Accurate detection of porosity in glass fiber reinforced polymers by terahertz spectroscopy

Abstract

Pores are inevitably produced during the production of glass fiber reinforced polymers (GFRP). Quantitative characterization of porosity is a most important aspect of performance evaluation of GFRP. Herein, we present a novel strategy for porosity detection of GFRP based on the interaction mechanism between terahertz (THz) wave and porous GFRP (porosity: 0.29%–4.01%; pore size: about 20–600 μm). By using the transmission and absorption spectra of GFRP, a porosity prediction model is established by combining the supervised learning approach of support vector regression (SVR) and ensemble methods, which can predict the porosity of unknown test samples with a coefficient of determination R2 = 0.976 and root mean square error RMSE = 0.174%. Conversely, THz transmission and absorption spectra for porous GFRP are successfully reconstructed by SVR and the ensemble methods. The results indicate that THz spectroscopy in combination with SVR and the ensemble methods is robust and accurate in porosity analysis and could play a significant role in industrial applications where nondestructive on-line detection of porosity in polymer composites is desirable.