Skip to main content
SpringerLink
Log in

Tool wear classification based on maximal overlap discrete wavelet transform and hybrid deep learning model

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

Abstract

A precise tool wear monitoring model is essential for manufacturing to ensure reliability and efficiency. This study aims to analyze and monitor the condition of small-sized cutting tools during end-milling operations based on direct and indirect approaches in machining AISI H13 alloy steel. The tool condition monitoring classification method was achieved by integrating the wavelet transform method for multiresolution analyses with a hybrid deep learning algorithm. A new approach combines maximal overlap discrete wavelet transform (MODWT) for signal preprocessing with a hybrid deep learning model that includes convolutional neural network (CNN) and bidirectional long-short term memory (BiLSTM) algorithms to improve tool wear identification accuracy and efficiency. In the present work, tool wear conditions are classified into five classes: normal tool, slight wear, moderate wear, high wear, and severe wear. The proposed model’s performance was evaluated by comparing its identification accuracy to other common machine and deep learning models. This evaluation was conducted through a case study that utilized a dataset obtained from a milling test. The proposed classification model is more accurate than other machine and deep learning models. During training, it achieves a classification accuracy of 98.96%, and the overall testing accuracy is 94.07%. The effectiveness and adaptability of the proposed method in tool condition monitoring applications are noteworthy.

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

Access this article

Log in via an institution

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
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
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  1. Wang M, Zhou J, Gao J, Li Z, Li E (2020) Milling tool wear prediction method based on deep learning under variable working conditions. XX:1–9. https://doi.org/10.1109/ACCESS.2020.3010378

  2. Nasir V, Sassani F (2021) A review on deep learning in machining and tool monitoring: methods, opportunities, and challenges. Int J Adv Manuf Technol 115(9):2683–2709. https://doi.org/10.1007/s00170-021-07325-7

  3. Zheng H, Lin J (2019) A deep learning approach for high speed machining tool wear monitoring. Proc 2019 3rd IEEE Int Conf Robot Autom Sci ICRAS 2019. https://doi.org/10.1109/ICRAS.2019.8809070

    Article  Google Scholar 

  4. Che JK, Ratnam MM (2018) Real-time monitoring of workpiece diameter during turning by vision method. Measurement 126:369–377. https://doi.org/10.1016/j.measurement.2018.05.089

    Article  Google Scholar 

  5. Addona DMD, Ullah AMMS (2017) Tool-wear prediction and pattern-recognition using artificial neural network and DNA-based computing. J Intell Manuf 28:1285–1301. https://doi.org/10.1007/s10845-015-1155-0

    Article  Google Scholar 

  6. Li N, Chen Y, Kong D, Tan S (2017) Force-based tool condition monitoring for turning process using v-support vector regression. Int J Adv Manuf Technol 91:351–361. https://doi.org/10.1007/s00170-016-9735-5

    Article  Google Scholar 

  7. Malekian M, Park SS, Jun MBG (2009) Tool wear monitoring of micro-milling operations. J Mater Process Technol 209:4903–4914. https://doi.org/10.1016/j.jmatprotec.2009.01.013

    Article  Google Scholar 

  8. Wang G, Guo Z, Yang Y (2013) Force sensor based online tool wear monitoring using distributed Gaussian ARTMAP network. Sens Actuators A Phys 192:111–118. https://doi.org/10.1016/j.sna.2012.12.029

    Article  Google Scholar 

  9. Caggiano A, Napolitano F, Teti R (2017) Dry turning of Ti6Al4V: tool wear curve reconstruction based on cognitive sensor monitoring. Procedia CIRP 62:209–214. https://doi.org/10.1016/j.procir.2017.03.046

    Article  Google Scholar 

  10. Liao X, Zhou G, Zhang Z, Lu J, Ma J (2019) Tool wear state recognition based on GWO–SVM with feature selection of genetic algorithm. Int J Adv Manuf Technol 104:1051–1063. https://doi.org/10.1007/s00170-019-03906-9

  11. Goodfellow I, Bengio Y, Ac. Deep learning (2016) The MIT Press, ISBN 0262035618

  12. Cao D, Sun H, Zhang J, Mo R (2020) In-process tool condition monitoring based on convolution neural network. Jisuanji Jicheng Zhizao Xitong/Computer Integr Manuf Syst CIMS 26. https://doi.org/10.13196/j.cims.2020.01.008

  13. Aghazadeh F, Tahan A, Thomas M (2018) Tool condition monitoring using spectral subtraction and convolutional neural networks in milling process. Int J Adv Manuf Technol 98:3217–3227. https://doi.org/10.1007/s00170-018-2420-0

