Additive Manufacturing (AM) process parameters significantly influence the thermal, mechanical, and surface roughness properties of printed parts. While experimental characterization is highly accurate, it is often excessively time-consuming and costly, hindering the development of scalable predictive models. This study adopts a Materials Informatics approach to curate a multi-sourced benchmarking dataset comprising 543 entries for ABS, PLA, and TPU filaments, integrating data from web-scraping (WSDS), literature (LDDS), and manufacturer technical datasheets (MTDS). Data cleaning included outlier detection via Tukey’s fences and the evaluation of imputation strategies, with K-Nearest Neighbors (KNN) consistently outperforming simple mean/median methods. We evaluated three regression models, Random Forest (RF), Ridge Regression (RR), and Support Vector Regression (SVR), using a nested cross-validation pipeline to predict tensile strength, elongation, and surface roughness. The results reveal significant data gaps in existing literature, with missingness exceeding 60% for critical parameters like fan speed and wall thickness. While the Random Forest model achieved high predictive accuracy for bulk mechanical properties, specifically a test-set R2 of 0.995 for PLA elongation, all models struggled with surface roughness, often yielding negative R2 values. This suggests that current process descriptors are insufficient to capture the stochastic nature of surface morphology. This work provides a foundational benchmarking dataset for the research community and highlights the necessity for stricter data mining protocols and more sophisticated feature engineering to achieve reliable predictive quality in material extrusion (MEX) polymers.

Applications of 3D printed metallic alloy materials like Ti6Al4V or Ti64 offer significant advantages for certain applications and incorporates complex geometries like lattice structures achieving material optimization. This study analyzes the thermal processing of i-SLS 3D-printed Ti6Al4V-PA12 composites with varying lattice structures (Solid, Gyroid, and Sinusoid). This paper analyzes the chemical barriers and morphological indicators of the sintered composites using SEM-EDX and Optical Microscopy, Pearson Correlation is calculated for statistical analysis. Results among the varying sintering profiles showed that sintering at 1250 °C (Intermediate Temperature Profile) has progress on initial Ti64 contact. Outcomes are dependent with the high surface-area-to volume ratio of the geometric shapes, leading to higher residual carbon decomposition, but prone to surface oxidation. Oxidation and carbon residues are factors interfering with the necking between Ti6Al4V particles. Thus, high temperature treatment for Ti6Al4V sintering under high purity argon atmosphere showed a lack of densification and failed to reach a sintered state.