  This paper introduces YOLOv8-TO, a novel approach for reverse engineering of
topology-optimized structures into interpretable geometric parameters using the
YOLOv8 instance segmentation model. Density-based topology optimization methods
require post-processing to convert the optimal density distribution into a
parametric representation for design exploration and integration with CAD
tools. Traditional methods such as skeletonization struggle with complex
geometries and require manual intervention. YOLOv8-TO addresses these
challenges by training a custom YOLOv8 model to automatically detect and
reconstruct structural components from binary density distributions. The model
is trained on a diverse dataset of both optimized and random structures
generated using the Moving Morphable Components method. A custom reconstruction
loss function based on the dice coefficient of the predicted geometry is used
to train the new regression head of the model via self-supervised learning. The
method is evaluated on test sets generated from different topology optimization
methods, including out-of-distribution samples, and compared against a
skeletonization approach. Results show that YOLOv8-TO significantly outperforms
skeletonization in reconstructing visually and structurally similar designs.
The method showcases an average improvement of 13.84% in the Dice coefficient,
with peak enhancements reaching 20.78%. The method demonstrates good
generalization to complex geometries and fast inference times, making it
suitable for integration into design workflows using regular workstations.
Limitations include the sensitivity to non-max suppression thresholds.
YOLOv8-TO represents a significant advancement in topology optimization
post-processing, enabling efficient and accurate reverse engineering of
optimized structures for design exploration and manufacturing.
