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...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.展开更多
Image corner detection plays an important role in image analysis and recognition. This paper presents a novel corner detector based on the growing neural gas （GNG） network and this proposed detector is called GNG-C....Image corner detection plays an important role in image analysis and recognition. This paper presents a novel corner detector based on the growing neural gas （GNG） network and this proposed detector is called GNG-C. With the GNG network,image topology information can be learned and used to implement corner detection. The GNG-C approach can be described as consisting of the following steps. First,a canny edge detector is used to acquire the contour information of the input image. This edge information is used to train a modified GNG network. A special stopping criterion is defined to terminate network learning. Second,vectors formed between network nodes and their neighbors are used to measure curvatures. Third,dynamic regions of support （ROS） are determined based on these curvatures. These ROS are used to suppress curvature noise. The curvature values of the nodes are then analyzed to estimate the candidate corners. Finally,the candidates are distilled by a non-maxima suppression process to obtain the final set of corners. Experiments on both artificial and real images show that the proposed corner detection method is feasible and effective.展开更多
拓扑优化变密度法得到的优化结构边界呈离散阶梯分布,边缘不清晰且可制造性较差,现有重构算法均基于非参数轮廓的提取,在可适用性及响应精度方面有待改进。针对此类不足提出一种拓扑结构优化方法,利用Canny检测算法提取拓扑优化结构边缘...拓扑优化变密度法得到的优化结构边界呈离散阶梯分布,边缘不清晰且可制造性较差,现有重构算法均基于非参数轮廓的提取,在可适用性及响应精度方面有待改进。针对此类不足提出一种拓扑结构优化方法,利用Canny检测算法提取拓扑优化结构边缘,基于DBSCAN(Density-Based Spatial Clustering of Applications with Noise)算法对其结构模型进行聚类分析,得到密度划分的多簇特征点集,利用凸包Graham Scan算法逆序扫描数据点集,连接自然极限边界形成整体轮廓,为考虑可制造性条件下拓扑设计提供一种优化方法。通过典型算例验证了设计方案的可行性,整体结构边缘清晰,且对拓扑构型可能存在的无实义内部空隙有修复作用。结果表明:经处理后的优化构型结构响应度误差较小,满足设计要求。展开更多
基金supported by the National Natural Science Foundation of China(61973293)the Central Guidance on Local Science and Technology Development Fund of Fujian Province,China(2021L3047 and 2020L3028)+1 种基金the Fujian Provincial Science and Technology Plan Project,China(2021Y0048 and 2021j01388)the Open Project Program of Fujian Key Laboratory of Special Intelligent Equipment Measurement and Control,Fujian Special Equipment Inspection and Research Institute,China(FJIES2023KF02).
文摘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.
基金supported by the National Natural Science Foundation of China (60972112)
文摘Image corner detection plays an important role in image analysis and recognition. This paper presents a novel corner detector based on the growing neural gas （GNG） network and this proposed detector is called GNG-C. With the GNG network,image topology information can be learned and used to implement corner detection. The GNG-C approach can be described as consisting of the following steps. First,a canny edge detector is used to acquire the contour information of the input image. This edge information is used to train a modified GNG network. A special stopping criterion is defined to terminate network learning. Second,vectors formed between network nodes and their neighbors are used to measure curvatures. Third,dynamic regions of support （ROS） are determined based on these curvatures. These ROS are used to suppress curvature noise. The curvature values of the nodes are then analyzed to estimate the candidate corners. Finally,the candidates are distilled by a non-maxima suppression process to obtain the final set of corners. Experiments on both artificial and real images show that the proposed corner detection method is feasible and effective.
文摘拓扑优化变密度法得到的优化结构边界呈离散阶梯分布,边缘不清晰且可制造性较差,现有重构算法均基于非参数轮廓的提取,在可适用性及响应精度方面有待改进。针对此类不足提出一种拓扑结构优化方法,利用Canny检测算法提取拓扑优化结构边缘,基于DBSCAN(Density-Based Spatial Clustering of Applications with Noise)算法对其结构模型进行聚类分析,得到密度划分的多簇特征点集,利用凸包Graham Scan算法逆序扫描数据点集,连接自然极限边界形成整体轮廓,为考虑可制造性条件下拓扑设计提供一种优化方法。通过典型算例验证了设计方案的可行性,整体结构边缘清晰,且对拓扑构型可能存在的无实义内部空隙有修复作用。结果表明:经处理后的优化构型结构响应度误差较小,满足设计要求。
