nach oben

International Journal on Interactive Design and Manufacturing (IJIDeM)

Erschienen in:

29.11.2022 | Original Paper

Real-time cutting tool condition assessment and stochastic tool life predictive models for tool reliability estimation by in-process cutting tool vibration monitoring

verfasst von: Mulpur Sarat Babu, Thella Babu Rao

Erschienen in: International Journal on Interactive Design and Manufacturing (IJIDeM) | Ausgabe 3/2023

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Real-time tool wear prediction and its remaining useful life (RUL) estimation is an important part of the development of a smart machining system while it is practically complex. A two-step framework proposed based on the statistical correlation of the experimentally measured cutting tool vibration data with the flank wear progression and estimation of the cutting tool RUL by the construction of stochastic tool life probability predictive models. The machining experiments are conducted on the IN718 superalloy with uncoated WC tools under the varied conditions of cutting speed and feed to acquire the data of flank wear and associated tool vibration data. The results of confirmation experiments show the statistical correlation constructed is practically viable for in-process flank wear prediction at any time of instance during machining with any set cutting conditions using the real-time tool vibration monitoring. The in-process observation of 1.5 g tool acceleration during machining with 60 m/min cutting speed and 0.1 mm/tooth feed signifies 15% of the cutting tool failure probability, and its remaining useful life is 12.91 min. For 50% of tool reliability machining with 0.1 mm/tooth feed and 60, 90 and 120 m/min cutting speed, tool accelerations of 2.01, 3.08 and 3.98 g reflect that the respective exhausted tool lives are 12, 8 and 6 min and the respective remaining useful lives are 8, 6 and 5 min. Hence, based on the presented analysis and results, it is envisaged the proposed framework is reliable and robust for in-process cutting tool condition prediction based on the real-time tool vibration monitoring for its adoption to develop a smart machining system with autonomous decision-making capability.

Vorheriger Artikel Finite element simulation of the tensile behavior of Ti–6Al–7Nb titanium alloy at elevated temperatures

Nächster Artikel Comparative study of free vibration characteristics of flexible kinked cantilever robotic arm

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Chelladurai, H., Jain, V.K., Vyas, N.S.: Development of a cutting tool condition monitoring system for high speed turning operation by vibration and strain analysis. Int. J. Adv. Manuf. Technol. 37(5–6), 471–485 (2008). https://doi.org/10.1007/s00170-007-0986-zCrossRef

Lee, J., Choi, H.J., Nam, J., Jo, S.B., Kim, M., Lee, S.W.: Development and analysis of an online tool condition monitoring and diagnosis system for a milling process and its real-time implementation. J. Mech. Sci. Technol. 31(12), 5695–5703 (2017). https://doi.org/10.1007/s12206-017-1110-4CrossRef

Tahir, N.H.M., Rizal, M., Mansor, W.N.W., Ghani, J.A., Nuawi, M.Z., Haron, C.H.C.: The correlation between tool wear and vibration signals from piezoelectric in end milling of AISI P20+NI. J. Mech. Eng. 3(2), 49–62 (2017)

Li, H.Z., Zeng, H., Chen, X.Q.: An experimental study of tool wear and cutting force variation in the end milling of Inconel 718 with coated carbide inserts. J. Mater. Process. Technol. 180(1–3), 296–304 (2006). https://doi.org/10.1016/j.jmatprotec.2006.07.009CrossRef

Dimla, D.E.: The correlation of vibration signal features to cutting tool wear in a metal turning operation. Int. J. Adv. Manuf. Technol. 19(10), 705–713 (2002). https://doi.org/10.1007/s001700200080CrossRef

Carolan, T.A., et al.: Acoustic emission monitoring of tool wear during the face milling of steels and aluminium alloys using a fibre optic sensor. Part 1: Energy analysis. Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 211(4), 299–309 (1997). https://doi.org/10.1243/0954405971516275CrossRef

Uzun, M., Serhat, S.: Tool wear, surface roughness, cutting temperature and chips morphology evaluation of Al/TiN coated carbide cutting tools in milling of Cu–B–CrC based ceramic matrix composites C. J Mater Res Technol 16, 1243–1259 (2021). https://doi.org/10.1016/j.jmrt.2021.12.063CrossRef

Chuangwen, X., Jianming, D., Yuzhen, C., Huaiyuan, L., Zhicheng, S., Jing, X.: The relationships between cutting parameters, tool wear, cutting force and vibration. Adv. Mech. Eng. 10(1), 1–14 (2018). https://doi.org/10.1177/1687814017750434CrossRef

Babu, M.S., Rao, T.B.: Multi-sensor heterogeneous data-based online tool health monitoring in milling of IN718 superalloy using OGM (1, N) model and SVM. Measurement 199, 111501 (2022). https://doi.org/10.1016/j.measurement.2022.111501CrossRef

10.

