Skip to main content
Erschienen in: Journal of Intelligent Manufacturing 6/2021

18.07.2020

Predicting tool wear size across multi-cutting conditions using advanced machine learning techniques

verfasst von: Yan Shen, Feng Yang, Mohamed Salahuddin Habibullah, Jhinaoui Ahmed, Ankit Kumar Das, Yu Zhou, Choon Lim Ho

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 6/2021

Einloggen

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

search-config
loading …

Abstract

The need to monitor tool wear is crucial, particularly in advanced manufacturing industries, as it aims to maximise the lifespan of the cutting tool whilst guaranteeing the quality of workpiece to be manufactured. Although there have been many studies conducted on monitoring the health of cutting tools under a specific cutting condition, the monitoring of tool wear across multi-cutting conditions still remains a challenging proposition. In addressing this, this paper presents a framework for monitoring the health of the cutting tool, operating under multi-cutting conditions. A predictive model, using advanced machine learning methods with multi-feature multi-model ensemble and dynamic smoothing scheme, is developed. The applicability of the framework is that it takes into account machining parameters, including depth of cut, cutting speed and feed rate, as inputs into the model, thus generating the key features for the predictions. Real data from the machining experiments were collected, investigated and analysed, with prediction results showing high agreement with the experiments in terms of the trends of the predictions as well as the accuracy of the averaged root mean squared error values.

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!

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!

