Skip to main content
Log in

Tool wear classification based on machined surface images using convolution neural networks

  • Published:
Sādhanā Aims and scope Submit manuscript

Abstract

Among several factors that are having a profound impact on the overall machining process efficiency, cutting tool wear is the most significant one. Monitoring and identification of cutting tool wear state well before to its failure is important to achieve superior machining quality and profitable production. With the recent advancements in computational hardware, significant amount of research is being carried out on using deep learning techniques, in specific, convolution neural networks (CNN) for developing cutting tool wear monitoring system. Although, few researchers reported the use of CNN as a pathway to tool wear classification problems with significant results, the fundamental methodology adopted by these techniques still needs to be investigated. Hence, in the present work, a deep CNN architecture is designed by choosing appropriate hyper-parameters and a CNN model is developed by selecting proper training parameters for cutting tool wear classification. Machined surface images acquired during turning operation performed on mild steel components under dry condition by uncoated carbide inserts as cutting tool are used as input data to the CNN model for predicting the tool condition. The proposed model, whose classification performance is independent of machining conditions, has capability to extract the features and classify the cutting tool among the two classes (i.e., unworn and worn classes). Accuracies of 96.3% and 99.9% are realized for classification of tool flank wear from raw and minimally pre-processed (contrast enhanced) machined surface images, respectively.

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

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8

Similar content being viewed by others

References

  1. Gao R, Wang L, Teti R, Dornfeld D, Kumara S, Mori M and Helu M 2015 Cloud-enabled prognosis for manufacturing. CIRP Ann. 64: 749–772

    Article  Google Scholar 

  2. Wang L, Adamson G, Holm M and Moore P 2012 A review of function blocks for process planning and control of manufacturing equipment. J. Manuf. Syst. 31: 269–279

    Article  Google Scholar 

  3. Peng T and Xu X 2014 Energy-efficient machining systems: a critical review. Int. J. Adv. Manuf. Technol. 72: 1389–1406

    Article  Google Scholar 

  4. Wu X, Liu Y, Zhou X and Mou A 2019 Automatic identification of tool wear based on convolutional neural network in face milling process. Sensors 19: 3817

    Article  Google Scholar 

  5. Lins R G, de Araujo P R M and Corazzim M 2020 In-process machine vision monitoring of tool wear for Cyber-Physical Production Systems. Robot. Comput. Integr. Manuf. 61: 101859

    Article  Google Scholar 

  6. Terrazas G, Martínez-Arellano G, Benardos P and Ratchev S 2018 Online tool wear classification during dry machining using real time cutting force measurements and a CNN approach. J. Manuf. Mater. Process. 2: 72

    Google Scholar 

  7. Liu M-K, Tseng Y-H and Tran M-Q 2019 Tool wear monitoring and prediction based on sound signal. Int. J. Adv. Manuf. Technol. 103: 3361–3373

    Article  Google Scholar 

  8. Hui Y, Mei X, Jiang G, Tao T, Pei C and Ma Z, 2019, Milling Tool Wear State Recognition by Vibration Signal Using a Stacked Generalization Ensemble Model. Shock Vib. 2019

  9. Abou-El-Hossein K and Kops N 2017 Investigation on the use of cutting temperature and tool wear in the turning of mild steel bars. J. Mech. Eng. Sci. 11: 3038–3045

    Article  Google Scholar 

  10. Dutta S, Pal S K, Sen R, Dutta S, Pal S K and Sen R 2014 Digital image processing in machining. Springer, Berlin

    Book  Google Scholar 

  11. Mannan M A, Kassim A A and Jing M 2000 Application of image and sound analysis techniques to monitor the condition of cutting tools. Pattern Recognit. Lett. 21: 969–979

    Article  Google Scholar 

  12. Kassim A A, Mian Z and Mannan M A 2004 Connectivity oriented fast Hough transform for tool wear monitoring. Pattern Recognit. 37: 1925–1933

    Article  Google Scholar 

  13. Kassim A A, Mian Z and Mannan M A 2006 Tool condition classification using Hidden Markov Model based on fractal analysis of machined surface textures. Mach. Vis. Appl. 17: 327–336

    Article  Google Scholar 

  14. Bhat N N, Dutta S, Vashisth T, Pal S, Pal S K and Sen R 2016 Tool condition monitoring by SVM classification of machined surface images in turning. Int. J. Adv. Manuf. Technol. 83: 1487–1502

    Article  Google Scholar 

  15. Bhat N N, Dutta S, Pal S K and Pal S 2016 Tool condition classification in turning process using hidden Markov model based on texture analysis of machined surface images. Measurement. 90: 500–509

