Top

The International Journal of Advanced Manufacturing Technology

Published in:

16-12-2023 | ORIGINAL ARTICLE

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

Authors: Ahmed Abdeltawab, Zhang Xi, Zhang longjia

Published in: The International Journal of Advanced Manufacturing Technology | Issue 5-6/2024

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

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.

previous article Improvement of quality and yield for investment casting of centrifugal pump impeller by tilting mold and optimizing runner/riser system

next article Identification of the mechanical properties and surface morphology of the friction still welding on aluminum alloy specimens

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

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

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

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.2019.8809070CrossRef

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.2018.05.089CrossRef

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/s10845-015-1155-0CrossRef

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/s00170-016-9735-5CrossRef

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.2009.01.013CrossRef

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.2012.12.029CrossRef

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.2017.03.046CrossRef

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.2020.108554CrossRef

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-yCrossRef

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.2020.203479CrossRef

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.2017.08.002CrossRef

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.2017.03.024CrossRef

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.2018.05.052CrossRef

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.2017.12.006CrossRef

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.2014.02.002CrossRef

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.2013.2274422CrossRef

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/s141121588CrossRef

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.2014.01.004CrossRef

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.2019.107461CrossRef

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.2021.116457CrossRef

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.2022.3163669CrossRef

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/10.1002/2050-7038.12164CrossRef

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.2018.12.001CrossRef

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.2023.06.004CrossRef

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.2023.110467CrossRef

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.2013.02.028CrossRef

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

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 48:83–89CrossRef

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/s00170-019-04020-6CrossRef

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.2023.109434CrossRef

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.2022.07.016CrossRef

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.2019.109247CrossRef

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/s00170-010-2703-6CrossRef

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.2017.05.006CrossRef

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.2020.07595CrossRef

Title: Tool wear classification based on maximal overlap discrete wavelet transform and hybrid deep learning model
Authors: Ahmed Abdeltawab
Zhang Xi
Zhang longjia
Publication date: 16-12-2023
Publisher: Springer London
Published in: The International Journal of Advanced Manufacturing Technology / Issue 5-6/2024
Print ISSN: 0268-3768
Electronic ISSN: 1433-3015
DOI: https://doi.org/10.1007/s00170-023-12797-w

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Other articles of this Issue 5-6/2024

Experimental and numerical investigation of process force evolution in Ultra-High Rotational Speed Micro FSW

A mid-high spatial frequency error suppression method based on the pseudo-random path with space MABF mapping for complex surface

A proposal of models for thermal compensation in machine tools based on a formulation for in-series heat transfer

Effect of laser shock peening on fretting corrosion behaviors of aluminum alloy in different aqueous environments

Analytical prediction of shot peening residual stress distribution using inherent strain in aluminum–magnesium alloy plates under various peening conditions

Machine learning-aided cooling profile prediction in plastic injection molding

Premium Partners