From Density to Geometry: YOLOv8 Instance Segmentation for Reverse Engineering of Optimized Structures
arxiv(2024)
摘要
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
with peak enhancements reaching 20.78
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.
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