Frequency-dependent dielectric analysis and machine learning modelling of 3D-Printed acrylonitrile butadiene styrene filled with aluminium tri-hydroxide composites

Due to the increasing demand for dielectric polymers that offer high efficiency and reliability in electronic and electrical systems, several studies have examined polymeric composites with enhanced dielectric properties. In the current research, Acrylonitrile Butadiene Styrene (ABS) polymer loaded with different amounts of Aluminium Trihydroxide (ATH) was prepared using the Fused Filament Fabrication (FFF) technique to evaluate its dielectric characteristics. The dielectric properties of pure ABS and ABS-reinforced ATH samples were studied at various temperatures and frequencies; the results show a clear enhancement in their dielectric performance with increasing ATH content. There was 50%, 65% and 80% increment in the dielectric constant (ɛʼ) of ABS/ATH samples containing 30%, 40%, and 50% ATH, respectively, relative to the pure ABS polymer. The dielectric losses (tan δ) showed pronounced relaxation peaks in the glass transition temperature regime (70-90°C), indicating efficient viscoelastic relaxation and higher energy losses. The impedance analysis showed a 35-55 % decrease in impedance between the 30-150°C temperature intervals, illustrating better charge movement and lower interfacial resistance. A regression-based machine learning approach was used for predicting the dielectric characteristics based on the printing process parameters and filler percentage, providing accurate predictions and highlighting the most important variables that affect dielectric behavior. The SEM and EDS characterization confirms the preparation of an effectively dispersed composite structure, which is expected to improve interfacial properties and dielectric efficiency.