Abdul Kadir, A. Z., & Saif, Y. (2020). Machine monitoring system: a decade in review. The International Journal of Advanced Manufacturing Technology, 108(11), 3645–3659.

    Article  Google Scholar 

  • Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning (pp. 448–456).

  • Kim, J., Lee, H., Jeong, S., & Ahn, S.-H. (2021). Sound-based remote real-time multi-device operational monitoring system using a convolutional neural network (cnn). Journal of Manufacturing Systems, 58, 431–441.

    Article  Google Scholar 

  • Kim, Y., Kim, T., Youn, B. D., & Ahn, S.-H. (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, 33(6), 1813–1828.

    Article  Google Scholar 

  • Lee, G.-Y., Kim, M., Quan, Y.-J., Kim, M.-S., Kim, T. J. Y., Yoon, H.-S., et al. (2018). Machine health management in smart factory: A review. Journal of Mechanical Science and Technology, 32(3), 987–1009.

    Article  Google Scholar 

  • Li, X., Zhang, W., & Ding, Q. (2019). Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction. Reliability engineering & system safety, 182, 208–218.

    Article  Google Scholar 

  • Li, X., Zhang, W., Ding, Q., & Sun, J.-Q. (2020). Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation. Journal of Intelligent Manufacturing, 31(2), 433–452.

    Article  Google Scholar 

  • Liang, Y., Lu, X., Li, W., & Wang, S. (2018). Cyber physical system and big data enabled energy efficient machining optimisation. Journal of cleaner Production, 187, 46–62.

    Article  Google Scholar 

  • Lim, K. Y. H., Zheng, P., & Chen, C.-H. (2020). A state-of-the-art survey of Digital Twin: Techniques, engineering product lifecycle management and business innovation perspectives. Journal of Intelligent Manufacturing, 31(6), 1313–1337.

    Article  Google Scholar 

  • Liu, C., Vengayil, H., Lu, Y., & Xu, X. (2019). A cyber-physical machine tools platform using opc ua and mtconnect. Journal of Manufacturing Systems, 51, 61–74.

    Article  Google Scholar 

  • Liu, L., Tian, S., Xue, D., Zhang, T., & Chen, Y. (2019). Industrial feedforward control technology: A review. Journal of Intelligent Manufacturing, 30(8), 2819–2833.

    Article  Google Scholar 

  • Luo, B., Wang, H., Liu, H., Li, B., & Peng, F. (2018). Early fault detection of machine tools based on deep learning and dynamic identification. IEEE Transactions on Industrial Electronics, 66(1), 509–518.

    Article  Google Scholar 

  • Madhusudana, C., Budati, S., Gangadhar, N., Kumar, H., & Narendranath, S. (2016). Fault diagnosis studies of face milling cutter using machine learning approach. Journal of Low Frequency Noise, Vibration and Active Control, 35(2), 128–138.

    Article  Google Scholar 

  • Masters, D., & Luschi, C. (2018). Revisiting small batch training for deep neural networks. arXiv preprint arXiv:1804.07612.

  • Nannapaneni, S., Mahadevan, S., Dubey, A., & Lee, Y.-T.T. (2021). Online monitoring and control of a cyber-physical manufacturing process under uncertainty. Journal of Intelligent Manufacturing, 32(5), 1289–1304.

    Article  Google Scholar 

  • O’Malley, T., Bursztein, E., Long, J., Chollet, F., Jin, H., Invernizzi, L., ... & Schäfer, F. (2019). Kerastuner. https://github.com/keras-team/keras-tuner. Accessed 2 April 2022.

  • Ou, J., Li, H., Huang, G., & Yang, G. (2021). Intelligent analysis of tool wear state using stacked denoising autoencoder with online sequential-extreme learning machine. Measurement, 167, 108153.

    Article  Google Scholar 

  • Pan, Y., Kang, R., Dong, Z., Du, W., Yin, S., & Bao, Y. (2022). On-line prediction of ultrasonic elliptical vibration cutting surface roughness of tungsten heavy alloy based on deep learning. Journal of Intelligent Manufacturing, 33(3), 675–685.

    Article  Google Scholar 

  • Sihag, N., & Sangwan, K. S. (2020). A systematic literature review on machine tool energy consumption. Journal of Cleaner Production, 275, 123125.

    Article  Google Scholar 

  • Smith, L.N. (2017). Cyclical learning rates for training neural networks. 2017 ieee winter conference on applications of computer vision (wacv) (pp. 464–472).

  • Song, H., Gao, H., Guo, L., Li, Y., & Dong, X. (2020). Cnc machine tool wear monitoring based on densely connected convolutional networks. 2020 prognostics and health management conference (phm-besançon) (pp. 36–41).

  • Thoppil, N. M., Vasu, V., & Rao, C. (2021). Health indicator construction and remaining useful life estimation for mechanical systems using vibration signal prognostics. International Journal of System Assurance Engineering and Management, 12(5), 1001–1010.

    Google Scholar 

  • Wang, J., Ye, L., Gao, R. X., Li, C., & Zhang, L. (2019). Digital twin for rotating machinery fault diagnosis in smart manufacturing. International Journal of Production Research, 57(12), 3920–3934.

  • Wang, Y., Zheng, L., & Wang, Y. (2021). Event-driven tool condition monitoring methodology considering tool life prediction based on industrial internet. Journal of Manufacturing Systems, 58, 205–222.

    Article  Google Scholar 

  • Xu, L., Huang, C., Li, C., Wang, J., Liu, H., & Wang, X. (2020). A novel intelligent reasoning system to estimate energy consumption and optimize cutting parameters toward sustainable machining. Journal of Cleaner Production, 261, 121160.

    Article  Google Scholar 

  • Yip, W. S., To, S., & Zhou, H. (2021). Current status, challenges and opportunities of sustainable ultra-precision manufacturing. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-021-01782-3

  • Zhang, X., Xu, L., Zhang, H., Jiang, Z., & Cai, W. (2021). Emergy based intelligent decision-making model for remanufacturing process scheme integrating economic and environmental factors. Journal of Cleaner Production, 291, 125247.

    Article  Google Scholar 

  • Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019). Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 115, 213–237.

    Article  Google Scholar 

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Acknowledgements

The material is based on the work supported by the Wisconsin Alumni Research Foundation (WARF, MSN237362). Authors gracefully acknowledge the donation of the ROBONANO \(\alpha \)-0iB to the University of Wisconsin Madison by FANUC Corporation, Japan. Both the first and the second authors have equal contributions towards this research work.

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Author notes

  1. Zhicheng Xu and Vignesh Selvaraj have contributed equally to this work.

Authors and Affiliations

  1. Mechanical Engineering, University of Wisconsin Madison, 1415 Engineering Drive, Madison, WI, 53703, USA

    Zhicheng Xu, Vignesh Selvaraj & Sangkee Min

  2. School of Automotive Engineering, Wuhan University of Technology, 122 Luoshi Road, Wuhan, 430070, Hubei, China

    Zhicheng Xu

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  1. Zhicheng Xu
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  2. Vignesh Selvaraj
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  3. Sangkee Min
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Correspondence to Sangkee Min.

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Xu, Z., Selvaraj, V. & Min, S. State identification of a 5-axis ultra-precision CNC machine tool using energy consumption data assisted by multi-output densely connected 1D-CNN model. J Intell Manuf 35, 147–160 (2024). https://doi.org/10.1007/s10845-022-02030-y

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