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To analyze the impact of different objective functions and additive manufacturing (AM) constraints on structural topology optimization, it is necessary to perform an in-depth comparative study. This analysis should consider specific structural design factors, such as compliance, volume, or stress minimization, and assess their effects on the topology optimization for AM. In addition, the inclusion of AM constraints can have a significant influence on various aspects, including optimal part geometry, part volume, support structure volume, and structural performance. Thus, it is essential to investigate and compare these factors to determine the optimal part design for AM. This study focuses on comparing topology optimization results obtained using compliance, stress, or multi-objective minimization, with and without AM constraints. A comparative analysis was conducted in the study, utilizing three structural design examples: cantilever beam, bridge-shaped structure, and L-shaped beam. The comparison results provide insights into the effects of build orientation, AM constraints such as overhang, and different design objectives on the structural topology optimization for AM. [ABSTRACT FROM AUTHOR]

Modularity indices based on Design Structure Matrices (DSMs) have been utilised to help measure modularity and cluster a product’s architecture into independent or coordinated modules, but many metrics have difficulty (a) measuring the modularity of different types of architectures in real-world products such as bus-type architectures and (b) optimising module boundaries in architectures. After reviewing existing modularity indices and clustering algorithms, we introduce new modularity indices that can capture the degrees of (1) connection strengths within each independent module and between different modules, (2) density of connections within each module and between modules, (3) proximity of interactions to the diagonal of the DSM, and (4) density of connections between buses and other components. Moreover, the proposed metrics can serve as objective functions to obtain optimal DSMs to maximise modularity. A comparative analysis of the proposed modularity index shows that the proposed metric can measure the modularity for 28 different types of DSMs unlike other metrics. Also, the module definition results for an aircraft engine with complex connections between components indicate that clustering using the new modularity indices can help obtain optimal modular product architectures with higher modularity compared to existing clustering results in the literature. [ABSTRACT FROM PUBLISHER]