Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Deep neural network-based cost function for metal cutting data assimilation

  • 8 Accesses

Abstract

Metal cutting is a complex process in machining that is typically not easily modeled via the finite element method (FEM) due to insufficient models or uncertain conditions. To improve FEM analysis of such a complex process, it is desirable to estimate the model parameters and uncertain conditions based on measurements of actual machining processes, which could be realized by data assimilation. However, the application of data assimilation to complex processes such as metal cutting is not straightforward because a comparison of the actual process and the corresponding FEM images is not trivial. To overcome this issue, we consider an extension of the cost function based on the object detection and classification capabilities of deep learning by evaluating the similarity between the FEM results and the images acquired during the actual machining process. The overall procedure is demonstrated by investigating a cutting chip in a turning process, whose shape depends on the workpiece material, cutting conditions, and the cutting tool. We first trained a deep neural network using chip images acquired from a turning experiment. This resulted in 85.5% detection and classification accuracy for testing data obtained from the same experiment. The trained network is then used to detect the images generated based on FEM. It was confirmed that the confidence score calculated using the trained deep neural network can be used to quantify the difference of the cutting chip shape generated by FEM. This preliminary study revealed that a realistic cutting chip shape can be generated by estimating the work-hardening exponent and the static friction coefficient in FEM based on images obtained during the turning process. This confirms that a deep learning-based cost function can be used to achieve image-based data assimilation.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

References

  1. 1.

    Lee J, Davari H, Singh J, Pandhare V (2018) Industrial artificial intelligence for industry 4.0-based manufacturing systems. Manuf Lett 18:20–23

  2. 2.

    Lauro CH, Brandao LC, Baldo D, Reis RA, Davim JP (2014) Monitoring and processing signal applied in machining processes – a review. Measurement 58:73–86

  3. 3.

    Herwan J, Kano S, Ryabov O, Sawada H, Kasashima N, Misaka T (2019) Retrofitting old CNC turning with an accelerometer at a remote location towards industry 4.0. Manuf Lett 21:56–59

  4. 4.

    Fischer A, Eberhard P (2014) Controlling vibrations of a cutting process using predictive control. Comput Mech 54:21–31

  5. 5.

    Parida AK, Maity K (2019) Modeling of machining parameters affecting flank wear and surface roughness in hot turning of Monel-400 using response surface methodology (RSM). Measurement 137:375–381

  6. 6.

    Upadhyay V, Jain PK, Metha NK (2013) In process prediction of surface roughness in turning of Ti-6Al-4V alloy using cutting parameters and vibration signals. Measurement 46:154–160

  7. 7.

    Herwan J, Kano S, Ryabov O, Sawada H, Watanabe M (2018) Comparing vibration sensor positions in CNC turning for a feasible application in smart manufacturing system. Int J Autom Technol 12:282–289

  8. 8.

    Wang P, Liu Z, Gao RX, Guo Y (2019) Heterogeneous data-driven hybrid machine learning for tool condition prognosis. CIRP Ann Manuf Technol 68(1):455–458

  9. 9.

    Eberhard P, Gaugele T (2013) Simulation of cutting processes using mesh-free Lagrangian particle methods. Comput Mech 51:261–278

  10. 10.

    Rodriguez Prieto JM, Carbonell JM, Cante JC, Oliver J, Jonsen P (2018) Generation of segmental chips in metal cutting modeled with the PFEM. Comput Mech 61:639–655

  11. 11.

    Sabel M, Sator C, Müller R (2014) A particle finite element method for machining simulations. Comput Mech 54:123–131

  12. 12.

    Harzallah M, Pottier T, Senatore J, Mousseigne M, Germain G, Landon Y (2017) Numerical and experimental investigations of Ti-6Al-4V chip generation and thermo-mechanical couplings in orthogonal cutting. Int J Mech Sci 134:189–202

  13. 13.

    Ilg C, Haufe A, Koch D, Stander N, Witowski K, Svedin A, Liewald M. Application of a full-field calibration concept for parameter identification of HS-steel with LS-OPT, 15th International LS-DYNA User Conference, June 10-12, 2018

  14. 14.

    Basudhar A (2019) Adaptive sampling using LS-OPT, 12th European LS-DYNA User Conference

  15. 15.

    Cavariani, S., Scattina, A., Scalera, S., Bianco, S., D’Aiuto, F., De Caro, D., Ghisleri, D., Luera, A., Tedesco, M. M., Ilg, C., A full-field calibration approach on material parameter identification, 12th European LS-DYNA User Conference, 2019

  16. 16.

    Zhang D, Zhang X-M, Ding H (2018) Inverse identification of material plastic constitutive parameters based on the DIC determined workpiece deformation fields in orthogonal cutting. Procedia CIRP 71:134–139

  17. 17.