Literatur
Zurück zum Zitat Benkedjouh, T., Medjaher, K., Zerhouni, N., & Rechak, S. (2015). Health assessment and life prediction of cutting tools based on support vector regression. Journal of Intelligent Manufacturing, 26(2), 213–223.CrossRef Benkedjouh, T., Medjaher, K., Zerhouni, N., & Rechak, S. (2015). Health assessment and life prediction of cutting tools based on support vector regression. Journal of Intelligent Manufacturing, 26(2), 213–223.CrossRef
Zurück zum Zitat Fish, R. K., Ostendorf, M., Bernard, G. D., & Castanon, D. A. (2003). Multilevel classification of milling tool wear with confidence estimation. IEEE Transactions on Pattern Analysis and Machine Learning, 25(1), 75–85.CrossRef Fish, R. K., Ostendorf, M., Bernard, G. D., & Castanon, D. A. (2003). Multilevel classification of milling tool wear with confidence estimation. IEEE Transactions on Pattern Analysis and Machine Learning, 25(1), 75–85.CrossRef
Zurück zum Zitat Geramifard, O., Xu, J. X., Zhou, J. H., & Li, X. (2011). Continuous health condition monitoring: A single hidden semi-Markov model approach. In IEEE Conference on Prognostics and Health Management (PHM) (pp. 1–10). IEEE. Geramifard, O., Xu, J. X., Zhou, J. H., & Li, X. (2011). Continuous health condition monitoring: A single hidden semi-Markov model approach. In IEEE Conference on Prognostics and Health Management (PHM) (pp. 1–10). IEEE.
Zurück zum Zitat Geramifard, O., Xu, J. X., Zhou, J. H., & Li, X. (2012). A physically segmented hidden Markov model approach for continuous tool condition monitoring: Diagnostics and prognostics. IEEE Transactions on Industrial Informatics, 8(4), 964–973.CrossRef Geramifard, O., Xu, J. X., Zhou, J. H., & Li, X. (2012). A physically segmented hidden Markov model approach for continuous tool condition monitoring: Diagnostics and prognostics. IEEE Transactions on Industrial Informatics, 8(4), 964–973.CrossRef
Zurück zum Zitat Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157–1182. Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157–1182.
Zurück zum Zitat Hauke, Jan, & Kossowski, Tomasz. (2011). Comparison of values of Pearson’s and Spearman’s correlation coefficients on the same sets of data. Quaestiones geographicae, 30(2), 87–93.CrossRef Hauke, Jan, & Kossowski, Tomasz. (2011). Comparison of values of Pearson’s and Spearman’s correlation coefficients on the same sets of data. Quaestiones geographicae, 30(2), 87–93.CrossRef
Zurück zum Zitat ISO 3685:1993. (1993). Tool-life testing with single-point turning tools (2nd ed.). Geneva: International Organization for Standardization. ISO 3685:1993. (1993). Tool-life testing with single-point turning tools (2nd ed.). Geneva: International Organization for Standardization.
Zurück zum Zitat Kalidasan, R., Ramanuj, V., Sarma, D. K., & Senthilvelan, S. (2014). Influence of cutting speed and offset distance over cutting tool vibration in multi-tool turning process. In Advanced materials research (vol. 984, pp. 100–105), New York: Trans Tech Publications. Kalidasan, R., Ramanuj, V., Sarma, D. K., & Senthilvelan, S. (2014). Influence of cutting speed and offset distance over cutting tool vibration in multi-tool turning process. In Advanced materials research (vol. 984, pp. 100–105), New York: Trans Tech Publications.
Zurück zum Zitat Kong, D., Chen, Y., & Li, N. (2018). Gaussian process regression for tool wear prediction. Mechanical Systems and Signal Processing, 1(104), 556–574.CrossRef Kong, D., Chen, Y., & Li, N. (2018). Gaussian process regression for tool wear prediction. Mechanical Systems and Signal Processing, 1(104), 556–574.CrossRef
Zurück zum Zitat Kong, D., Chen, Y., Li, N., & Tan, S. (2017). Tool wear monitoring based on kernel principal component analysis and v-support vector regression. The International Journal of Advanced Manufacturing Technology., 89(1–4), 175–190.CrossRef Kong, D., Chen, Y., Li, N., & Tan, S. (2017). Tool wear monitoring based on kernel principal component analysis and v-support vector regression. The International Journal of Advanced Manufacturing Technology., 89(1–4), 175–190.CrossRef
Zurück zum Zitat Lenz, J., & Westkaemper, E. (2017). Wear prediction of woodworking cutting tools based on history data. Procedia CIRP, 1(63), 675–679.CrossRef Lenz, J., & Westkaemper, E. (2017). Wear prediction of woodworking cutting tools based on history data. Procedia CIRP, 1(63), 675–679.CrossRef
Zurück zum Zitat Li, X., Er, M. J., Ge, H., Gan, O. P., Huang, S., Zhai, L. Y., Linn, S., & Torabi, A. J. (2012). Adaptive network fuzzy inference system and support vector machine learning for tool wear estimation in high speed milling processes. In IECON 2012-38th annual conference on ieee industrial electronics society (pp. 2821–2826). IEEE. Li, X., Er, M. J., Ge, H., Gan, O. P., Huang, S., Zhai, L. Y., Linn, S., & Torabi, A. J. (2012). Adaptive network fuzzy inference system and support vector machine learning for tool wear estimation in high speed milling processes. In IECON 2012-38th annual conference on ieee industrial electronics society (pp. 2821–2826). IEEE.
Zurück zum Zitat Murua, M., Suárez, A., de Lacalle, L. N., Santana, R., & Wretland, A. (2018). Feature extraction-based prediction of tool wear of Inconel 718 in face turning. Insight-Non-Destructive Testing and Condition Monitoring., 60(8), 443–450.CrossRef Murua, M., Suárez, A., de Lacalle, L. N., Santana, R., & Wretland, A. (2018). Feature extraction-based prediction of tool wear of Inconel 718 in face turning. Insight-Non-Destructive Testing and Condition Monitoring., 60(8), 443–450.CrossRef
Zurück zum Zitat Pang, C. K, Zhou, J. H., Zhong, Z. W., & Lewis, F. L. (2010). Tool wear forecast using Dominant Feature Identification of acoustic emissions. In IEEE international conference on control applications (CCA) (pp. 1063–1068). IEEE. Pang, C. K, Zhou, J. H., Zhong, Z. W., & Lewis, F. L. (2010). Tool wear forecast using Dominant Feature Identification of acoustic emissions. In IEEE international conference on control applications (CCA) (pp. 1063–1068). IEEE.
