A non-destructive, real-time method for estimating the volume fraction of a dielectric mixture inside a resonant cavity is presented. A convolutional neural network (CNN)-based approach is used to estimate the fractional composition of two-phase dielectric mixtures inside a resonant cavity using scattering parameter (S-parameter) measurements. A rectangular cavity sensor with a strip feed structure is characterized using a vector network analyzer (VNA) from 0.01--20~GHz. The CNN is trained using both simulated and experimentally measured S-parameters and achieves high predictive accuracy even without de-embedding or filtering, demonstrating robustness to measurement imperfections. The simulation results achieve a coefficient of determination (R2)=0.99 using k-fold cross-validation, while the experimental model using raw data achieves an R2=0.94 with a mean absolute error (MAE) below 6\%. Data augmentation further improves the accuracy of the experimental prediction to above R2=0.998 (MAE<0.72\%). The proposed method enables rapid, non-destructive, accurate, low-cost, and real-time estimation of material fractions, illustrating strong potential for sensing applications in microwave material characterization.
@article{arxiv.2512.13233,
title = {Measurement of Material Volume Fractions in a Microwave Resonant Cavity Sensor Using Convolutional Neural Network},
author = {Mojtaba Joodaki and Idriz Pelaj},
journal= {arXiv preprint arXiv:2512.13233},
year = {2025}
}