Integration of digital imagery for topology optimization

To manufacture high-quality products with low manufacturing costs and optimal performance, better design concepts are required. The initial design concept can lead to inefficient structural design and higher manufacturing costs if the topology is not optimal. Topology optimization enables designers to reach their design goals faster, more accurately, and cost-effectively. However, the geometry obtained through topology optimization is not manufacturing-ready due to non-smooth boundaries and gray level images, which require post-processing design implementation by engineers. Various researchers have used different image processing techniques to convert the gray image into a binary map to address this issue. This paper focuses on using image processing to evaluate the differences in optimal designs induced by meshing. This study aims to aid in the parametric understanding of different designs targeting the same application by introducing two new parameters: similarity ratio and conformity ratio. The results compare an optimal geometry obtained using structured and unstructured meshes. Topological optimization algorithms applied to mechanical problems allow for reducing a structure's mass while ensuring its rigidity. However, the final structures may differ for the same problem depending on whether they were meshed regularly or irregularly. This article characterizes the differences between the two final structures using an image processing approach.