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Tool wear prediction in milling based on a GSA-BP model with a multisensor fusion method

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Abstract

Tool wear damages the surface quality of the workpiece and increases equipment downtime. Tool wear prediction is of great importance for reducing processing costs and improving processing efficiency. This paper applies multisensor fusion technology to predict tool wear. The cutting force, vibration, and acoustic emission signals are collected simultaneously during the milling process. The time domain, frequency domain, and time–frequency domain characteristics of each signal are extracted, reduced, and filtered through correlation analysis. A GSA-BP prediction model is established by a BP neural network in which the weights and thresholds are optimized through the gravitational search algorithm (GSA). The test results show that the prediction results of the GSA-BP model are in good agreement with the actual wear, and the prediction accuracy is higher than that of the traditional BP neural network model.

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Data availability

The datasets used or analyzed during the electrical current study are available from the corresponding author on reasonable request.

Code availability

The codes used or analyzed during the electrical current study are available from the corresponding author on reasonable request.

Abbreviations

v c :

Cutting speed, m/min

f z :

Feed per tooth, mm/z

a e :

Axial depth, mm

a p :

Radial depth, mm

Fx :

Cutting force X direction

Fy :

Cutting force Y direction

Fz :

Cutting force Z direction

AE :

Acoustic emission

Ax :

Vibration X direction

Ay :

Vibration Y direction

Az :

Vibration Z direction

I p :

Peak factor

C f :

Pulse factor

C e :

Margin factor

S f :

Waveform factor

X max :

The maximum

X ptp :

Peak–peak

X rms :

Root mean square

Am :

Absolute mean value

Va :

Variance

Amp-fc :

Barycenter frequency

Amp-max :

Maximum amplitude

Amp-mean :

Average amplitude

Amp-median :

Median amplitude

Amp-pk :

Amplitude variance

Power-max :

Maximum power

Power-mean :

Average power

Power-median :

Median power

E_cfs3_0 :

Energy of the first node

E_cfs3_1 :

Energy of the second node

E_cfs3_2 :

Energy of the third node

E_cfs3_3 :

Energy of the fourth node

E_cfs3_4 :

Energy of the fifth node

E_cfs3_5 :

Energy of the sixth node

E_cfs3_6 :

Energy of the seventh node

E_cfs3_7 :

Energy of the eighth node

References

  1. Kurada S, Bradley C (1997) A review of machine vision sensors for tool condition monitoring. Comput Ind 34(1):55–72

    Article  Google Scholar 

  2. Castejón M, Alegre E, Barreiro J, Hernández LK (2007) On-line tool wear monitoring using geometric descriptors from digital images. Int J Mach Tool Manu 47(12-13):1847–1853

    Article  Google Scholar 

  3. Hocheng H, Tseng HC, Hsieh ML, Lin YH (2018) Tool wear monitoring in single-point diamond turning using laser scattering from machined workpiece. J Manuf Process 31(JAN):405-415

  4. Gao D, Liao Z, Lv Z, Lu Y (2015) Multi-scale statistical signal processing of cutting force in cutting tool condition monitoring. Int J Adv Manuf Technol 80(9-12):1843–1853

    Article  Google Scholar 

  5. Kong D, Chen Y, Li N (2017) Force-based tool wear estimation for milling process using Gaussian mixture hidden Markov models. Int J Adv Manuf Technol 92(5-8):2853–2865

    Article  Google Scholar 

  6. Wang C, Bao Z, Zhang P, Ming W, Chen M (2019) Tool wear evaluation under minimum quantity lubrication by clustering energy of acoustic emission burst signals. Measurement 138:256–265

    Article  Google Scholar 

  7. Hesser DF, Markert B (2019) Tool wear monitoring of a retrofitted CNC milling machine using artificial neural networks. Manuf Lett 19:1–4

