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Tool wear monitoring in ultrasonic welding using high-order decomposition

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Abstract

Ultrasonic welding has been used for joining lithium-ion battery cells in electric vehicle manufacturing. The geometric profile change of tool shape significantly affects the weld quality and should be monitored during production. In this paper, a high-order decomposition method is suggested for tool wear monitoring. In the proposed monitoring scheme, a low dimensional set of monitoring features is extracted from the high dimensional tool profile measurement data for detecting tool wear at an early stage. Furthermore, the proposed method can be effectively used to analyze the data cross-correlation structure in order to help identify the unusual wear pattern and find the associated assignable cause. The effectiveness of the proposed monitoring method was demonstrated using a simulation and a real-world case study.

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References

  • Abellan-Nebot, J. V., & Subirón, F. R. (2010). A review of machining monitoring systems based on artificial intelligence process models. The International Journal of Advanced Manufacturing Technology, 47(1–4), 237–257.

    Article  Google Scholar 

  • de Lathauwer, L., de Moor, B., & Vandewalle, J. (2000). On the best rank-1 and rank-(R1, R2,., Rn) approximation of high order tensors. SIAM Journal of Matrix Analysis and Applications, 21(4), 1324–1342.

    Article  Google Scholar 

  • He, X., Cai, D., & Niyogi, P. (2005). Tensor subspace analysis, advances in neural information processing systems, 18 (NIPS). Cambridge: MIT Press.

    Google Scholar 

  • Hotelling, H. (1947). Multivariate quality control. Techniques of Statistical Analysis, 1947, 114–184.

    Google Scholar 

  • Jolliffe, I. (2005). Principal component analysis. New York: Wiley Online Library.

    Google Scholar 

  • Kisić, E., Durović, Z., Kovačević, B., & Petrović, V. (2015). Application of \(T^{2}\) control charts and hidden Markov models in condition-based maintenance at thermoelectric power plants. Hindawi corporation, Shock and Vibration, 2015, Article ID 960349.

  • Kolda, T., & Bader, B. (2009). Tensor decompositions and applications. SIAM Rev, 51(3), 455–500.

  • Li, X., Dong, S., & Yuan, Z. (1999). Discrete wavelet transform for tool breakage monitoring. International Journal of Machine Tools and Manufacture, 39(12), 1935–1944.

    Article  Google Scholar 

  • Mason, R. L., Tracy, N. D., & Young, J. C. (1995). Decomposition of T2 for multivariate control chart interpretation. Journal of Quality Technology, 27(2), 109–119.

    Article  Google Scholar 

  • Paynabar, K., Jin, J. J., & Pacella, M. (2013). Monitoring and diagnosis of multichannel nonlinear profile variations using uncorrelated multilinear principal component analysis. IIE Transactions, 45(11), 1235–1247.

    Article  Google Scholar 

  • Shao, C., Guo, W., Kim, T. H., Jin, J. J., Hu, S. J., Spicer, J. P., & Abell, J. A. (2014). Characterization and monitoring of tool wear in ultrasonic metal welding. In 9th international workshop on microfactories, Honolulu, Hawaii, October 5–8, (pp. 161–169).

  • Shao, C., Kim, T. H., Jin, J. J., Hu, S. J., Spicer, J. P., & Abell, J. A. (2016). Tool wear monitoring for ultrasonic metal welding of lithium-ion batteries. ASME Journal of Manufacturing Science and Engineering, 138(5), 051005.

    Article  Google Scholar 

  • Shao, C., Paynabar, K., Kim, T. H., Jin, J. J., Hu, S. J., Spicer, J. P., et al. (2013). Feature selection for manufacturing process monitoring using cross-validation. Journal of Manufacturing Systems, 32(4), 550–555.

    Article  Google Scholar 

  • Shi, D., & Gindy, N. N. (2007). Tool wear predictive model based on least squares support vector machines. Mechanical Systems and Signal Processing, 21(4), 1799–1814.

    Article  Google Scholar 

  • Yan, H., Paynabar, K., & Shi, J. (2015). Image-based process monitoring using low-rank tensor decomposition. IEEE Transactions on Automation Science and Engineering, 99, 1–12.

    Google Scholar 

  • 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.

    Article  Google Scholar 

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Correspondence to Yaser Zerehsaz.

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Zerehsaz, Y., Shao, C. & Jin, J. Tool wear monitoring in ultrasonic welding using high-order decomposition. J Intell Manuf 30, 657–669 (2019). https://doi.org/10.1007/s10845-016-1272-4

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  • DOI: https://doi.org/10.1007/s10845-016-1272-4

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