Kuntoğlu, M., Sağlam, H.: Investigation of signal behaviors for sensor fusion with tool condition monitoring system in turning. Meas. J. Int. Meas. Confed. 173, 108582 (2021). https://doi.org/10.1016/j.measurement.2020.108582CrossRef

11.

Alonso, F.J., Salgado, D.R.: Analysis of the structure of vibration signals for tool wear detection. Mech. Syst. Signal Process. 22(3), 735–748 (2008). https://doi.org/10.1016/j.ymssp.2007.09.012CrossRef

12.

Bhuiyan, M.S.H., Choudhury, I.A.: Investigation of tool wear and surface finish by analyzing vibration signals in turning ASSAB-705 steel. Mach. Sci. Technol. 19(2), 236–261 (2015). https://doi.org/10.1080/10910344.2015.1018531CrossRef

13.

Khalili, K., Danesh, M.: Investigation of overhang effect on cutting tool vibration for tool condition monitoring. Virbroengineering 21, 162–170 (2013). https://doi.org/10.1094/MPMI-21-2-0162CrossRef

14.

Chen, X.Q., Li, H.Z.: Development of a tool wear observer model for online tool condition monitoring and control in machining nickel-based alloys. Int. J. Adv. Manuf. Technol. 45(7–8), 786–800 (2009). https://doi.org/10.1007/s00170-009-2003-1CrossRef

15.

Fang, N., Pai, P.S., Mosquea, S.: 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(1–4), 65–77 (2011). https://doi.org/10.1007/s00170-010-2703-6CrossRef

16.

Arslan, H., Er, A.O., Orhan, S., Aslan, E.: Tool condition monitoring in turning using statistical parameters of vibration signal. Int. J. Acoust. Vib. 21(4), 371–378 (2016). https://doi.org/10.20855/ijav.2016.21.4432CrossRef

17.

Wang, G.F., Yang, Y.W., Zhang, Y.C., Xie, Q.L.: Vibration sensor based tool condition monitoring using ν support vector machine and locality preserving projection. Sens. Actuat. A Phys. 209, 24–32 (2014). https://doi.org/10.1016/j.sna.2014.01.004CrossRef

18.

Ratava, J., Lohtander, M., Varis, J.: Tool condition monitoring in interrupted cutting with acceleration sensors. Robot. Comput. Integr. Manuf. 47, 70–75 (2017). https://doi.org/10.1016/j.rcim.2016.11.008CrossRef

19.

Toubhans, B., Fromentin, G., Viprey, F., Karaouni, H., Dorlin, T.: Machinability of inconel 718 during turning: Cutting force model considering tool wear, influence on surface integrity. J. Mater. Process. Technol. 285, 116809 (2020). https://doi.org/10.1016/j.jmatprotec.2020.116809CrossRef

20.

Salonitis, K., Kolios, A.: Force-based reliability estimation of remaining cutting tool life in titanium milling. Int. J. Adv. Manuf. Technol. 106(7–8), 3321–3333 (2020). https://doi.org/10.1007/s00170-019-04883-9CrossRef

21.

Liu, E., An, W., Xu, Z., Zhang, H.: Experimental study of cutting-parameter and tool life reliability optimization in inconel 625 machining based on wear map approach. J. Manuf. Process. 53(January), 34–42 (2020). https://doi.org/10.1016/j.jmapro.2020.02.006CrossRef

22.

Salonitis, K., Kolios, A.: Reliability assessment of cutting tool life based on surrogate approximation methods. Int. J. Adv. Manuf. Technol. 71(5–8), 1197–1208 (2014). https://doi.org/10.1007/s00170-013-5560-2CrossRef

23.

Rao, T.B.: Reliability analysis of the cutting tool in plasma-assisted turning and prediction of machining characteristics. Aust. J. Mech. Eng. (2020). https://doi.org/10.1080/14484846.2020.1769458CrossRef

24.

Klim, Z., Ennajimi, E., Balazinski, M., Fortin, C.: Cutting tool reliability analysis for variable feed milling of 17–4PH stainless steel. Wear 195(1–2), 206–213 (1996). https://doi.org/10.1016/0043-1648(95)06863-5CrossRef

25.

Xu, H., Zhang, C., Hong, G.S., Zhou, J., Hong, J., Woon, K.S.: Gated recurrent units based neural network for tool condition monitoring. Proc. Int. Jt. Conf. Neural Networks 2018, 1–7 (2018). https://doi.org/10.1109/IJCNN.2018.8489354CrossRef

26.

Hitomi, K., Nakamura, N., Inoue, S.: Reliability analysis of cutting tools. J. Manuf. Sci. Eng. Trans. ASME 101(2), 185–190 (1979). https://doi.org/10.1115/1.3439493CrossRef

27.