    Article  Google Scholar 

  8. Simon GD, Deivanathan R (2019) Early detection of drilling tool wear by vibration data acquisition and classification. Manuf Lett 21:60–65

    Article  Google Scholar 

  9. Kiswanto G (2020) Digital twin approach for tool wear monitoring of micro-milling. Procedia CIRP 93:1532–1537

    Article  Google Scholar 

  10. Zeng W, Jiang X, Blunt L (2009) Surface characterisation-based tool wear monitoring in peripheral milling. Int J Adv Manuf Technol 40(3-4):226–233

    Article  Google Scholar 

  11. Groche P, Wu Y (2019) Inline observation of tool wear in deep drawing with thermoelectric and optical measurements. CIRP Ann-Manuf Techn 68(1):567–570

    Article  Google Scholar 

  12. Rizal M, Ghani JA, Nuawi MZ, Haron CHC (2018) An embedded multi-sensor system on the rotating dynamometer for real-time condition monitoring in milling. Int J Adv Manuf Technol 95(1-4):811–823

    Article  Google Scholar 

  13. Liu C, Li Y, Zhou G, Shen W (2018) A sensor fusion and support vector machine based approach for recognition of complex machining conditions. J Intell Manuf J 29(8):1739–1752

    Article  Google Scholar 

  14. Lee WJ, Mendis GP, Sutherland JW (2019) Development of an intelligent tool condition monitoring system to identify manufacturing tradeoffs and optimal machining conditions. Procedia Manuf 33:256–263

    Article  Google Scholar 

  15. Zhang X, Lu X, Wang S, Wang W, Li W (2018) A multi-sensor based online tool condition monitoring system for milling process. Procedia CIRP 72:1136–1141

    Article  Google Scholar 

  16. Kene AP, Choudhury SK (2019) Analytical modeling of tool health monitoring system using multiple sensor data fusion approach in hard machining. Measurement 145:118–129

    Article  Google Scholar 

  17. Zhang KF, Yuan HQ, Nie P (2015) A method for tool condition monitoring based on sensor fusion. J Intell Manuf 26(5):1011–1026

    Article  Google Scholar 

  18. Zhang JZ, Chen JC (2008) Tool condition monitoring in an end-milling operation based on the vibration signal collected through a microcontroller-based data acquisition system. Int J Adv Manuf Technol 39(1-2):118–128

    Article  Google Scholar 

  19. Chen X, Li B (2007) Acoustic emission method for tool condition monitoring based on wavelet analysis. Int J Adv Manuf Technol 33(9-10):968–976

    Article  Google Scholar 

  20. Oraby S, Al-Modhuf A, Hayhurst D (2005) A diagnostic approach for turning tool based on the dynamic force signals. J Manuf Sci E-T Asme 127:463–475

    Article  Google Scholar 

  21. Niaki FA, Feng L, Ulutan D, Mears L (2016) A wavelet-based data-driven modelling for tool wear assessment of difficult to machine materials. Int J Mechatron Manuf Syst 9(2):97–121

    Google Scholar 

  22. Niu B, Sun J, Yang B (2020) Multisensory based tool wear monitoring for practical applications in milling of titanium alloy. Mater Today: Proceedings 22:1209–1217

    Google Scholar 

  23. Kaya B, Oysu C, Ertunc HM (2011) Force-torque based on-line tool wear estimation system for CNC milling of Inconel 718 using neural networks. Adv Eng Softw 42(3):76–84

    Article  Google Scholar 

  24. Ghosh N, Ravi YB, Patra A, Mukhopadhyay S, Paul S, Mohanty AR, Chattopadhyay AB (2007) Estimation of tool wear during CNC milling using neural network-based sensor fusion. Mech Syst Signal Pr 21(1):466–479

    Article  Google Scholar 

  25. Chen JC (2000) An effective fuzzy-nets training scheme for monitoring tool breakage. J Intell Manuf 11(1):85–101

    Article  Google Scholar 

  26. Yang Y, Guo Y, Huang Z, Chen N, Li L, Jiang Y, He N (2019) Research on the milling tool wear and life prediction by establishing an integrated predictive model. Measurement 145:178–189

