Skip to main content

Advertisement

Log in

Optimization of processing parameters for waterjet-guided laser machining of SiC/SiC composites

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

Abstract

Interactions between light and matter during short-pulse water-jet guided laser materials processing are highly nonlinear, and acutely sensitive to laser machining parameters. Traditionally, the physical simulation calculation methods based on laser, water and composite materials are complicated. This work combines neural networks and physical simulation models in the understanding of laser drilling of composite materials. Neural networks are used to predict SiC/SiC composites laser drilling results by using processing parameters (average power, scanning speed, and filling spacing) as input parameters, optimal combinations of processing parameters based on the neural network are identified, and the effectiveness of the learned knowledge is validated using a physical simulation model. The results show that the neural network can identify the nonlinear effect of processing parameters on machining quality with the MAE of 0.054 and the RMSE of 0.067. The physical simulation model could explain why this nonlinear effect exists. This method can be applied to a wide range of fields. In the face of unknown material and physical processing processes, the approach of combining neural networks and physical simulation models has the potential to significantly reduce the optimization time and deepen the understanding of laser processing.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

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
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

Download references

Acknowledgements

This work was supported by the Science Center for Gas Turbine Project (P2022-AB-IV-002-002).

Funding

Science Center for Gas Turbine Project, P2022-AB-IV-002-002, songmei yuan.

Author information

Authors and Affiliations

Authors

Contributions

MG: Investigation, Methodology, Experimentation, Calculation, Writing—original draft. SY: Supervision, Writing—review & editing, Funding acquisition. JW: Investigation, Methodology, Experimentation. JN: Investigation, Methodology, Calculation. ZZ: Investigation, Methodology. XL: Physical simulation. JZ: Methodology. NZ: Methodology. ML: Methodology.

Corresponding author

Correspondence to Songmei Yuan.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gao, M., Yuan, S., Wei, J. et al. Optimization of processing parameters for waterjet-guided laser machining of SiC/SiC composites. J Intell Manuf 35, 4137–4157 (2024). https://doi.org/10.1007/s10845-023-02225-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-023-02225-x

Keywords

Navigation