Erdi, M., Munish, K., Gupta, K., Li, Z., Krolczyk, G.M., Kuntoğlu, M.: Indirect Monitoring of Machining Characteristics via Advanced Sensor Systems: A Critical Review. Springer, London (2022)

28.

Kunto, M.: Modeling of cutting parameters and tool geometry for multi-criteria optimization of surface roughness and vibration via response surface methodology in (2020)

29.

Shah, M., Vakharia, V., Chaudhari, R., Vora, J., Yu, D., Khaled, P.: Tool wear prediction in face milling of stainless steel using singular generative adversarial network and LSTM deep learning models. Int. J. Adv. Manuf. Technol. (2022). https://doi.org/10.1007/s00170-022-09356-0CrossRef

30.

Bazi, R., Benkedjouh, T., Habbouche, H., Rechak, S., Zerhouni, N.: A hybrid CNN-BiLSTM approach-based variational mode decomposition for tool wear monitoring. Int. J. Adv. Manuf. Technol. 119(5–6), 3803–3817 (2022). https://doi.org/10.1007/s00170-021-08448-7CrossRef

31.

Duan, J., Hu, C., Zhan, X., Zhou, H., Liao, G., Shi, T.: Robotics and computer-integrated manufacturing MS-SSPCANet: a powerful deep learning framework for tool wear. Robot. Comput. Integr. Manuf. 78(52005168), 102391 (2022). https://doi.org/10.1016/j.rcim.2022.102391CrossRef

32.

Liu, X., Zhang, B., Li, X., Liu, S., Yue, C., Liang, S.Y.: An approach for tool wear prediction using customized DenseNet and GRU integrated model based on multi-sensor feature fusion. J. Intell. Manuf. (2022). https://doi.org/10.1007/s10845-022-01954-9CrossRef

33.

Chan, Y.W., Kang, T.C., Yang, C.T., Chang, C.H., Huang, S.M., Te Tsai, Y.: Tool wear prediction using convolutional bidirectional LSTM networks. J. Supercomput. 78(1), 810–832 (2022). https://doi.org/10.1007/s11227-021-03903-4CrossRef

34.

He, Z., Shi, T., Xuan, J.: Milling tool wear prediction using multi-sensor feature fusion based on stacked sparse autoencoders. Meas. J. Int. Meas. Confed. 190, 110719 (2022). https://doi.org/10.1016/j.measurement.2022.110719CrossRef

35.

Kene, A.P., Choudhury, S.K.: Analytical modeling of tool health monitoring system using multiple sensor data fusion approach in hard machining. Meas. J. Int. Meas. Confed. 145, 118–129 (2019). https://doi.org/10.1016/j.measurement.2019.05.062CrossRef

36.

Letot, C., Serra, R., Dossevi, M., Dehombreux, P.: Cutting tools reliability and residual life prediction from degradation indicators in turning process: a case study involving four approaches. Int. J. Adv. Manuf. Technol. 86(1–4), 495–506 (2016). https://doi.org/10.1007/s00170-015-8158-zCrossRef

37.

Sun, H., Zhang, X., Niu, W.: In-process cutting tool remaining useful life evaluation based on operational reliability assessment. Int. J. Adv. Manuf. Technol. 86(1–4), 841–851 (2016). https://doi.org/10.1007/s00170-015-8230-8CrossRef

38.

El-Hossainy, T.M.: Tool wear monitoring under dry and wet machining. Mater. Manuf. Process. 16(2), 165–176 (2001). https://doi.org/10.1081/AMP-100104298CrossRef

39.

Yan, R., Gao, R.X.: Tribology international multi-scale enveloping spectrogram for vibration analysis in bearing defect diagnosis. Tribol Int 42, 293–302 (2009). https://doi.org/10.1016/j.triboint.2008.06.013CrossRef

40.

Bi, F., Ma, T., Wang, X.: Development of a novel knock characteristic detection method for gasoline engines based on wavelet-denoising and EMD decomposition. Mech. Syst. Signal Process. 117, 517–536 (2019). https://doi.org/10.1016/j.ymssp.2018.08.008CrossRef

41.

Anijarv, T. E., Shabbir, N., Kutt, L., Iqbal, M. N.: Requirements to data acquisition and signal analysis for electrical grid condition monitoring. In: 2020 IEEE 61st Annual International Scientific Conference on Power and Electrical Engineering of Riga Technical University. RTUCON 2020—Proceedings (2020). https://doi.org/10.1109/RTUCON51174.2020.9316487

42.