    Article  Google Scholar 

  27. Gomes MC, Brito LC, da Silva MB, Duarte MAV (2021) Tool wear monitoring in micromilling using Support Vector Machine with vibration and sound sensors. Precis Eng 67:137–151

    Article  Google Scholar 

  28. Li W, Liu T (2019) Time varying and condition adaptive hidden Markov model for tool wear state estimation and remaining useful life prediction in micro-milling. Mech Syst Signal Pr 131:689–702

    Article  Google Scholar 

  29. Yu J, Liang S, Tang D, Liu H (2017) A weighted hidden Markov model approach for continuous-state tool wear monitoring and tool life prediction. Int J Adv Manuf Technol 91(1-4):201–211

    Article  Google Scholar 

  30. Thanh PN, Cho M-Y, Da TN (2021) Insulator leakage electrical current prediction using surface spark discharge data and particle swarm optimization based neural network. Electr Power Syst Res 191:106888

    Article  Google Scholar 

  31. Tang J, Li WX, Zhao B (2018) The application of GA-BP algorithm in prediction of tool wear state. MS&E 398(1):012025

    Google Scholar 

  32. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248

    Article  Google Scholar 

  33. Chaudhary D, Kumar B (2018) Cloudy GSA for load scheduling in cloud computing. Appl Soft Comput 71:861–871

    Article  Google Scholar 

  34. Naserbegi A, Aghaie M, Minuchehr A, Alahyarizadeh GH (2018) A novel exergy optimization of Bushehr nuclear power plant by gravitational search algorithm (GSA). Energy 148:373–385

    Article  Google Scholar 

  35. Momber AW, Buchbach S, Plagemann P, Marquardt T (2017) Edge coverage of organic coatings and corrosion protection over edges under simulated ballast water tank conditions. Prog Org Coat 108:90–92

    Article  Google Scholar 

Download references

Funding

This work is supported by the National Natural Science Foundation of China (Grant No. 52075276), the project for the Innovation Team of Universities and Institutes in Jinan (Grant No. 2018GXRC005), the Key Research and Development Plan of Shandong Province (Grant No. 2019GGX104084, 2019GGX104052), the National Natural Science Foundation of China (Grant No. 51905286), and the Science and Technology Program of Shandong University (Grant No. J18KA032).

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Authors and Affiliations

  1. School of Mechanical and Automotive Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, China

    Xiangfei Meng, Jingjie Zhang, Guangchun Xiao, Zhaoqiang Chen, Mingdong Yi & Chonghai Xu

  2. Key Laboratory of Equipments Manufacturing and Intelligent Measurement and Control, China National Light Industry, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 2-50353, China

    Xiangfei Meng, Jingjie Zhang, Guangchun Xiao, Zhaoqiang Chen, Mingdong Yi & Chonghai Xu

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  1. Xiangfei Meng
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  2. Jingjie Zhang
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  3. Guangchun Xiao
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  4. Zhaoqiang Chen
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  5. Mingdong Yi
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  6. Chonghai Xu
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Contributions

XiangFei Meng and Chonghai Xu conceived and designed the study. XiangFei Meng performed the experiments. XiangFei Meng and Jingjie Zhang analyzed data and wrote the paper. Chonghai Xu, Jingjie Zhang, Guangchun Xiao, Zhaoqiang Chen, and Mingdong Yi reviewed and modified the manuscript. All authors read and approved the manuscript.

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Correspondence to Chonghai Xu.

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Meng, X., Zhang, J., Xiao, G. et al. Tool wear prediction in milling based on a GSA-BP model with a multisensor fusion method. Int J Adv Manuf Technol 114, 3793–3802 (2021). https://doi.org/10.1007/s00170-021-07152-w

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