    Muñoz-Sánchez A, González-Farias IM, Soldani XM, Miguélez MH (2011) Hybrid FE/ANN and LPR approach for the inverse identification of material parameters from cutting tests. Int J Adv Manuf Technol 54(1–4):21–33

  18. 18.

    Kalnay E (2003) Atmospheric modeling, data assimilation and predictability. Cambridge University Press, Cambridge

  19. 19.

    Togashi F, Misaka T, Löhner R, Obayashi S (2018) Using ensemble Kalman filter to determine parameters for computational crowd dynamics simulations. Eng Comput 35(7):2612–2628

  20. 20.

    Kikuchi R, Misaka T, Obayashi S (2016) Real-time prediction of unsteady flow based on POD reduced-order model and particle filter. Int J Comput Fluid Dyn 30(4):285–306

  21. 21.

    Misaka T, Obayashi S, Endo E (2008) Measurement-integrated simulation of clear air turbulence using a four-dimensional variational method. J Aircr 45(4):1217–1229

  22. 22.

    Jiao, L., Zhang, F., Liu, F., Yang, S., Li, L., Feng, Z., Qu, R., A survey of deep learning-based object detection, arXiv:1907.09408, 2019

  23. 23.

    Martínez-Arellano G, Terrazas G, Ratchev S (2019) Tool wear classification using time series imaging and deep learning. Int J Adv Manuf Technol 104:3647–3662. https://doi.org/10.1007/s00170-019-04090-6

  24. 24.

    Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S, Courville, A. C., Bengio, Y. Generative adversarial nets, Twenty-eighth Conference on Neural Information Processing Systems, NIPS, 2014

  25. 25.

    Jones DR, Schonlau M, Welch WJ (1998) Efficient global optimization of expensive black box functions. J Glob Optim 13:455–492

  26. 26.

    Koch D, Haufe A (2019) First step towards machine-learning supported material parameter identification, 12th European LS-DYNA User Conference

  27. 27.

    Sumelka W, Lodygowski T (2013) Reduction of the number of material parameters by ANN approximation. Comput Mech 52:287–300

  28. 28.

    Okinawa Prefecture Education Network, Education videos, Youtube, https://www.youtube.com/watch?v=fU7YXfmbNa0

  29. 29.

    Hallquist JO (May 1998) LS-DYNA theory manual. Livermore Software Technology Corporation, Livermore

  30. 30.

    Johnson GR, Cook WH (1985) Fracture characteristics of three metals subject to various strains, strain rates, temperatures and pressures. Eng Fract Mech 21(1):31–48

  31. 31.

    Paraview. https://www.paraview.org/

  32. 32.

    Redmon, J., Divvala, S., Girshick, R., Farhadi, A., You look only once: unified real-time object detection, arXiv:1506.02640, 2015

  33. 33.

    Redmon J, Farhadi A (2017) Yolo9000: better, faster, stronger, IEEE Conference on Computer Vision and Patter Recognition (CVPR), p 6517-6525

  34. 34.

    Redmon J, Farhadi A (2018) YOLOv3: an incremental improvement, arXiv:1804.02767

  35. 35.

    Redmon J. YOLOv3 website, https://pjreddie.com/darknet/yolo/

  36. 36.

    Jović S, Arsić N, Vukojević V, Anicic O, Vujičić S (2017) Determination of the important machining parameters on the chip shape classification by adaptive neuro-fuzzy technique. Precis Eng 48:18–23

  37. 37.

    Forrester AIJ, Sóbester A, Keane AJ (2007) Multi-fidelity optimization via surrogate modelling. Proc R Soc A 463:3251–3269

  38. 38.

    Öpöz TT, Chen X (2016) Chip formation mechanism using finite element simulation. J Mech Eng 62(11):636–646

  39. 39.

    Olleak AA, EI-Hofy HA. Prediction of cutting forces in high speed machining of Ti6Al4V using SPH method, Proceedings of the 10th ASME 2015 Manufacturing science and engineering conference, MSEC2015-9201, June 8-12, 2015

  40. 40.

    Attanasio A, Ceretti E, Rizzuti S, Umbrello D, Micari F (2008) 3D finite element analysis of tool Wear in machining. CIRP Ann Manuf Technol 57:61–64

Download references

Acknowledgments

The authors would like to thank the Okinawa Prefectural Comprehensive Education Center for providing high-quality videos of the turning process.

Author information

Correspondence to Takashi Misaka.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Misaka, T., Herwan, J., Kano, S. et al. Deep neural network-based cost function for metal cutting data assimilation. Int J Adv Manuf Technol (2020). https://doi.org/10.1007/s00170-020-04984-w

Download citation

Keywords

  • Turning
  • Metal cutting FEM
  • Data assimilation
  • Deep learning
  • Parameter estimation