Skip to main content
Log in

Surface roughness stabilization method based on digital twin-driven machining parameters self-adaption adjustment: a case study in five-axis machining

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

Surface roughness, which has a significant influence on fatigue strength and wear resistance, is an important technical parameter. In practical machining, it is unstable and may be larger than the acceptable surface roughness due to unstable machining process. This will seriously deteriorate the surface performance of the workpieces. Therefore, an effective surface roughness stabilization method is of great significance to improve machining efficiency and reduce machining cost. In this paper, a surface roughness stabilization method is proposed and illustrated by taking five-axis machining as an example. A self-learning surface roughness prediction model based on Pigeon-Inspired Optimization and Support Vector Machine is firstly constructed and its prediction error is only 8.69% in the initial stage. This model has the self-learning ability that the prediction accuracy can be improved with the increase of training data. Furthermore, a machining parameters self-adaption adjustment method based on digital twin is proposed to make the machined surface quality stable. In this method, considering the feasibility of practical machining operation, the cutter posture (i.e. lead angle and tilt angle in five-axis machining) and spindle speed are selected as the adjustable parameters. When the predicted surface roughness doesn’t meet the requirements, the Gradient Descent algorithm is applied to recalculate the new parameters for adjustment. According to the experimental results, the proposed method can stabilize surface roughness and improve the surface quality, which is vital for the precision manufacturing of complex workpiece. Meanwhile, it also greatly improves the intelligence level of manufacturing and production.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Benardos, P. G., & Vosniakos, G. C. (2003). Predicting surface roughness in machining: A review. International Journal of Machine Tools and Manufacture, 43(8), 833–844.

    Article  Google Scholar 

  • Chen, J. S., Huang, Y. K., & Chen, M. S. (2011). A study of the surface scallop generating mechanism in the ball-end milling process. International Journal of Machine Tools and Manufacture, 45(9), 1077–1084.

    Article  Google Scholar 

  • Cicek, A., Kivak, T., & Ekici, E. (2015). Optimization of drilling parameters using Taguchi technique and response surface methodology (RSM) in drilling of AISI 304 steel with cryogenically treated HSS drills. Journal of Intelligent Manufacturing, 26(2), 295–305.

    Article  Google Scholar 

  • Duan, H. B., & Qiao, P. X. (2014). Pigeon-inspired optimization: A new swarm intelligence optimizer for air robot path planning. International Journal of Intelligent Computing & Cybernetics, 7(1), 24–37.

    Article  Google Scholar 

  • Geng, L., Liu, P. L., & Liu, K. (2015). Optimization of cutter posture based on cutting force prediction for five-axis machining with ball-end cutters. International Journal of Advanced Manufacturing Technology, 78(5–8), 1289–1303.

    Article  Google Scholar 

  • Ghosh, G., Mandal, P., & Mondal, S. C. (2019a). Modeling and optimization of surface roughness in keyway milling using ANN, genetic algorithm, and particle swarm optimization. The International Journal of Advanced Manufacturing Technology, 100(5), 1223–1242.

    Article  Google Scholar 

  • Ghosh, A. K., Ullah, A. M. M. S., & Kubo, A. (2019b). Hidden Markov model-based digital twin construction for futuristic manufacturing systems. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 33(3), 317–331.

    Article  Google Scholar 

  • Ghosh, A. K., Ullah, A. M. M. S., Kubo, A., Akamatsu, T., & D’Addona, D. M. (2020). Machining phenomenon twin construction for industry 4.0: A case of surface roughness. Journal of Manufacturing and Materials Processing, 4(1), 11.

    Article  Google Scholar 

  • Huang, P. T. B., Zhang, H. J., & Lin, Y. C. (2017). Development of a Grey online modeling surface roughness monitoring system in end milling operations. Journal of Intelligent Manufacturing, 30, 1923–1936.

    Article  Google Scholar 

  • Karkalos, N. E., Galanis, N. I., & Markopoulos, A. P. (2016). Surface roughness prediction for the milling of Ti–6Al–4 V ELI alloy with the use of statistical and soft computing techniques. Measurement, 90, 25–35.