  14. Ma J, Luo D, Liao X, Zhang Z, Huang Y, Lu J (2021) Tool wear mechanism and prediction in milling TC18 titanium alloy using deep learning. Measurement 173:108554. https://doi.org/10.1016/j.measurement.2020.108554

    Article  Google Scholar 

  15. Xia M, Li T, Shu T, Wan J, De Silva CW, Wang Z (2018) A two-stage approach for the remaining useful life prediction of bearings using deep neural networks. IEEE Trans Industr Inform 15(6):3703–3711. https://doi.org/10.1109/TII.2018.2868687

  16. Peng Y, Song Q, Wang R, Liu Z, Liu Z (2023) Intelligent recognition of tool wear in milling based on a single sensor signal. Int J Adv Manuf Technol 124:1077–1093. https://doi.org/10.1007/s00170-022-10404-y

    Article  Google Scholar 

  17. Peng R, Pang H, Jiang H, Hu Y (2020) Study of tool wear monitoring using machine vision. 54:259–270. https://doi.org/10.3103/S0146411620030062

  18. You Z, Gao H, Guo L, Liu Y, Li J (2020) On-line milling cutter wear monitoring in a wide field-of-view camera. Wear. https://doi.org/10.1016/j.wear.2020.203479

    Article  Google Scholar 

  19. Li W, Singh HM, Guo YB (2013) An online optical system for inspecting tool condition in milling of H13 tool steel and IN 718 alloy. 1067–1077. https://doi.org/10.1007/s00170-012-4548-7

  20. Fernández-robles L, Azzopardi G, Alegre E, Petkov N, Castejón-limas M (2017) Identification of milling inserts in situ based on a versatile machine vision system. J Manuf Syst 45:48–57. https://doi.org/10.1016/j.jmsy.2017.08.002

    Article  Google Scholar 

  21. Yu X, Lin X, Dai Y, Zhu K (2017) Image edge detection based tool condition monitoring with morphological component analysis. ISA Trans. https://doi.org/10.1016/j.isatra.2017.03.024

    Article  Google Scholar 

  22. Hussain S, Chen X (2008) Remote milling tool-wear monitoring and direct wear features extraction by image processing. Inter J of Internet Manuf and Services 1(3):246–261. https://doi.org/10.1504/IJIMS.2008.021197

  23. Zaretalab A, Haghighi HS, Mansour S, Sajadieh MS (2018) A mathematical model for the joint optimization of machining conditions and tool replacement policy with stochastic tool life in the milling process. Int J Adv Manuf Technol 96:2319–2339. https://doi.org/10.1007/s00170-018-1683-9

  24. Yang K, Wang G, Dong Y, Zhang Q, Sang L (2019) Early chatter identification based on an optimized variational mode decomposition. Mech Syst Signal Process 115:238–254. https://doi.org/10.1016/j.ymssp.2018.05.052

    Article  Google Scholar 

  25. Dai Y, Zhu K (2018) A machine vision system for micro-milling tool condition monitoring. Precis Eng. https://doi.org/10.1016/j.precisioneng.2017.12.006

    Article  Google Scholar 

  26. Wang G, Yang Y, Xie Q, Zhang Y (2014) Advances in engineering software force based tool wear monitoring system for milling process based on relevance vector machine. Adv Eng Softw 71:46–51. https://doi.org/10.1016/j.advengsoft.2014.02.002

    Article  Google Scholar 

  27. Hong Y, Yoon H, Moon J, Cho Y, Ahn S (2016) Tool-wear monitoring during micro-end milling using wavelet packet transform and Fisher ’ s linear discriminant. 17:845–855. https://doi.org/10.1007/s12541-016-0103-z

  28. Nadimi S, Oliaei B (2016) Influence of tool wear on machining forces and tool deflections during micro milling. 1963–1980. https://doi.org/10.1007/s00170-015-7744-4

  29. Geramifard O, Xu J, Zhou J (2014) Multimodal hidden Markov model-based approach for tool wear monitoring. IEEE Trans Ind Electron 61:2900–2911. https://doi.org/10.1109/TIE.2013.2274422

    Article  Google Scholar 

  30. Zhu K, Vogel-heuser B (2014) Sparse representation and its applications in micro-milling condition monitoring : noise separation and tool condition monitoring. 185–199. https://doi.org/10.1007/s00170-013-5258-5

