SpringerLink
Log in

A monitoring method for surface roughness of γ-TiAl alloy based on deep learning of time–frequency diagram

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract  

γ-TiAl alloy is a typically difficult material to machine, with common machining defects such as grain pull-out and material spalling during machining, resulting in component scrap. As a result, it is critical to investigate the monitoring method of surface roughness during milling. A new model for predicting surface roughness is proposed in this paper. The prediction problem of surface roughness is turned into a classification problem based on deep learning. The features of the force signal are extracted using a continuous wavelet transform, and the one-dimensional signal is converted into a two-dimensional time–frequency diagram to obtain additional information. The transfer learning mechanism is introduced to make the model faster and improve prediction accuracy. The overfitting problem is solved, and the model’s generalization ability is improved through batch normalization and a dropout layer. The results show that the method has high recognition accuracy, with a maximum classification accuracy of 98% and an average accuracy of 96.12%, i.e., a 10.52% improvement over the traditional model, allowing it to accurately realize monitoring of surface quality in milling processing.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

Instant access to the full article PDF.

Institutional subscriptions

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
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Yao C, Lin J, Wu D, Ren J (2018) Surface integrity and fatigue behavior when turning γ-TiAl alloy with optimized PVD-coated carbide inserts. Chin J Aeronaut 31(4):826–836. https://doi.org/10.1016/j.cja.2017.06.002

    Article  Google Scholar 

  2. Xu R, Li M, Zhao Y (2023) A review of microstructure control and mechanical performance optimization of γ-TiAl alloys. J Alloy Compd 932:183–201. https://doi.org/10.1016/j.jallcom.2022.167611

    Article  Google Scholar 

  3. Mantle AL, Aspinwall DK (2001) Surface integrity of a high speed milled gamma titanium aluminide. J Mater Process Technol 118(1):143–150. https://doi.org/10.1016/S0924-0136(01)00914-1

    Article  Google Scholar 

  4. Genc O, Unal R (2022) Development of gamma titanium aluminide (γ-TiAl) alloys: a review. J Alloy Compd 929:167–179. https://doi.org/10.1016/j.jallcom.2022.167262

    Article  Google Scholar 

  5. Bailey GW, McKernan S, Price RL, Walck SD, Charest PM, Gauvin R, Russ JC (2000) Characterization of surface roughness. Microsc Microanal 6(S2):916–917. https://doi.org/10.1016/0043-1648(62)90002-9

    Article  Google Scholar 

  6. Jeyapoovan T, Murugan M (2013) Surface roughness classification using image processing. Measurement 46(7):2065–2072. https://doi.org/10.1016/j.measurement.2013.03.014

    Article  Google Scholar 

  7. Chen S, Feng R, Zhang C, Zhang Y (2018) Surface roughness measurement method based on multi-parameter modeling learning. Measurement 129:664–676. https://doi.org/10.1016/j.measurement.2018.07.071

    Article  Google Scholar 

  8. Janiesch C, Zschech P, Heinrich K (2021) Machine learning and deep learning. Electron Mark 31(3):685–695. https://doi.org/10.1007/s12525-021-00475-2

    Article  Google Scholar 

  9. Wu TY, Lei KW (2019) Prediction of surface roughness in milling process using vibration signal analysis and artificial neural network. Int J Adv Manuf Technol 102(1):305–314. https://doi.org/10.1007/s00170-018-3176-2

    Article  Google Scholar 

  10. Pimenov DY, Bustillo A, Mikolajczyk T (2018) Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth. J Intell Manuf 29(5):1045–1061. https://doi.org/10.1007/s10845-017-1381-8

    Article  Google Scholar 

  11. Cheng M, Jiao L, Yan P, Li S, Dai Z, Qiu T, Wang X (2022) Prediction and evaluation of surface roughness with hybrid kernel extreme learning machine and monitored tool wear. J Manuf Process 84:1541–1556. https://doi.org/10.1016/j.jmapro.2022.10.072

    Article  Google Scholar 

  12. Guo W, Wu C, Ding Z, Zhou Q (2021) Prediction of surface roughness based on a hybrid feature selection method and long short-term memory network in grinding. Int J Adv Manuf Technol 112(9):2853–2871. https://doi.org/10.1007/s00170-020-06523-z

    Article  Google Scholar 

  13. Cai Z, Sun Y, Zhao Z, Li Y (2022) Ultrasonic pattern recognition method for surface roughness based on time-frequency analysis and deep learning. J Electr Eng Technol 37(15):3743–3752

    Google Scholar 

  14. Zolfaghari M, Gholami S (2021) A hybrid approach of adaptive wavelet transform, long short-term memory and ARIMA-GARCH family models for the stock index prediction. Expert Syst Appl 182:115149. https://doi.org/10.1016/j.eswa.2021.115149

    Article  Google Scholar 

  15. Pour M (2018) Determining surface roughness of machining process types using a hybrid algorithm based on time series analysis and wavelet transform. Int J Adv Manuf Technol 97(5):2603–2619. https://doi.org/10.1007/s00170-018-2070-2

    Article  Google Scholar 

  16. Nouhi S, Pour M (2021) Prediction of surface roughness of various machining processes by a hybrid algorithm including time series analysis, wavelet transform and multi view embedding. Measurement 184:109904. https://doi.org/10.1016/j.measurement.2021.109904

    Article  Google Scholar 

  17. Wojna Z, Ferrari V, Guadarrama S, Silberman N, Chen L-C, Fathi A, Uijlings J (2019) The devil is in the decoder: classification, regression and GANs. Int J Comput Vision 127(11):1694–1706. https://doi.org/10.1007/s11263-019-01170-8

