Estimating the elastic constants of orthotropic composites using guided waves and an inverse problem of property estimation

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. Limited number of unique sets of simulations were conducted. Out of available simulation data, 30% of the sets were used for validation. 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, by using a model solely trained on simulation and a very scare amount of data. This approach has the potential to significantly reduce the computational time for predicting the elastic constants, and thereby could have wideranging applications in materials science and engineering.

Automated summary

This research focuses on estimating the elastic constants of orthotropic laminates using ultrasonic guided waves and inverse machine learning models. The results show that this approach has the potential to accurately predict the elastic constants of a material and reduce computational time.