  31. Wang G, Yang Y, Li Z (2014) Force sensor based tool condition monitoring using a heterogeneous ensemble learning model. Sensors (Switzerland) 14:21588–21602. https://doi.org/10.3390/s141121588

    Article  Google Scholar 

  32. Wang GF, Yang YW, Zhang YC, Xie QL (2014) Sensors and actuators a : physical vibration sensor based tool condition monitoring using vector machine and locality preserving projection. Sensors Actuators A Phys 209:24–32. https://doi.org/10.1016/j.sna.2014.01.004

    Article  Google Scholar 

  33. Aghazadeh F, Tahan A, Thomas M (2018) Tool condition monitoring using spectral subtraction algorithm and artificial intelligence methods in milling process. Int J Mech Eng Robot Res. https://doi.org/10.18178/ijmerr.7.1.30-34

  34. Dutta S, Pal SK, Sen R (2016) Progressive tool condition monitoring of end milling from machined surface images. https://doi.org/10.1177/0954405416640417

  35. Fernández-robles L, Sánchez-gonzález L, Díez-gonzález J, Castejón-limas M, Pérez H (2021) Neurocomputing use of image processing to monitor tool wear in micro milling. 452:333–340. https://doi.org/10.1016/j.neucom.2019.12.146

  36. Networks CBL (2017) Learning to monitor machine health with convolutional bi-directional LSTM networks. 1–18. https://doi.org/10.3390/s17020273

  37. An Q, Tao Z, Xu X, El Mansori M, Chen M (2020) A data-driven model for milling tool remaining useful life prediction with convolutional and stacked LSTM network. Meas J Int Meas Confed. https://doi.org/10.1016/j.measurement.2019.107461

    Article  Google Scholar 

  38. Patnaik B, Mishra M, Bansal RC, Jena RK (2021) MODWT-XGBoost based smart energy solution for fault detection and classification in a smart microgrid. Appl Energy 285:116457. https://doi.org/10.1016/j.apenergy.2021.116457

    Article  Google Scholar 

  39. Costa FB, Neto CMS, Carolino SF, Ribeiro RLA, Barreto RL, Rocha TOA, Pott P (2012) Comparison between two versions of the discrete wavelet transform for real-time transient detection on synchronous machine terminals. 2012 10th IEEE/IAS Int Conf Ind Appl INDUSCON 2012:1–5. https://doi.org/10.1109/INDUSCON.2012.6453533

  40. Liu D, Dysko A, Hong Q, Tzelepis D, Booth CD (2022) Transient wavelet energy-based protection scheme for inverter-dominated microgrid. IEEE Trans Smart Grid 13:2533–2546. https://doi.org/10.1109/TSG.2022.3163669

    Article  Google Scholar 

  41. Ashok V, Yadav A (2020) A real-time fault detection and classification algorithm for transmission line faults based on MODWT during power swing. Int Trans Electr Energy Syst 30:1–27. https://doi.org/10.1002/2050-7038.12164

    Article  Google Scholar 

  42. Chen HY, Lee CH (2021) Deep learning approach for vibration signals applications. Sensors 21. https://doi.org/10.3390/s21113929

  43. Hong CW, Lee K, Ko MS, Kim JK, Oh K, Hur K (2020) Multivariate time series forecasting for remaining useful life of turbofan engine using deep-stacked neural network and correlation analysis. Proc - 2020 IEEE Int Conf Big Data Smart Comput BigComp 2020:63–70. https://doi.org/10.1109/BigComp48618.2020.00-98

  44. Wu C, Jiang P, Ding C, Feng F, Chen T (2019) Intelligent fault diagnosis of rotating machinery based on one-dimensional convolutional neural network. Comput Ind 108:53–61. https://doi.org/10.1016/j.compind.2018.12.001

    Article  Google Scholar 

  45. Zhang P, Gao D, Hong D, Lu Y, Wang Z, Liao Z (2023) Intelligent tool wear monitoring based on multi-channel hybrid information and deep transfer learning. J Manuf Syst 69:31–47. https://doi.org/10.1016/j.jmsy.2023.06.004

    Article  Google Scholar 

  46. Qin B, Wang Y, Liu K, Jiang S, Luo Q (2023) A novel online tool condition monitoring method for milling titanium alloy with consideration of tool wear law. Mech Syst Signal Process 199:110467. https://doi.org/10.1016/j.ymssp.2023.110467

    Article  Google Scholar 

  47. Papageorgiou D, Medrea C, Kyriakou N (2013) Failure analysis of H13 working die used in plastic injection moulding. Eng Fail Anal 35:355–359. https://doi.org/10.1016/j.engfailanal.2013.02.028

