Electromechanical characterization and deep learning-based force prediction in 3D-printed PLA core–shell composites

In this study, composite samples were produced using fused deposition modeling to contain an outer layer of red insulating polylactic acid and an inner core of black conductive polylactic acid. The mechanical properties of the samples were determined using three-point bending experiments, and their electro-mechanical impedance responses during loading were monitored using impedance experiments. To examine the effects of physical aging and crystallization, the composites were heat-treated at 50 °C for 2, 4, and 6 hours. The insulating polylactic acid exhibited brittle fracture with a sharp post-peak force drop, whereas the conductive polylactic acid showed a ductile fracture mode accompanied by substantial energy dissipation. The physically aged composites were stiffer and less ductile, which was attributed to the presence of partially crystallized and partially relaxed molecules, especially around the glass transition region. In support of the observed properties, scanning electron microscopy images confirmed that the fractured layers were smoother compared to the ductile conductive layers, which had surface roughness patterns resembling fibrillated or lamellar structures. Additionally, the impedance signals obtained during the bending experiments were linked with the corresponding mechanical properties using an advanced deep learning model that could model the electric signals to accurately provide the corresponding real-time force values.