    Article  Google Scholar 

  18. Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T, Wang X, Wang G, Cai J, Chen T (2018) Recent advances in convolutional neural networks. Pattern Recogn 77:354–377. https://doi.org/10.1016/j.patcog.2017.10.013

    Article  Google Scholar 

  19. D’souza RN, Huang P-Y, Yeh F-C (2020) Structural analysis and optimization of convolutional neural networks with a small sample size. Sci Rep 10(1):834. https://doi.org/10.1038/s41598-020-57866-2

    Article  Google Scholar 

  20. Xu Y, Zhang H (2022) Convergence of deep convolutional neural networks. Neural Netw 153:553–563. https://doi.org/10.1016/j.neunet.2022.06.031

    Article  MATH  Google Scholar 

  21. Kim J, Yoon H, Kim MS (2022) Tweaking deep neural networks. IEEE Trans Pattern Anal Mach Intell 44(9):5715–5728 (https://ieeexplore.ieee.org/document/9429980/)

    Google Scholar 

  22. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp 770–778. https://doi.org/10.1109/CVPR.2016.90

    Book  Google Scholar 

  23. Sharma G, Umapathy K, Krishnan S (2020) Trends in audio signal feature extraction methods. Appl Acoust 158:107–120. https://doi.org/10.1016/j.apacoust.2019.107020

    Article  Google Scholar 

  24. Cohen L (1989) Time-frequency distributions-a review. Proceed IEEE 77(7):941–981 (https://ieeexplore.ieee.org/document/30749)

    Article  Google Scholar 

  25. Wang Z, Chen J, Fan Y, Cheng Y, Wu X, Zhang J, Wang B, Wang X, Yong T, Liu W, Liu J, Du J, Yang W, Yang F (2020) Evaluating photosynthetic pigment contents of maize using UVE-PLS based on continuous wavelet transform. Comput Electron Agric 169:105–113. https://doi.org/10.1016/j.compag.2019.105160

    Article  Google Scholar 

  26. Zhuang F, Qi Z, Duan K, ** D, Zhu Y, Zhu H, **ong H, He Q (2021) A comprehensive survey on transfer learning. Proceed IEEE 109(1):43–76 (https://ieeexplore.ieee.org/document/9134370)

    Article  Google Scholar 

  27. Rogers AW, Vega-Ramon F, Yan J, del Río-Chanona EA, **g K, Zhang D (2022) A transfer learning approach for predictive modeling of bioprocesses using small data. Biotechnol Bioeng 119(2):411–422. https://doi.org/10.1002/bit.27980

    Article  Google Scholar 

  28. Wang Z, Liu Y (2020) Study of surface integrity of milled gamma titanium aluminide. J Manuf Process 56:806–819. https://doi.org/10.1016/j.jmapro.2020.05.021

    Article  Google Scholar 

  29. Genc O, Unal R (2022) Development of gamma titanium aluminide (γ-TiAl) alloys: a review. J Alloy Compd 929:167262. https://doi.org/10.1016/j.jallcom.2022.167262

    Article  Google Scholar 

  30. García Plaza E, Núñez López PJ (2018) Analysis of cutting force signals by wavelet packet transform for surface roughness monitoring in CNC turning. Mech Syst Signal Process 98:634–651. https://doi.org/10.1016/j.ymssp.2017.05.006

    Article  Google Scholar 

  31. Persson BNJ (2023) On the use of surface roughness parameters. Tribol Lett 71(2):29–30. https://doi.org/10.1007/s11249-023-01700-z

    Article  Google Scholar 

  32. Yang P, Wen C, Geng H, Liu P (2021) Intelligent fault diagnosis method for blade damage of quad-rotor UAV based on stacked pruning sparse denoising autoencoder and convolutional neural network. Machines 9(12):360

    Article  Google Scholar 

  33. Zhang H, Wu S, Zhang Z, Han L (2023) Rock joint roughness determination method based on deep learning of time–frequency​ spectrogram. Eng Appl Artif Intell 117:105–111. https://doi.org/10.1016/j.engappai.2022.105505

    Article  Google Scholar 

  34. Wang H, Li J, Wu H, Hovy E, Sun Y (2022) Pre-trained language models and their applications. Engineering 46–54. https://doi.org/10.1016/j.eng.2022.04.024

  35. Möhring HC, Eschelbacher S, Georgi P (2021) Machine learning approaches for real-time monitoring and evaluation of surface roughness using a sensory milling tool. Procedia CIRP 102:264–269. https://doi.org/10.1016/j.procir.2021.09.045

    Article  Google Scholar 

  36. Lin K, Zhao Y, Wang L, Shi W, Cui F, Zhou T (2023) MSWNet: a visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal solid waste sorting. Front Environ Sci Eng 17(6):77. https://doi.org/10.1007/s11783-023-1677-1

    Article  Google Scholar 

  37. Fleet D, Pajdla T, Schiele B, Tuytelaars T (2014) Visualizing and understanding convolutional networks. Computer Vision – ECCV. Cham. https://doi.org/10.1007/978-3-319-10590-1_53

    Chapter  Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of China (52175416, 51775280) and Science Center for Gas Turbine Project (P2022-A-IV-001–003).

Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Nan**g University of Science and Technology, Nan**g, 210094, China

    Yongxian Wu, Linyan Liu, Lei Huang & Zhenhua Wang

Authors
  1. Yongxian Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Linyan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lei Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhenhua Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to this work.

Corresponding authors

Correspondence to Linyan Liu or Zhenhua Wang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, Y., Liu, L., Huang, L. et al. A monitoring method for surface roughness of γ-TiAl alloy based on deep learning of time–frequency diagram. Int J Adv Manuf Technol 129, 2989–3007 (2023). https://doi.org/10.1007/s00170-023-12453-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-023-12453-3

Keywords

Search

Navigation