Inverse characterization of composites using guided waves and convolutional neural networks with dual-branch feature fusion

Abstract

In this work, ultrasonic guided waves and a dual-branch version of convolutional neural networks are used to solve two different but related inverse problems, i.e., finding layup sequence type and identifying material properties. In the forward problem, polar group velocity representations are obtained for two fundamental Lamb wave modes using the stiffness matrix method. For the inverse problems, a supervised classification-based network is implemented to classify the polar representations into different layup sequence types (inverse problem − 1) and a regression-based network is utilized to identify the material properties (inverse problem − 2).

Keywords:

• Material characterization
• property identification
• inverse problem
• guided waves
• deep learning
• dual-branch CNN

Acknowledgments

J.S. acknowledge funding from the Accelerated Materials Development for Manufacturing Program at A*STAR via the AME Programmatic Fund by the Agency for Science, Technology and Research under Grant No. A1898b0043.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data Availability

The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.

Notes

* https://github.com/mahindrautela/MatChar_dualCNN

Additional information

Funding

J.S. acknowledge funding from the Accelerated Materials Development for Manufacturing Program at A*STAR via the AME Programmatic Fund by the Agency for Science, Technology and Research under Grant No. A1898b0043.