From Density to Geometry: YOLOv8 Instance Segmentation for Reverse Engineering of Optimized Structures

TL;DR

This paper presents YOLOv8-TO, a novel instance segmentation approach that automatically reconstructs geometric parameters from density distributions of topology-optimized structures, improving accuracy and efficiency over traditional skeletonization methods.

Contribution

The paper introduces YOLOv8-TO, a custom-trained YOLOv8 model with a novel loss function for automatic reverse engineering of complex structures from density data.

Findings

Outperforms skeletonization with 13.84% higher Dice coefficient

Achieves up to 20.78% peak improvement in reconstruction accuracy

Demonstrates fast inference and good generalization to complex geometries

Abstract

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.