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.

翻译：本文提出YOLOv8-TO，一种利用YOLOv8实例分割模型将拓扑优化结构逆向工程转化为可解释几何参数的新方法。基于密度的拓扑优化方法需要通过后处理将最优密度分布转化为参数化表示，以支持设计探索并与CAD工具集成。传统方法如骨架化在处理复杂几何时存在困难且需要人工干预。YOLOv8-TO通过训练定制YOLOv8模型自动检测并重建二元密度分布中的结构组件，解决了上述挑战。模型在采用移动可变形组件法生成的多样化数据集（包括优化结构与随机结构）上进行训练。基于预测几何的Dice系数构建的自定义重建损失函数，通过自监督学习训练模型的新回归头。该方法在来自不同拓扑优化方法（含分布外样本）的测试集上进行了评估，并与骨架化方法进行了对比。结果表明，YOLOv8-TO在重建视觉与结构相似性设计方面显著优于骨架化方法。该方法在Dice系数上平均提升13.84%，峰值提升达20.78%。该方法展现出对复杂几何的良好泛化能力与快速推理速度，使其适用于常规工作站上的设计工作流集成。局限性包括对非极大值抑制阈值的敏感性。YOLOv8-TO标志着拓扑优化后处理领域的重大进展，为设计探索与制造提供了高效精准的优化结构逆向工程能力。