Comparativo entre peças impressas em 3D geradas por design tradicional, generativo e peças otimizadas topologicamente

Abstract

With the advancements in computational capabilities, artificial inteligence and machine learning, new design methods that enable the generation of geometries optmized based on the project stress requirements or aiming to reduce mass. At this dissertation was studied the differences in mechanical strength of a part generated by traditional 3D methods, topology optmization and generative design methods, designing and 3D printing the generated geometries, producing 5 samples each with a distinctive mass for each method, and comparing them to a traditionally designed sample that won’t have mass variation. With the goal of measuring the effectiveness of these methods, it was used the relation between the supported force and mass of the samples(CM). At data analisys the generative design methodology demonstrated gains on the CM relation when compared to the topology optimized, that prove it is relevance in optimizing an existing project and enabled the design engineer to rapdly iterate/test it’s designs.