    Article  Google Scholar 

  16. Kassim A A, Mannan M A and Mian Z 2007 Texture analysis methods for tool condition monitoring. Image Vis. Comput. 25: 1080–1090

    Article  Google Scholar 

  17. Dutta S, Pal S K and Sen R 2016 On-machine tool prediction of flank wear from machined surface images using texture analyses and support vector regression. Precis. Eng. 43: 34–42

    Article  Google Scholar 

  18. Dutta S, Pal S K and Sen R, 2016, Tool condition monitoring in turning by applying machine vision. J. Manuf. Sci. Eng. 138

  19. Dutta S, Pal S K and Sen R 2018 Progressive tool condition monitoring of end milling from machined surface images. Proc. Inst Mech. Eng. Part B J. Eng. Manuf. 232: 251–266

    Article  Google Scholar 

  20. LeCun Y, Bottou L, Bengio Y and Haffner P 1998 Gradient-based learning applied to document recognition. Proc. IEEE. 86: 2278–2324

    Article  Google Scholar 

  21. Farabet C, Martini B, Akselrod P, Talay S, LeCun Y and Culurciello E, 2010 Hardware accelerated convolutional neural networks for synthetic vision systems, In: Proc. 2010 IEEE Int. Symp. Circuits Syst., IEEE, pp. 257–260

  22. LeCun Y, Bengio Y and Hinton G 2015 Deep learning. Nature. 521: 436–444

    Article  Google Scholar 

  23. Guo Y, Liu Y, Oerlemans A, Lao S, Wu S and Lew M S 2016 Deep learning for visual understanding: A review. Neurocomputing. 187: 27–48

    Article  Google Scholar 

  24. Wang J, Ma Y, Zhang L, Gao R X and Wu D 2018 Deep learning for smart manufacturing: Methods and applications. J. Manuf. Syst. 48: 144–156

    Article  Google Scholar 

  25. Iso S 1993 Tool Life Testing with Single Point Turning Tools. ISO. 3685: 1993

    Google Scholar 

  26. Chang S I and Ravathur J S 2005 Computer vision based non-contact surface roughness assessment using wavelet transform and response surface methodology. Qual. Eng. 17: 435–451

    Article  Google Scholar 

  27. Grzesik W and Brol S 2009 Wavelet and fractal approach to surface roughness characterization after finish turning of different workpiece materials. J. Mater. Process. Technol. 209: 2522–2531

    Article  Google Scholar 

  28. Josso B, Burton D R and Lalor M J 2001 Wavelet strategy for surface roughness analysis and characterisation. Comput. Methods Appl. Mech. Eng. 191: 829–842

    Article  Google Scholar 

  29. Josso B, Burton D R and Lalor M J 2002 Frequency normalised wavelet transform for surface roughness analysis and characterisation. Wear. 252: 491–500

    Article  Google Scholar 

  30. Morala-Argüello P, Barreiro J and Alegre E 2012 A evaluation of surface roughness classes by computer vision using wavelet transform in the frequency domain. Int. J. Adv. Manuf. Technol. 59: 213–220

    Article  Google Scholar 

  31. Zhao R, Yan R, Chen Z, Mao K, Wang P and Gao R X 2019 Deep learning and its applications to machine health monitoring. Mech. Syst. Signal Process. 115: 213–237

    Article  Google Scholar 

  32. Goodfellow I, Bengio Y, Courville A, Goodfellow I, Bengio Y and Courville A, 2016, Deep learning, MIT press

  33. Shang W, Sohn K, Almeida D and Lee H, Understanding and improving convolutional neural networks via concatenated rectified linear units, In: Int. Conf. Mach. Learn., pp. 2217–2225

  34. Hinton G E and Salakhutdinov R R 2006 Reducing the dimensionality of data with neural networks. Science (80-) 313: 504–507

    Article  MathSciNet  Google Scholar 

  35. Srivastava N, Hinton G, Krizhevsky A, Sutskever I and Salakhutdinov R 2014 Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15: 1929–1958

    MathSciNet  MATH  Google Scholar 

  36. van der Maaten L and Hinton G 2008 Visualizing data using t-SNE. J. Mach. Learn. Res. 9: 2579–2605

    MATH  Google Scholar 

Download references

Acknowledgement

Authors are thankful to the Director, CSIR-CMERI, Durgapur and CAMM, CSIR-CMERI, Durgapur for motivation and support.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Samik Dutta.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, M.P., Dutta, S. & Murmu, N.C. Tool wear classification based on machined surface images using convolution neural networks. Sādhanā 46, 130 (2021). https://doi.org/10.1007/s12046-021-01654-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12046-021-01654-9

Keywords

Navigation