    Article  Google Scholar 

  • Li, Z. X., Zhang, Z. Y., Shi, J. C., & Wu, D. Z. (2019). Prediction of surface roughness in extrusion-based additive manufacturing with machine learning. Robotics and Computer-Integrated Manufacturing, 57, 488–495.

    Article  Google Scholar 

  • Liang, T., Yao, C. F., Ren, J. X., & Zhang, D. H. (2017). Effect of cutter path orientations on cutting forces, tool wear, and surface integrity when ball end milling TC17. International Journal of Advanced Manufacturing Technology, 88(9–12), 1–14.

    Google Scholar 

  • Lim, K., Zheng, P., & Chen, C. H. (2019). A state-of-the-art survey of Digital Twin: Techniques, engineering product lifecycle management and business innovation perspectives. Journal of Intelligent Manufacturing, 31, 1313–1337.

    Article  Google Scholar 

  • Liu, Y., Wan, M., Xing, W. J., Xiao, Q. B., & Zhang, W. H. (2018). Generalized actual inverse kinematic model for compensating geometric errors in five-axis machine tools. International Journal of Mechanical Sciences, 145, 299–317.

    Article  Google Scholar 

  • Liu, N., Wang, S. B., Zhang, Y. F., & Lu, W. F. (2016). A novel approach to predicting surface roughness based on specific cutting energy consumption when slot milling Al-7075. International Journal of Mechanical Sciences, 118, 13–20.

    Article  Google Scholar 

  • Lu, X. H., Hu, X. C., Jia, Z. Y., Liu, M. Y., Song, G., Qu, C. L., et al. (2017). Model for the prediction of 3D surface topography and surface roughness in micro-milling Inconel 718. International Journal of Advanced Manufacturing Technology, 94(1), 1–14.

    Google Scholar 

  • Lu, Y. Q., Liu, C., Wang, K. K., Huang, H. Y., & Xu, X. (2020). Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues. Robotics and Computer-Integrated Manufacturing, 61, 101837.

    Article  Google Scholar 

  • Munoz-Escalona, P., & Maropoulos, P. G. (2015). A geometrical model for surface roughness prediction when face milling Al 7075-T7351 with square insert tools. Journal of Manufacturing Systems, 36, 216–223.

    Article  Google Scholar 

  • Noordin, M. Y., Venkatesh, V. C., Sharif, S., Elting, S., & Abdullah, A. (2004). Application of response surface methodology in describing the performance of coated carbide tools when turning AISI 1045 steel. Journal of Materials Processing Technology, 145(1), 46–58.

    Article  Google Scholar 

  • Pan, Y. H., Wang, Y. H., Zhou, P., Yan, Y., & Guo, D. M. (2020). Activation functions selection for BP neural network model of ground surface roughness. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-020-01538-5.

    Article  Google Scholar 

  • Pimenov, D. Y., Bustillo, A., & Mikolajczyk, T. (2018). Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth. Journal of Intelligent Manufacturing, 29(5), 1045–1061.

    Article  Google Scholar 

  • Qiu, H. X., & Duan, H. B. (2020). A multi-objective pigeon-inspired optimization approach to UAV distributed flocking among obstacles. Information Sciences, 509, 515–529.

    Article  Google Scholar 

  • Rao, K. V., & Murthy, P. B. G. S. N. (2016). Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM, ANN and SVM. Journal of Intelligent Manufacturing, 29(7), 1533–1543.

    Google Scholar 

  • Redelinghuys, A. J. H., Basson, A. H., & Kruger, K. (2019). A six-layer architecture for the digital twin: A manufacturing case study implementation. Journal of Intelligent Manufacturing, 31(6), 1383–1402.

    Article  Google Scholar 

  • Shamshirband, S., Mohammadi, K., Khorasanizadeh, H., Yee, P. L., Lee, M., Petković, D., et al. (2016). Estimating the diffuse solar radiation using a coupled support vector machine–wavelet transform model. Renewable and Sustainable Energy Reviews, 56, 428–435.

