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This thesis explores using multi-scale topology optimization (TO) by utilizing inverse homogenization to automate the adjustment of each unit-cell's geometry and placement in a lattice structure within a pressure vessel (the design domain) to achieve desired structural properties. The aim is to find the optimal material distribution within the design domain as well as desired material properties at each discretized element and use machine learning (ML) to map microstructures with corresponding prescribed effective properties. Effective properties are obtained through homogenization, where microscopic properties are upscaled to macroscopic ones. The symmetry group of a unit-cell's elasticity tensor can be utilized for stiffness directional tunability, i.e., to tune the cell's performance in different load directions.  A few geometrical variations of a chosen unit-cell were homogenized to build an effective anisotropic elastic material model by obtaining their effective elasticity. The symmetry group and the stiffness directionality of the cells’ effective elasticity tensors were identified. This was done using both the pattern of the matrix representation of the effective elasticity tensor and the roots of the monoclinic distance function. A cell library of symmetry-preserving variations with a corresponding material property space was created, displaying the achievable properties within the library. Two ML models were implemented to map material properties to appropriate cells. A TO algorithm was also implemented to produce an optimal material distribution within a design domain of a pressure vessel in 2D to maximize stiffness. However, the TO algorithm to obtain desired material properties for each element in the domain was not realized within the time frame of this thesis.  The cells were successfully homogenized. The effective elasticity tensor of the chosen cell was found to belong to the cubic symmetry group in its natural coordinate system. The results suggest that the symmetry group of an elasticity tensor retrieved through numerical experiments can be identified using the monoclinic distance function. If near-zero minima are present, they can be utilized to find the natural coordinate system. The cubic symmetry allowed the cell library's material property space to be spanned by only three elastic constants, derived from the elasticity matrix. The orthotropic symmetry group can enable a greater directional tunability and design flexibility than the cubic one. However, materials exhibiting cubic symmetry can be described by fewer material properties, limiting the property space, which could make the multi-scale TO less complex. The ML models successfully predicted the cell parameters for given elastic constants with satisfactory results. The TO algorithm was successfully implemented. Two different boundary condition cases were used – fixing the domain’s corner nodes and fixing the middle element’s nodes. The latter was found to produce more sensible results. The formation of a cylindrical outer shape could be distinguished in the produced material design, which was deemed reasonable since cylindrical pressure vessels are consistent with engineering practice due to their inherent ability to evenly distribute load. The TO algorithm must be extended to include the elastic constants as design variables to enable the multi-scale TO.

[pt] Os recentes avanços das técnicas de manufatura aditiva vêm ampliando a sua flexibilidade em fabricar peças complexas em escala cada vez menores. Neste contexto, o projeto de microestruturas porosas vem se destacando na comunidade científica devido a capacidade de se otimizar a topologia da célula para atender aos requisitos de projeto. No entanto, existem vários desafios que dificultam a fabricação de peças obtidas pelo método de otimização topológica multiescala, dentre eles, a conectividade das microestruturas. A otimização topológica multiescala consiste na otimização tanto da macroescala, estrutura global, quanto da microescala, microestrutura do material. O objetivo principal deste trabalho é desenvolver um esquema eficaz para garantir a transição entre as diferentes microestruturas de material obtidas na otimização multiescala. As metodologias multiescala de otimização topológica simultânea de ambas as escalas e os procedimentos de homogeneização são descritos. Apresentam-se os principais aspectos numéricos e computacionais destes métodos, assim como exemplos ilustrativos. / [en] Recent advances in additive manufacturing techniques have increased their flexibility in making complex parts on a smaller scale. In this context, the design of porous microstructures has been standing out in the scientific community due to the ability to optimize the cell topology to meet the design requirements. However, there are several challenges that inhibit the fabrication of optimized parts obtained by the multi-scale topology optimization method, such as the connectivity of microstructures. The multiscale topological optimization consists of the optimization of both the macro-scale, global structure, and the micro-scale, microstructure of the material. The main objective of this work is to develop an effective scheme to guarantee compatibility in the transition between the different material microstructures obtained in multiscale optimization. The multiscale methodologies for simultaneous topological optimization of both scales and the homogenization procedures are described. The main numerical and computational aspects of these methods are presented, as well as representative examples to illustrate the capabilities of the proposed scheme.