    Article  Google Scholar 

  48. Sun XG, Sun L, Wang EH (2014) Study on joint surface parameter identification method of shaft-toolholder and toolholder-tool for vertical CNC milling machine. Mach Tool Hydraul 42:106–109

    Google Scholar 

  49. Wang B, Sun W, Wen B (2012) The finite element modeling of high-speed spindle system dynamics with spindle-holder-tool joints. Jixie Gongcheng Xuebao Chinese J Mech Eng 48:83–89

    Article  Google Scholar 

  50. Wang L, Gao RX (2006) Condition monitoring and control for intelligent manufacturing; Springer Science & Business Media, ISBN 1846282691

  51. Ong P, Lee WK, Lau RJH (2019) Tool condition monitoring in CNC end milling using wavelet neural network based on machine vision. Int J Adv Manuf Technol. https://doi.org/10.1007/s00170-019-04020-6

    Article  Google Scholar 

  52. Zhang L, Zhang X, Liu X, Guo Z (2020) Inspection and compensation of spindle thermal extension based on machine vision. In Proceedings of the 2020 IEEE International Conference on Mechatronics and Automation (ICMA); IEEE pp 576–581

  53. Mansi, Saini K, Vanraj, Dhami SS (2021) MODWT and VMD based intelligent gearbox early stage fault detection approach. J Fail Anal and Preven 21:1821–1837. https://doi.org/10.1007/s11668-021-01228-1

  54. Imani A, Moravej Z, Pazoki M (2023) A novel MODWT-based fault detection and classification scheme in VSC-HVDC transmission line. Electr Power Syst Res 221:109434. https://doi.org/10.1016/j.epsr.2023.109434

    Article  Google Scholar 

  55. Li Y, Peng T, Zhang C, Sun W, Hua L, Ji C (2022) Multi-step ahead wind speed forecasting approach coupling maximal overlap discrete wavelet transform, improved grey wolf optimization algorithm and long short-term memory. Renew Energy 196:1115–1126. https://doi.org/10.1016/j.renene.2022.07.016

    Article  Google Scholar 

  56. Ghimire S, Deo RC, Raj N, Mi J (2023) Wavelet-based 3-phase Hybrid SVR model trained with satellite-derived predictors, particle swarm optimization and maximum overlap discrete wavelet transform for solar radiation prediction. Renew Sustain Energy Rev 113:109247. https://doi.org/10.1016/j.rser.2019.109247

    Article  Google Scholar 

  57. Fang N, Pai PS, Mosquea S (2011) Effect of tool edge wear on the cutting forces and vibrations in high-speed finish machining of Inconel 718: an experimental study and wavelet transform analysis. Int J Adv Manuf Technol 52:65–77. https://doi.org/10.1007/s00170-010-2703-6

    Article  Google Scholar 

  58. 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 

  59. Aralikatti SS, Ravikumar KN, Kumar H, ShivanandaNayaka H (2020) Sugumaran, V. Comparative study on tool fault diagnosis methods using vibration signals and cutting force signals by machine learning technique. SDHM Struct Durab Heal Monit 14:127–145. https://doi.org/10.32604/SDHM.2020.07595

    Article  Google Scholar 

Download references

Acknowledgements

Thanks to Shanghai WPT Company for providing all the hardware to support this study.

Author information

Authors and Affiliations

  1. Mechanical Manufacture and Automation, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, People’s Republic of China

    Ahmed Abdeltawab, Zhang Xi & Zhang longjia

  2. Production Engineering and Mechanical Design Department, Faculty of Engineering, Mansoura University, El Mansoura, 35516, Egypt

    Ahmed Abdeltawab

Authors
  1. Ahmed Abdeltawab
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhang Xi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhang longjia
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Ahmed Abdeltawab: writing, original draft, writing, review editing, experimental work, formal analysis, data analysis, investigation, methodology, validation.

Zhang xi: project administration, idea, review editing, experimental resources support, methodology, validation, supervision.

Zhang Longjia: writing, vision system application, experimental work, methodology, validation.

Corresponding author

Correspondence to Zhang Xi.

Ethics declarations

Competing interests

The authors declare no competing interests.

Consent to participate

Not applicable.

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

Abdeltawab, A., Xi, Z. & longjia, Z. Tool wear classification based on maximal overlap discrete wavelet transform and hybrid deep learning model. Int J Adv Manuf Technol 130, 2381–2406 (2024). https://doi.org/10.1007/s00170-023-12797-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-023-12797-w

Keywords

Search

Navigation