Zurück zum Zitat Pratama, M., Er, M. J., Li, X., Gan, O. P., Oentaryo, R. J., Linn, S., Zhai, L., & Arifin, I. Tool wear prediction using evolutionary dynamic fuzzy neural (EDFNN) network. In IECON 2011-37th annual conference on IEEE industrial electronics society (pp. 4739–4744). IEEE. Pratama, M., Er, M. J., Li, X., Gan, O. P., Oentaryo, R. J., Linn, S., Zhai, L., & Arifin, I. Tool wear prediction using evolutionary dynamic fuzzy neural (EDFNN) network. In IECON 2011-37th annual conference on IEEE industrial electronics society (pp. 4739–4744). IEEE.
Zurück zum Zitat Ren, Q., Balazinski, M., & Baron, L. (2009). Uncertainty prediction for tool wear condition using type-2 TSK fuzzy approach. In IEEE international conference on systems, man and cybernetics, 2009. SMC (pp. 660–665). IEEE. Ren, Q., Balazinski, M., & Baron, L. (2009). Uncertainty prediction for tool wear condition using type-2 TSK fuzzy approach. In IEEE international conference on systems, man and cybernetics, 2009. SMC (pp. 660–665). IEEE.
Zurück zum Zitat Siddhpura, A., & Paurobally, R. (2013). A review of flank wear prediction methods for tool condition monitoring in a turning process. The International Journal of Advanced Manufacturing Technology, 65(1), 371–393.CrossRef Siddhpura, A., & Paurobally, R. (2013). A review of flank wear prediction methods for tool condition monitoring in a turning process. The International Journal of Advanced Manufacturing Technology, 65(1), 371–393.CrossRef
Zurück zum Zitat Taylor, F. W. (1907). On the art of cutting metals. New York: The American Society of Mechanical Engineers. Taylor, F. W. (1907). On the art of cutting metals. New York: The American Society of Mechanical Engineers.
Zurück zum Zitat Wang, J., Wang, P., & Gao, R. X. (2015). Enhanced particle filter for tool wear prediction. Journal of Manufacturing Systems., 31(36), 35–45.CrossRef Wang, J., Wang, P., & Gao, R. X. (2015). Enhanced particle filter for tool wear prediction. Journal of Manufacturing Systems., 31(36), 35–45.CrossRef
Zurück zum Zitat Wu, Q., Yang, X., & Zhou, Q. (2012). Pattern recognition and its application in fault diagnosis of electromechanical system. Journal of Information and Computational Science, 9(8), 2221–2228. Wu, Q., Yang, X., & Zhou, Q. (2012). Pattern recognition and its application in fault diagnosis of electromechanical system. Journal of Information and Computational Science, 9(8), 2221–2228.
Zurück zum Zitat Yang, F., Habibullah, M. S., Zhang, T., Xu, Z., Lim, P., & Nadarajan, S. (2016). Health index-based prognostics for remaining useful life predictions in electrical machines. IEEE Transactions on Industrial Electronics, 63(4), 2633–2644.CrossRef Yang, F., Habibullah, M. S., Zhang, T., Xu, Z., Lim, P., & Nadarajan, S. (2016). Health index-based prognostics for remaining useful life predictions in electrical machines. IEEE Transactions on Industrial Electronics, 63(4), 2633–2644.CrossRef
Zurück zum Zitat Yousefi, R., Gorjizadeh, A., & Mikaeil, R. (2011). The effect of machining parameters on force signal and tool wear in stone cutting. In AIP conference proceedings (vol. 1315, no. 1, pp. 961–966), AIP. Yousefi, R., Gorjizadeh, A., & Mikaeil, R. (2011). The effect of machining parameters on force signal and tool wear in stone cutting. In AIP conference proceedings (vol. 1315, no. 1, pp. 961–966), AIP.
Zurück zum Zitat Zhang, G., & Guo, C. (2016). Modeling flank wear progression based on cutting force and energy prediction in turning process. Procedia Manufacturing., 1(5), 536–545.CrossRef Zhang, G., & Guo, C. (2016). Modeling flank wear progression based on cutting force and energy prediction in turning process. Procedia Manufacturing., 1(5), 536–545.CrossRef
Zurück zum Zitat Zhang, J., Starly, B., Cai, Y., Cohen, P. H., & Lee, Y. S. (2017). Particle learning in online tool wear diagnosis and prognosis. Journal of Manufacturing Processes., 1(28), 457–463.CrossRef Zhang, J., Starly, B., Cai, Y., Cohen, P. H., & Lee, Y. S. (2017). Particle learning in online tool wear diagnosis and prognosis. Journal of Manufacturing Processes., 1(28), 457–463.CrossRef
Zurück zum Zitat Zhang, H., Zhang, C., Zhang, J., & Zhou, L. (2014). Tool wear model based on least squares support vector machines and Kalman filter. Production Engineering, 8(1–2), 101–109.CrossRef Zhang, H., Zhang, C., Zhang, J., & Zhou, L. (2014). Tool wear model based on least squares support vector machines and Kalman filter. Production Engineering, 8(1–2), 101–109.CrossRef
Zurück zum Zitat Zhao, R., Wang, J., Yan, R., & Mao, K. (2016). Machine health monitoring with LSTM networks. In 2016 10th international conference on sensing technology (ICST) (pp. 1–6). IEEE. Zhao, R., Wang, J., Yan, R., & Mao, K. (2016). Machine health monitoring with LSTM networks. In 2016 10th international conference on sensing technology (ICST) (pp. 1–6). IEEE.
Zurück zum Zitat Zhou, J. H., Pang, C. K., Zhong, Z. W., & Lewis, F. L. (2011). Tool wear monitoring using acoustic emissions by dominant-feature identification. IEEE Transactions on Instrumentation and Measurement, 60(2), 547–559.CrossRef Zhou, J. H., Pang, C. K., Zhong, Z. W., & Lewis, F. L. (2011). Tool wear monitoring using acoustic emissions by dominant-feature identification. IEEE Transactions on Instrumentation and Measurement, 60(2), 547–559.CrossRef
Zurück zum Zitat Zhu, M., Xiao, P., & Zhang, C. (2016). A modeling method for monitoring tool wear condition based on adaptive dynamic non-bias least square support vector machine. In International conference on system reliability and science (ICSRS) (pp. 53–59). IEEE. Zhu, M., Xiao, P., & Zhang, C. (2016). A modeling method for monitoring tool wear condition based on adaptive dynamic non-bias least square support vector machine. In International conference on system reliability and science (ICSRS) (pp. 53–59). IEEE.
Metadaten
Titel
Predicting tool wear size across multi-cutting conditions using advanced machine learning techniques
verfasst von
Yan Shen
Feng Yang
Mohamed Salahuddin Habibullah
Jhinaoui Ahmed
Ankit Kumar Das
Yu Zhou
Choon Lim Ho
Publikationsdatum
18.07.2020
Verlag
Springer US
Erschienen in
Journal of Intelligent Manufacturing / Ausgabe 6/2021
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI
https://doi.org/10.1007/s10845-020-01625-7

Weitere Artikel der Ausgabe 6/2021

Journal of Intelligent Manufacturing 6/2021 Zur Ausgabe

    Marktübersichten

    Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen.