Skip to main content

Advertisement

SpringerLink
Log in

Real-time monitoring of high-power disk laser welding statuses based on deep learning framework

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

The laser welding quality is determined by its welding statuses, and online welding statuses are depicted by the real-time signals captured from the welding process. A multiple-sensor system is designed to obtain information as comprehensive as possible for welding statuses monitoring. The multiple-sensor system includes an auxiliary illumination visual sensor system, an ultraviolet and visible band visual sensor system, a spectrometer and two photodiodes. The signals captured by different sensors are analyzed via signal or digital image processing algorithms, and distinct features are extracted from these signals to depict the online welding statuses. A deep learning framework based on stacked sparse autoencoder (SSAE) is established to model the relationship between the multi-sensor features and their corresponding welding statuses, and Genetic algorithm (GA) is applied to optimize the parameters of the SSAE framework (SSAE-GA). The proposed framework achieves higher accuracy and stronger robustness in monitoring welding status by comparing with the backpropagation neural network, support vector machine and random forest. Three new experiments with different welding parameters are implemented to validate the effectiveness and generalization of our proposed method. This study provides a novel and accurate method for high-power disk laser welding status monitoring.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  • Alvear-Sandoval, R. F., & Figueiras-Vidal, A. R. (2018). On building ensembles of stacked denoising auto-encoding classifiers and their further improvement. Information Fusion,39, 41–52.

    Article  Google Scholar 

  • Atabaki, M. M., Yazdian, N., Ma, J., & Kovacevic, R. (2016). High power laser welding of thick steel plates in a horizontal butt joint configuration. Optics & Laser Technology,83, 1–12.

    Article  Google Scholar 

  • Charte, D., Charte, F., García, S., Jesus, M. J., & Herrera, F. (2018). A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines. Information Fusion,44, 78–96.

    Article  Google Scholar 

  • Cho, J. H., & Na, S. J. (2007). Theoretical analysis of keyhole dynamics in polarized laser drilling. Journal of Physics D: Applied Physics,40, 7638–7647.

    Article  Google Scholar 

  • Dos Santos, E. M., Sabourin, R., & Maupin, P. (2009). Overfitting cautious selection of classifier ensembles with genetic algorithms. Information Fusion,10, 150–162.

    Article  Google Scholar 

  • Gao, X. D., & Wen, Q. (2013). Monitoring of high-power fiber laser welding based on principal component analysis of a molten pool configuration. Laser Physics,23, 126001.

    Article  Google Scholar 

  • Gao, X., You, D., & Katayama, S. (2012). Infrared image recognition for seam tracking monitoring during fiber laser welding. Mechatronics,22, 370–380.

    Article  Google Scholar 

  • Garcia-Allende, P. B., Mirapeix, J., Conde, O. M., Cobo, A., & Lopez-Higuera, J. M. (2009). Spectral processing technique based on feature selection and artificial neural networks for arc-welding quality monitoring. NDT & E International,42, 56–63.

    Article  Google Scholar 

  • Greses, J., Hilton, P. A., Barlow, P. A., & Steen, W. M. (2004). Plume attenuation under high power Nd: yttritium–aluminum–garnet laser welding. Journal of Laser Applications,16, 9–15.

    Article  Google Scholar 

  • Jha, M. N., Pratihar, D. K., Bapat, A. V., Dey, V., Ali, M., & Bagchi, A. C. (2014). Knowledge-based systems using neural networks for electron beam welding process of reactive material (Zircaloy-4). Journal of Intelligent Manufacturing,25, 1315–1333.

    Article  Google Scholar 

  • Jia, F., Lei, Y., Lin, J., Zhou, X., & Lu, N. (2016). Deep neural networks: a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mechanical Systems and Signal Processing,72–73, 303–315.

    Article  Google Scholar 

  • Kong, F., Ma, J., Carlson, B., & Kovacevic, R. (2012). Real-time monitoring of laser welding of galvanized high strength steel in lap joint configuration. Optics & Laser Technology,44(7), 2186–2196.

    Article  Google Scholar 

  • Kuo, C. F. J., Tung, C. P., & Weng, W. H. (2019). Applying the support vector machine with optimal parameter design into an automatic inspection system for classifying micro-defects on surfaces of light-emitting diode chips. Journal of Intelligent Manufacturing,30, 727–741.

    Article  Google Scholar 

  • Liu, C. Q., Li, Y. G., Zhou, G. Y., & Shen, W. M. (2018). A sensor fusion and support vector machine based approach for recognition of complex machining conditions. Journal of Intelligent Manufacturing,29, 1739–1752.

    Article  Google Scholar 

  • Liu, Y., Zhou, S., & Chen, Q. (2011). Discriminative deep belief networks for visual data classification. Pattern Recognition,44(10–11), 2287–2296.

    Article  Google Scholar 

  • Mirapeix, J., García-Allende, P. B., Cobo, A., Conde, O. M., & López-Higuera, J. M. (2007). Real-time arc-welding defect detection and classification with principal component analysis and artificial neural networks. NDT & E International,40, 315–323.

    Article  Google Scholar 

  • Molino, A., Martina, M., Vacca, F., Masera, G., Terreno, A., Pasquettaz, G., et al. (2009). FPGA implementation of time–frequency analysis algorithms for laser welding monitoring. Microprocessors and Microsystems,33, 179–190.

