Triply periodic minimal surface(TPMS)structures are widely utilized in engineering and biomedical fields owing to their superior mechanical and functional properties.However,limited by the current additive manufacturi...Triply periodic minimal surface(TPMS)structures are widely utilized in engineering and biomedical fields owing to their superior mechanical and functional properties.However,limited by the current additive manufacturing(AM)techniques,insufficient wall thickness often leads to poor forming quality or even printing failure.Therefore,accurate prediction of wall thickness parameters during the design stage is essential.This study proposes a prediction approach for the wall thickness parameters of TPMS models by integrating a Convolutional Neural Network–Support Vector Regression(CNN-SVM)framework with Multiple Linear Regression(MLR).A total of 152 TPMS models were randomly generated,resulting in 912 sets of sample data.Voxel-based sampling and rasterization preprocessing were employed to prepare the data for model input.The CNN-SVM model was developed using TPMS type,lattice filling type,volume fraction,and cell length as input features,with wall thickness as the output variable.Subsequently,the MLR method was applied to quantify the influence weights of these parameters.Experimental results demonstrate that the CNN-SVM model achieves a mean squared error(MSE)of 0.0011 and a coefficient of determination(R2)of 0.92.Approximately 86.9%of the test samples exhibited prediction errors within 20%,representing performance improvements of 15.8%,10.6%,and 18.5%over traditional MLR,CNN,and SVM models,respectively.The MLR analysis further indicates that the Sheet filling type exerts the most significant positive effect on wall thickness(0.45729),whereas theDiamond TPMS structure shows the most prominent negative impact(−0.23494).The proposed hybrid model provides an effective and reliable strategy for predicting wall thickness parameters in TPMS-based additive manufacturing designs.展开更多
Industrial surface inspection is crucial for the manufacture of high-end equipment across industries,with precise image acquisition being fundamental.Existing imaging systems often lack flexibility,they are restricted...Industrial surface inspection is crucial for the manufacture of high-end equipment across industries,with precise image acquisition being fundamental.Existing imaging systems often lack flexibility,they are restricted to specific objects,and face challenges in industrial structures without standardized computer-aided design(CAD)models or with complex surfaces.Inspired by human-like multidimensional observation,this study developed a universal image acquisition system based on measured point clouds,offering strong adaptability and robustness in complex industrial settings.The system is divided into three layers:the physical layer responsible for hardware integration,the interaction layer that facilitates bidirectional data exchange with the control layer,and the control layer integrating a new paradigm of multiple intelligent algorithms.The physical layer incorporates 2D and 3D cameras,turntables and industrial robots,enhancing the flexibility and compatibility of imaging.The interaction layer manages bidirectional information transmission and data exchange,offering a visualized area to enhance the user interaction experience.The control layer consists of point cloud preprocessing,primitive segmentation,viewpoint generation and pose estimation algorithms,using point cloud-based viewpoint generation and trajectory planning for high-precision image acquisition applicable to complex surface inspections across scenarios and structures.The system’s utility is demonstrated through a software and hardware algorithm platform and an interactive interface.Experimental validation on curved surfaces of different configurations and sizes confirms its universal image acquisition advantages.This system promises to introduce a cost-effective,versatile solution for complex surfaces,driving adoption across diverse industrial scenarios.展开更多
基金funded by Sichuan Provincial Regional Innovation Cooperation Project Funding,grant number 2024YFHZ0073Funded by the Research Innovation Team Programof Sichuan University of Science and Chemical Technology,grant number SUSE652A015.
文摘Triply periodic minimal surface(TPMS)structures are widely utilized in engineering and biomedical fields owing to their superior mechanical and functional properties.However,limited by the current additive manufacturing(AM)techniques,insufficient wall thickness often leads to poor forming quality or even printing failure.Therefore,accurate prediction of wall thickness parameters during the design stage is essential.This study proposes a prediction approach for the wall thickness parameters of TPMS models by integrating a Convolutional Neural Network–Support Vector Regression(CNN-SVM)framework with Multiple Linear Regression(MLR).A total of 152 TPMS models were randomly generated,resulting in 912 sets of sample data.Voxel-based sampling and rasterization preprocessing were employed to prepare the data for model input.The CNN-SVM model was developed using TPMS type,lattice filling type,volume fraction,and cell length as input features,with wall thickness as the output variable.Subsequently,the MLR method was applied to quantify the influence weights of these parameters.Experimental results demonstrate that the CNN-SVM model achieves a mean squared error(MSE)of 0.0011 and a coefficient of determination(R2)of 0.92.Approximately 86.9%of the test samples exhibited prediction errors within 20%,representing performance improvements of 15.8%,10.6%,and 18.5%over traditional MLR,CNN,and SVM models,respectively.The MLR analysis further indicates that the Sheet filling type exerts the most significant positive effect on wall thickness(0.45729),whereas theDiamond TPMS structure shows the most prominent negative impact(−0.23494).The proposed hybrid model provides an effective and reliable strategy for predicting wall thickness parameters in TPMS-based additive manufacturing designs.
基金supported by the National Natural Science Foundation of China(Nos.62303457 and U21A20482)Beijing Municipal Natural Science Foundation,China(No.4252053)Project funded by Beijing Zhongke Huiling Robot Technology Co.,Ltd,China(No.E5D11703).
文摘Industrial surface inspection is crucial for the manufacture of high-end equipment across industries,with precise image acquisition being fundamental.Existing imaging systems often lack flexibility,they are restricted to specific objects,and face challenges in industrial structures without standardized computer-aided design(CAD)models or with complex surfaces.Inspired by human-like multidimensional observation,this study developed a universal image acquisition system based on measured point clouds,offering strong adaptability and robustness in complex industrial settings.The system is divided into three layers:the physical layer responsible for hardware integration,the interaction layer that facilitates bidirectional data exchange with the control layer,and the control layer integrating a new paradigm of multiple intelligent algorithms.The physical layer incorporates 2D and 3D cameras,turntables and industrial robots,enhancing the flexibility and compatibility of imaging.The interaction layer manages bidirectional information transmission and data exchange,offering a visualized area to enhance the user interaction experience.The control layer consists of point cloud preprocessing,primitive segmentation,viewpoint generation and pose estimation algorithms,using point cloud-based viewpoint generation and trajectory planning for high-precision image acquisition applicable to complex surface inspections across scenarios and structures.The system’s utility is demonstrated through a software and hardware algorithm platform and an interactive interface.Experimental validation on curved surfaces of different configurations and sizes confirms its universal image acquisition advantages.This system promises to introduce a cost-effective,versatile solution for complex surfaces,driving adoption across diverse industrial scenarios.
