The physical properties of perovskite-type materials are sensitive to their chemical composition and crystallographic structure, which makes them highly versatile for various advanced technological applications. In this theoretical study, density functional theory (DFT) is employed to investigate the electronic properties of the perovskite-like material CaTiO3, focusing on the substitution of Ti4+ with the magnetic transition metal Cr4+. The results reveal a systematic increase in the effective magnetic moment and a gradual decrease in the bandgap with increasing Cr4+ content in the CaTi1−xCrxO3 system (x = 0.0, 0.25, 0.5, 0.75, 1.0). The interactions between electronic orbitals associated with Ti-O-Cr inter-octahedral bonds modify the magnetic response of the material, leading to hybridizations between valence and conduction states that alter its semiconductor character. This tunability in electronic and magnetic properties underscores the potential of these materials for applications in spintronics. This study offers novel insights into the design of new magnetic semiconductor materials with tailored functionalities, contributing to the development of next-generation spintronic devices.
Fused deposition modeling (FDM) is one of the most widely adopted additive manufacturing (AM) technologies due to its accessibility and versatility; however, ensuring process reliability and product quality remains a significant challenge. This work introduces a novel methodology to evaluate the fidelity of programmed extruder head trajectories and speeds against those executed during the printing process. The approach integrates infrared thermography and image processing. A type-V ASTM D638-14 polylactic acid (PLA) specimen was fabricated using 16 layers, and its G-code data were systematically compared with kinematic variables extracted from long-wave infrared (LWIR) thermal images. The results demonstrate that the approach enables the detection of deviations in nozzle movement, providing valuable insights into layer deposition accuracy and serving as an early indicator for potential defect formation. This thermal image–based monitoring can serve as a non-invasive tool for in situ quality control (QC) in FDM, supporting process optimization and improved reliability of AM polymer components. These findings contribute to the advancement of smart sensing strategies for integration into industrial additive manufacturing workflows.
3 October 2018 in Revista de la Academia Colombiana de Ciencias Exactas, Físicas y Naturales