    Article  Google Scholar 

  • Paleocrassas, A. G., & Tu, J. F. (2010). Inherent instability investigation for low speed laser welding of aluminum using a single-mode fiber laser. Journal of Materials Processing Technology,210(10), 1411–1418.

    Article  Google Scholar 

  • Pang, S., Chen, X., Shao, X., Gong, S., & Xiao, J. (2016). Dynamics of vapor plume in transient keyhole during laser welding of stainless steel: Local evaporation, plume swing and gas entrapment into porosity. Optics and Lasers in Engineering,82, 28–40.

    Article  Google Scholar 

  • Pang, S., Chen, X., Zhou, J., Shao, X., & Wang, C. (2015). 3D transient multiphase model for keyhole, vapor plume, and weld pool dynamics in laser welding including the ambient pressure effect. Optics and Lasers in Engineering,74, 47–58.

    Article  Google Scholar 

  • Pimenov, D. Y., Bustillo, A., & Mikolajczyk, T. (2018). Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth. Journal of Intelligent Manufacturing,29, 1045–1061.

    Article  Google Scholar 

  • Rodil, S. S., Gómez, R. A., Bernárdez, J. M., Rodríguez, F., Miguel, L. J., & Perán, J. R. (2010). Laser welding defects detection in automotive industry based on radiation and spectroscopical measurements. International Journal of Advanced Manufacturing Technology,49(1), 133–145.

    Article  Google Scholar 

  • Ronao, C. A., & Cho, S. B. (2016). Human activity recognition with smartphone sensors using deep learning neural networks. Expert Systems with Applications,59, 235–244.

    Article  Google Scholar 

  • Sforza, P., & Blasiis, D. (2002). Online optical monitoring system for arc welding. NDT & E International,35, 37–43.

    Article  Google Scholar 

  • Shao, H., Jiang, H., Wang, F., & Zhao, H. (2017). An enhancement deep feature fusion method for rotating machinery fault diagnosis. Knowledge-Based Systems,119, 200–220.

    Article  Google Scholar 

  • Sibillano, T., Ancona, A., Berardi, V., & Lugara, P. M. A. (2009). Real-time spectroscopic sensor for monitoring laser welding processes. Sensors,9(5), 3376–3385.

    Article  Google Scholar 

  • Srinivasa Murthy, Y. V., & Koolagudi, S. G. (2018). Classification of vocal and non-vocal segments in audio clips using genetic algorithm based feature selection (GAFS). Expert Systems with Applications,106, 77–91.

    Article  Google Scholar 

  • Tamilselvan, P., & Wang, P. (2013). Failure diagnosis using deep belief learning based health state classification. Reliability Engineering & System Safety,115, 124–135.

    Article  Google Scholar 

  • Tan, W., Bailey, N. S., & Shin, Y. C. (2013). Investigation of keyhole plume and molten pool based on a three-dimensional dynamic model with sharp interface formulation. Journal of Physics D: Applied Physics,46, 055501.

    Article  Google Scholar 

  • Wang, L., Gao, X., & Chen, Z. (2018). Status analysis of keyhole bottom in laser-MAG hybrid welding process. Optics Express,26, 347–355.

    Article  Google Scholar 

  • Zhang, Y. X., & Gao, X. D. (2013). Analysis of characteristics of molten pool using cast shadow during high-power disk laser welding. The International Journal of Advanced Manufacturing Technology,70, 1979–1988.

    Article  Google Scholar 

  • Zhang, Y. X., Gao, X. D., & Katayama, S. (2015). Weld appearance prediction with BP neural network improved by genetic algorithm during disk laser welding. Journal of Manufacturing Systems,34, 53–59.

    Article  Google Scholar 

  • Zhang, Y., Li, F. Z., Liang, Z. C., Ying, Y. Y., Lin, Q. D., & Wei, H. Y. (2018). Correlation analysis of penetration based on keyhole and plasma plume in laser welding. Journal of Materials Processing Technology,256, 1–12.

    Article  Google Scholar 

Download references

Acknowledgements

This work was partly supported by the National Natural Science Foundation of China (Grant Numbers 51675104, 61703110), Innovation Team Project, Department of Education of Guangdong Province, China (2017KCXTD010), the Guangdong Provincial Natural Science Foundation of China (Grant Numbers 2017A030310494, 2016A030310347) and Youth Science Foundation of Guangdong University of Technology (Grant Number 16ZK0010). Many thanks are given to Katayama Laboratory of Osaka University for their assistance of laser welding experiments.

Author information

Authors and Affiliations

  1. Guangdong Provincial Welding Engineering Technology Research Center, Guangdong University of Technology, Guangzhou, 510006, China

    Yanxi Zhang, Deyong You, Xiangdong Gao, Congyi Wang & Yangjin Li

  2. US-China Youth Education Solutions Foundation, New York, NY, 10019, USA

    Perry P. Gao

Authors
  1. Yanxi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Deyong You
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiangdong Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Congyi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yangjin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Perry P. Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiangdong Gao.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Y., You, D., Gao, X. et al. Real-time monitoring of high-power disk laser welding statuses based on deep learning framework. J Intell Manuf 31, 799–814 (2020). https://doi.org/10.1007/s10845-019-01477-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-019-01477-w

Keywords

Search

Navigation