Abstract
The surface quality of tungsten heavy alloy parts has an important influence on its service performance. The accurate on-line prediction of surface roughness in ultra-precision cutting of tungsten heavy alloy has always been the difficulty of research. In this paper, the ultrasonic elliptical vibration cutting technology is used for ultra-precision machining of tungsten heavy alloy. Based on the idea of deep learning, the surface roughness is discretized, and the fitting problem in surface roughness is transformed into a classification problem. The generalization ability of the prediction model is improved by introducing batch standardization and Dropout. The relationship between the vibration signal and the surface roughness is established. Experimental results show that the model can achieve on-line prediction of cutting surface roughness. The prediction accuracy rate can be improved by more than 10% compared with the direct fitting method.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
All data generated or analyzed during this study are included in this published article.
References
Amini, S., Nouri Hosseinabadi, H., & Sajjady, S. A. (2016). Experimental study on effect of micro textured surfaces generated by ultrasonic vibration assisted face turning on friction and wear performance. Applied Surface Science, 390, 633–648.
Bishop, C. M. (1995). Regularization and complexity control in feed-forward networks. In Proceedings international conference on artificial neural networks ICANN’95, pp. 141–148.
Brehl, D. E., & Dow, T. A. (2008). Review of vibration-assisted machining. Precision Engineering, 32(3), 153–172.
Domen, T., Samo, S., Jure, S., & Danijel, S. (2020). Segmentation-based deep-learning approach for surface-defect detection. Journal of Intelligent Manufacturing, 31(3), 759–776.
Gao, Y. (2014). Influence of local surface strengthening on fatigue properties of components with holes of an a-100 steel. Cailiao Rechuli Xuebao/Transactions of Materials & Heat Treatment, 35(5), 160–164.
Gao, X., Li, C., Li, Y., & Duong, C. V. (2014). Diamond tool wear reduction by combining ultrasonic elliptical vibration with graphite particle atmosphere. Advanced Materials Research, 1027, 32–35.
German, R. M., Bourguignon, L. L., & Rabin, B. H. (1985). Microstructure limitations of high tungsten content heavy alloys. JOM Journal of the Minerals Metals and Materials Society, 37(8), 36–39.
Gong, W., Kwak, I., Pota, P., Koyano-Nakagawa, N., & Garry, D. J. (2018). DrImpute: imputing dropout events in single cell RNA sequencing data. BMC Bioinformatics, 19(1), 220.
Huang, B., & Fan, J. (2001). Research and application of nano tungsten material. China Tungsten Industry, 16(5–6), 38–44.
Huang, Z., Zhu, J., Lei, J., Li, X., & Tian, F. (2020). Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations. Journal of Intelligent Manufacturing, 31(4), 953–966.
Kaufmann, M., & Neu, R. (2007). Tungsten as first wall material in fusion devices. Fusion Engineering and Design, 82(5–14), 521–527.
Kim, S., Choi, Y., & Lee, M. (2015). Deep learning with support vector data description. Neurocomputing, 165(OCT.1), 111–117.
Li, X., Jia, X., Yang, Q., & Lee, J. (2020). Quality analysis in metal additive manufacturing with deep learning. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-020-01549-2.
Liao, L., Jin, W., & Pavel, R. (2016). Enhanced restricted boltzmann machine with prognosability regularization for prognostics and health assessment. IEEE Transactions on Industrial Electronics, 63(11), 7076–7083.
Lin, J., Han, J., Zhou, X., Hao, Z., & Lu, M. (2016). Study on predictive model of cutting force and geometry parameters for oblique elliptical vibration cutting. International Journal of Mechanical Sciences, 117, 43–52.
Man, C., Ting, H., Zhang, S., & Yang, L. (2016). Importance of processing benchmark unity to the machining accuracy of component. Machine Tool & Hydraulics, 44(8), 13–15.
Moriwaki, T., & Shamoto, E. (1995). Ultrasonic elliptical vibration cutting. CIRP Annals—Manufacturing Technology, 44, 31–34.
Plaza, E. G., & Lopez, P. J. N. (2017). Surface roughness monitoring by singular spectrum analysis of vibration signals. Mechanical systems and signal processing, 84(pt. A), 516–530.
Plaza, E. G., & López, P. J. N. (2018). Application of the wavelet packet transform to vibration signals for surface roughness monitoring in CNC turning operations. Mechanical Systems and Signal Processing, 98, 902–919.
Rabin, B. H., Bose, A., & German, R. M. (1989). Characteristics of liquid phase sintered tungsten heavy alloys. International Journal of Powder Metallurgy, 25, 21–26.
Shamoto, E., & Moriwaki, T. (1994). Study on elliptical vibration cutting. CIRP Annals—Manufacturing Technology, 43(1), 35–38.
Shao, H., Jiang, H., Lin, Y., & Li, X. (2018). A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders. Mechanical Systems And Signal Processing, 102(MAR.1), 278–297.
Sharma, V. S., Dhiman, S., Sehgal, R., & Sharma, S. K. (2008). Estimation of cutting forces and surface roughness for hard turning using neural networks. Journal of Intelligent Manufacturing, 19(4), 473–483.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1), 1929–1958.
Sukthomya, W., & Tannock, J. (2005). The training of neural networks to model manufacturing processes. Journal of Intelligent Manufacturing, 16(1), 39–51.
Tian, H., Ren, D., Li, K., & Zhao, Z. (2020). An adaptive update model based on improved Long Short Term Memory for online prediction of vibration signal. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-020-01556-3.
Wang, J., Li, S., An, Z., Jiang, X., Qian, W., & Ji, S. (2019). Batch-normalized deep neural networks for achieving fast intelligent fault diagnosis of machines. Neurocomputing, 329, 53–65.
Zhang, B. S., & Kang, Z. J. (1999). Piercing properties and application of high-density tungsten alloy. China Tungsten Industry, z1, 178–181.
Zhang, W., Li, C., Peng, G., Chen, Y., & Zhang, Z. (2018). A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mechanical Systems and Signal Processing, 100, 439–453.
Zhao, Z., Li, Y., Liu, C., & Gao, J. (2020). On-line part deformation prediction based on deep learning. Journal of Intelligent Manufacturing, 31, 561–574.
Zhao, M., Wang, F., & Liang, R. (1983). Application of high specific gravity alloy in national defense industry and civil industry. Powder Metallurgy Technology, 1(6), 13–18.
Funding
This study was funded by Science Challenge Project (TZ2018006-0101-01), the National Natural Science Foundation of China (51975095) and National Science and Technology Major Project (2017-VII-0002-0095).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Pan, Y., Kang, R., Dong, Z. et al. On-line prediction of ultrasonic elliptical vibration cutting surface roughness of tungsten heavy alloy based on deep learning. J Intell Manuf 33, 675–685 (2022). https://doi.org/10.1007/s10845-020-01669-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-020-01669-9