Abstract
In recent years, the growing popularity of artificial neural networks has urged more and more researchers to try introduce these methods to the machining field, with some of them actually producing good results. The acquisition of cutting data often means higher cost and time, limiting the application of neural network in the machining sector, to a certain extent. In this paper, for the task of cutting force prediction, a “transfer network” was established, based on data obtained by simulation, combined with the theory and method in the field of transfer learning. Compared to “ordinary network”, that is, traditional back-propagation neural network based on experimental samples alone, transfer network exhibits obvious performance advantages. On one hand, this means that, using the same experimental samples, the prediction error of transfer network will be controlled; while on the other hand, when the same prediction error is achieved, the number of experimental samples required by the transfer network will be less.
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References
Asiltürk, I., & Çunkaş, M. (2011). Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method. Expert Systems with Applications, 38(5), 5826–5832. https://doi.org/10.1016/j.eswa.2010.11.041.
Borgwardt, K. M., Gretton, A., Rasch, M. J., Kriegel, H. P., Schölkopf, B., & Smola, A. J. (2006). Integrating structured biological data by kernel maximum mean discrepancy. Bioinformatics, 22(14), e49–e57. https://doi.org/10.1093/bioinformatics/btl242.
Cao, P., Zhang, S., & Tang, J. (2018). Preprocessing-free gear fault diagnosis using small datasets with deep convolutional neural network-based transfer learning. IEEE Access, 6, 26241–26253. https://doi.org/10.1109/ACCESS.2018.2837621.
Ghifary, M., Kleijn, W. B., & Zhang, M. (2014). Domain adaptive neural networks for object recognition. In Pacific Rim international conference on artificial intelligence (pp. 898–904). Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_76.
Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., & Smola, A. (2012). A kernel two-sample test. Journal of Machine Learning Research, 13(Mar), 723–773.
Jurkovic, Z., Cukor, G., Brezocnik, M., & Brajkovic, T. (2018). A comparison of machine learning methods for cutting parameters prediction in high speed turning process. Journal of Intelligent Manufacturing, 29(8), 1683–1693. https://doi.org/10.1007/s10845-016-1206-1.
Long, M., Cao, Y., Wang, J., & Jordan, M. I. (2015). Learning transferable features with deep adaptation networks. arXiv preprint arXiv:1502.02791.
Özel, T., & Nadgir, A. (2002). Prediction of flank wear by using back propagation neural network modeling when cutting hardened H-13 steel with chamfered and honed CBN tools. International Journal of Machine Tools and Manufacture, 42(2), 287–297. https://doi.org/10.1016/S0890-6955(01)00103-1.
Pan, S. J., & Yang, Q. (2009). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. https://doi.org/10.1109/TKDE.2009.191.
Radhakrishnan, T., & Nandan, U. (2005). Milling force prediction using regression and neural networks. Journal of Intelligent Manufacturing, 16(1), 93–102. https://doi.org/10.1007/s10845-005-4826-4.
Rao, K. V., Murthy, B. S. N., & Rao, N. M. (2014). Prediction of cutting tool wear, surface roughness and vibration of work piece in boring of AISI 316 steel with artificial neural network. Measurement, 51, 63–70. https://doi.org/10.1016/j.measurement.2014.01.024.
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), 1145–1158. https://doi.org/10.1007/s00170-016-8548-x.
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. https://doi.org/10.1007/s10845-008-0097-1.
Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., & Liu, C. (2018). A survey on deep transfer learning. In International conference on artificial neural networks (pp. 270–279). Springer, Cham. https://doi.org/10.1007/978-3-030-01424-7_27.
Tandon, V., & El-Mounayri, H. (2001). A novel artificial neural networks force model for end milling. The International Journal of Advanced Manufacturing Technology, 18(10), 693–700. https://doi.org/10.1007/s001700170011.
Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., & Darrell, T. (2014). Deep domain confusion: Maximizing for domain invariance. arXiv preprint arXiv:1412.3474.
Vaishnav, S., Agarwal, A., & Desai, K. A. (2019). Machine learning-based instantaneous cutting force model for end milling operation. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-019-01514-8.
Wang, L. Y., Huang, H. H., West, R. W., Li, H. J., & Du, J. T. (2018). A model of deformation of thin-wall surface parts during milling machining process. Journal of Central South University, 25(5), 1107–1115. https://doi.org/10.1007/s11771-018-3810-z.
Yeganefar, A., Niknam, S. A., & Asadi, R. (2019). The use of support vector machine, neural network, and regression analysis to predict and optimize surface roughness and cutting forces in milling. The International Journal of Advanced Manufacturing Technology, 105(1–4), 951–965. https://doi.org/10.1007/s00170-019-04227-7.
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014). How transferable are features in deep neural networks? In Advances in neural information processing systems (pp. 3320–3328).
Zhang, R., Tao, H., Wu, L., & Guan, Y. (2017). Transfer learning with neural networks for bearing fault diagnosis in changing working conditions. IEEE Access, 5, 14347–14357. https://doi.org/10.1109/ACCESS.2017.2720965.
Zhang, F. C., Wang, Q., & Yang, R. (2014). Analysis to milling force base on AdvantEdge finite element analysis. In Advanced materials research (Vol. 981, pp. 895–898). Trans Tech Publications Ltd. https://doi.org/10.4028/www.scientific.net/AMR.981.895.
Acknowledgements
This project was supported by National Science and Technology Major Project (2018ZX04011001), the Major Program of Shandong Province Natural Science Foundation (ZR2018ZA0401, ZR2018ZB0521) and Shandong Science Fund for Distinguished Young Scholars (JQ201715).
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Wang, J., Zou, B., Liu, M. et al. Milling force prediction model based on transfer learning and neural network. J Intell Manuf 32, 947–956 (2021). https://doi.org/10.1007/s10845-020-01595-w
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DOI: https://doi.org/10.1007/s10845-020-01595-w