Masurkar, F., Aggarwal, S., Wen Tham, Z., Zhang, L., Yang, F., & Cui, F. (2024). Estimating the elastic constants of orthotropic composites using guided waves and an inverse problem of property estimation. Applied Acoustics, 216, 109750. https://doi.org/10.1016/j.apacoust.2023.109750
Abstract:
The present research focusses on estimating the elastic constants of orthotropic laminates using ultrasonic guided waves (GWs) excited through Lead Zirconate Titanate (PZT) sensors and sensed using one-dimensional laser vibrometer. The elastic constants of a material are crucial for understanding its mechanical behaviour and are typically determined through experimental testing. However, this process can be time-consuming and expensive. We formulate this problem as an inverse problem of property estimation. Thus, in this work, the simulation models with PZT transducers have been employed for generating time series (TS) GWs for the orthotropic material. Then, an inverse machine learning model is trained using a TS dataset pertaining to different elastic constants generated using the simulations. The inverse model consists of deep neural networks and designing a loss function for the specific application. A total of 135 unique sets of
simulations are conducted with one set being the actual elastic constants. Out of these sets, 41 sets itself are used for validation. In order to further test the model, a blind experimental test was conducted, and the corresponding elastic constants were estimated with a mean absolute percentage error (MAPE) of 12.89% and standard deviation of 5.47%. The results demonstrate that formulation of property estimation as inverse
problem is capable of accurately predicting the elastic constants of a material, with a high level of accuracy by using a model solely trained on simulation and a very scare amount of data. This approach has the potential to significantly reduce the time and cost associated with experimental testing and could have wide-ranging applications in materials science and engineering.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - RIE2020 AME Industry Alignment Fund – Pre-positioning Programme (IAF-PP)
Grant Reference no. : A20F5a0043
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - RIE2020 AME Industry Alignment Fund – Pre-positioning Programme (IAF-PP)
Grant Reference no. : A19C9a0044