Mohanraj, T., Yerchuru, J., Krishnan, H., Nithin-Aravind, R.S., Yameni, R.: Development of tool condition monitoring system in end milling process using wavelet features and Hoelder’s exponent with machine learning algorithms. Meas. J. Int. Meas. Confed. 173, 108671 (2021). https://doi.org/10.1016/j.measurement.2020.108671CrossRef

43.

Khorrami, H., Moavenian, M.: Expert systems with applications a comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification. Expert Syst. Appl. 37(8), 5751–5757 (2010). https://doi.org/10.1016/j.eswa.2010.02.033CrossRef

44.

Systems, M.: Singularityanalysis using continuous wavelet transform for bearing fault diagnosis. Mech Syst Signal Process 16, 1025–1041 (2002). https://doi.org/10.1006/mssp.2002.1474CrossRef

45.

Lacalle, D., De Piss, G.M., Ealo, J.A., Sastoque, L., Fernandes, M.H.: Tool wear monitoring of high-speed broaching process with carbide tools to reduce production errors. Mech Syst Signal Process 172, 109003 (2022). https://doi.org/10.1016/j.ymssp.2022.109003CrossRef

46.

Springer: Springer Complexity program (2020)

47.

Grzesik, W., Niesłony, P., Habrat, W., Sieniawski, J., Laskowski, P.: Investigation of tool wear in the turning of Inconel 718 superalloy in terms of process performance and productivity enhancement. Tribol. Int. 118, 337–346 (2018). https://doi.org/10.1016/j.triboint.2017.10.005CrossRef

48.

Musfirah, A.H., Ghani, J.A., Haron, C.H.C.: Tool wear and surface integrity of inconel 718 in dry and cryogenic coolant at high cutting speed. Wear 376–377, 125–133 (2017). https://doi.org/10.1016/j.wear.2017.01.031CrossRef

49.

Zhang, X., Yu, T., Xu, P., Zhao, J.: In-process stochastic tool wear identification and its application to the improved cutting force modeling of micro milling. Mech. Syst. Signal Process. 164, 108233 (2022). https://doi.org/10.1016/j.ymssp.2021.108233CrossRef

50.

Karandikar, J.M., Abbas, A.E., Schmitz, T.L.: Tool life prediction using Bayesian updating. Part 1: milling tool life model using a discrete grid method. Precis. Eng. 38(1), 18–27 (2014). https://doi.org/10.1016/j.precisioneng.2013.06.006CrossRef

51.

Ghosh, S., Kumar Naskar, S., Kumar Mandal, N.: Estimation of residual life of a cutting tool used in a machining process. MATEC Web Conf. 192, 4–7 (2018). https://doi.org/10.1051/matecconf/201819201017CrossRef

52.

Ranfagni, A., Ruggeri, R., Agresti, A.: Tunneling as a stochastic process. Found. Phys. 28(3), 515–525 (1998). https://doi.org/10.1023/A:1018776330627CrossRef

53.

Watson, J.D.: Tool-life distributions part 1: single-injury tool-life model. J. Manuf. Sci. Eng. Trans. ASME 99(3), 519–522 (1977). https://doi.org/10.1115/1.3439271CrossRef

54.

Karandikar, J.M., Abbas, A., Schmitz, T.L.: Remaining useful tool life predictions in turning using Bayesian inference. Int. J. Progn. Heal. Manag. 4(2), 11 (2013)

55.

Hitomi, K., Nakamura, N., Inoue, S.: Reliability Analysis of Cutting Tools. ASME Pap 101, 185–190 (1978)

56.

Lai, C.: Springer Handbook of Engineering Statistics (2006). https://doi.org/10.1007/978-1-84628-288-1

Titel: Real-time cutting tool condition assessment and stochastic tool life predictive models for tool reliability estimation by in-process cutting tool vibration monitoring
verfasst von: Mulpur Sarat Babu
Thella Babu Rao
Publikationsdatum: 29.11.2022
Verlag: Springer Paris
Erschienen in: International Journal on Interactive Design and Manufacturing (IJIDeM) / Ausgabe 3/2023
Print ISSN: 1955-2513
Elektronische ISSN: 1955-2505
DOI: https://doi.org/10.1007/s12008-022-01109-3

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 3/2023

Understanding hot workability of power plant P92 creep resistant steels using dynamic material modelling (DMM) and microstructural evolution

Optimizing reciprocal wear responses of centrifugally cast A333 hybrid functionally graded composite using Taguchi and response surface methodology

Finite element simulation of the tensile behavior of Ti–6Al–7Nb titanium alloy at elevated temperatures

Structural and modal analysis of crankcase of single cylinder diesel engine

Intelligent dimensional prediction systems with real-time monitoring sensors for injection molding via statistical regression and artificial neural networks

Optimization of friction stir welding processes using hybrid-taguchi methods: a comparative analysis

Premium Partner