Defect detection and repair algorithm for structures generated by topology optimization based on 3D hierarchical fully convolutional network

Abstract

Customized 3D-printed structural parts are widely used in surgical robotics. To satisfy the mechanical properties and kinematic functions of these structural parts, a topology optimization technique is adopted to obtain the optimal structural layout while meeting the constraints and objectives. However, topology optimization currently faces some practical challenges that must be addressed, such as ensuring that structures do not have significant defects when converted to additive manufacturing models. To address this problem, we designed a 3D hierarchical fully convolutional network (FCN) to predict the precise position of the defective structures. Based on the prediction results, an effective repair strategy is adopted to repair the defective structure. A series of experiments is conducted to demonstrate the effectiveness of our approach. Compared to the 2D fully convolutional network and the rule-based detection method, our approach can accurately capture most defect structures and achieve 89.88% precision and 95.59% recall. Furthermore, we investigate the impact of different ways to increase the receptive field of our model, as well as the trade-off between different defect-repairing strategies. The results of the experiment demonstrate that the hierarchical structure, which increases the receptive field, can substantially improve the defect detection performance. To the best of our knowledge, this paper is the first to investigate 3D defect prediction and repair for topology optimization in conjunction with deep learning algorithms, providing practical tools and new perspectives for the subsequent development of topology optimization techniques.

Keywords

• Topology optimization
• Additive manufacturing
• Deep learning
• 3D semantic segmentation
• Defect detection