This research addresses a   critical gap in the structural design of 3D-printed glass by integrating   anisotropic material behavior, specifically transverse isotropy, into topology   optimization algorithms. Traditional optimization methods assume isotropic   material properties, failing to accurately represent the unique   characteristics introduced by the layer-by-layer nature of glass additive   manufacturing. Factors such as incomplete fusion between layers and   geometry-induced corrugations significantly influence structural behavior,   yet their exact roles remain unclear.

The novelty of this thesis lies   in modifying the Finite Element Analysis (FEA) component of the Solid   Isotropic Material with Penalization (SIMP) algorithm to incorporate   transverse isotropy directly into the optimization loop. The methodology   includes flexural testing to precisely identify the engineered  properties of 3D-printed specimens and extensive comparative analysis between   pure SIMP algorithms and the newly proposed algorithm. This comparison   evaluates the structural behavior improvements provided by the new approach.

The main outcome of this   research is the successful integration of transverse isotropy into the   optimization algorithm, clearly evidenced by the objective function (compliance)   metrics and enhanced principal stress distribution in the resulting   geometries. The optimized designs produced by the new algorithm closely align   with the practical capabilities and constraints of 3D-printed glass,   effectively bridging advanced computational design with realistic manufacturing   applications for structurally efficient glass architecture.