Vigilancia Tecnológica

This study presents a predictive multiscale modeling tool for defect analysis of fused filament fabricated?printed materials and their performance prediction using a mechanistic data science?based reduced?order modeling approach.Process?induced defects are inherent to additively manufactured parts and significantly influence the performance of printed materials. This article introduces a predictive performance modeling framework for defect analysis in fused filament fabrication (FFF)?printed materials, leveraging a mechanistic data science?based reduced?order modeling (ROM) approach. The proposed model addresses process?induced variabilities by quantifying defects across micro? and mesoscales and integrating them into part?scale performance predictions. Employing mechanistic ROM enables concurrent multiscale modeling of FFF?printed materials, explicitly accounting for local microstructures and defects. A thermoelastic extension of the mechanistic ROM is introduced to evaluate the thermal residual stresses developed during the FFF process. Applying the mechanistic ROM framework, defects in FFF?printed polylactic acid (PLA) and PLA with short carbon fiber (PLA/SCF) composites are analyzed to establish a multiscale model for predicting the mechanical performance of tensile and three?point bending specimens. The results highlight that accurate material performance prediction relies on capturing process?induced defects, with the mechanistic ROM successfully simulating behavior across both local and part?scale domains. The proposed defect analysis and modeling approach can be extended to other additive manufacturing processes to offer valuable insights into microstructure?guided material design.