    Article  Google Scholar 

  • Sun, Z. W., To, S., Zhang, S. J., & Zhang, G. Q. (2018). Theoretical and experimental investigation into non-uniformity of surface generation in micro-milling. International Journal of Mechanical Sciences, 140, 313–324.

    Article  Google Scholar 

  • Tangjitsitcharoen, S., Thesniyom, P., & Ratanakuakangwan, S. (2017). Prediction of surface roughness in ball-end milling process by utilizing dynamic cutting force ratio. Journal of Intelligent Manufacturing, 28(1), 13–21.

    Article  Google Scholar 

  • Tao, F., Zhang, M., Liu, Y. S., & Nee, A. Y. C. (2018). Digital twin driven prognostics and health management for complex equipment. CIRP Annals, 67(1), 169–172.

    Article  Google Scholar 

  • Tong, X., Liu, Q., Pi, S. W., & Xiao, Y. (2020). Real-time machining data application and service based on IMT digital twin. Journal of Intelligent Manufacturing, 31(5), 1113–1132.

    Article  Google Scholar 

  • Tuegel, E. J., Ingraffea, A. R., Eason, T. G., & Spottswood, S. M. (2011). Reengineering aircraft structural life prediction using a digital twin. International Journal of Aerospace Engineering, 2011, 154798. https://doi.org/10.1155/2011/154798.

    Article  Google Scholar 

  • Ullah, A. M. M. S. (2017). Surface roughness modeling using Q-sequence. Mathematical & Computational Applications, 22(2), 33.

    Article  Google Scholar 

  • Ullah, A. M. M. S. (2019). Modeling and simulation of complex manufacturing phenomena using sensor signals from the perspective of Industry 4.0. Advanced Engineering Informatics, 39, 1–13.

    Article  Google Scholar 

  • Ullah, A. M. M. S., Fuji, A., Kubo, A., Tamaki, J., & Kimura, M. (2015). On the surface metrology of bimetallic components. Machining Science & Technology An International Journal, 19(2), 339–359.

    Article  Google Scholar 

  • Ullah, A. M. M. S., Tamaki, J., & Kubo, A. (2010). Modeling and simulation of 3D surface finish of grinding. Advanced Materials Research, 126–128, 672–677.

    Article  Google Scholar 

  • Vapnik, V. N. (1999). An overview of statistical learning theory. IEEE Transactions on Neural Networks, 10(5), 988–999.

    Article  Google Scholar 

  • Vapnik, V. N. (2000). The nature of statistical learning theory. New York: Springer.

    Book  Google Scholar 

  • Wang, S. B. (2015). Automated five-axis tool path generation based on dynamic analysis. Singapore: National University of Singapore.

    Google Scholar 

  • Wang, S. B., Geng, L., Zhang, Y. F., Liu, K., & Ng, T. E. (2015). Cutting force prediction for five-axis ball-end milling considering cutter vibrations and run-out. International Journal of Mechanical Sciences, 96–97, 206–215.

    Article  Google Scholar 

  • Xu, L., Huang, C., Li, C., Wang, J., & Wang, X. (2020). An improved case based reasoning method and its application in estimation of surface quality toward intelligent machining. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-020-01573-2.

    Article  Google Scholar 

  • Zhao, Z. Y., Wang, S. B., Wang, Z. H., Liu, N., Wang, S. L., Ma, C., et al. (2019). Interference- and chatter-free cutter posture optimization towards minimal surface roughness in five-axis machining. International Journal of Mechanical Sciences, 171, 105395.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by National Key R&D Program of China (No. 2019YFB1703701) and Natural Science Foundation Project of Chongqing Science and Technology Commission (No. cstc2019jcyj-msxmX0058).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sibao Wang.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, Z., Wang, S., Wang, Z. et al. Surface roughness stabilization method based on digital twin-driven machining parameters self-adaption adjustment: a case study in five-axis machining. J Intell Manuf 33, 943–952 (2022). https://doi.org/10.1007/s10845-020-01698-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-020-01698-4

Keywords

Navigation