High-precision temperature-strain composite sensing based on a single resonator-type surface acoustic wave sensor using machine learning algorithms

Surface acoustic wave (SAW) sensors are extensively employed for temperature/strain measurement. However, precise temperature/strain sensing remains challenging under complex conditions involving simultaneous mechanical strains and temperature fluctuations, due to the inherent cross-sensitivity between temperature and strain. To address this challenge, we developed a single resonator-type SAW sensor operable up to 600 °C and integrated machine learning techniques for temperature-strain multiphysics decoupling. Six machine learning models are implemented and evaluated using normalized root mean square error ( NRMSE ), root mean square error ( RMSE ), mean absolute error ( MAE ), coefficient of determination ( R² ), and execution time ( ET ) metrics. Results demonstrate that the extreme gradient boosting (XGBoost) model achieves optimal predictive performance from 22 to 160 °C and applied strains of 0 με to 800 με. For temperature/strain predictions respectively, R² values reach 99.97% and 99.23%, simultaneously enabling high-precision temperature and strain sensing. Our study presents an effective solution for simultaneous temperature and strain sensing using a single resonator-type SAW sensor operating in a single acoustic wave mode.