Automation and intelligence have become the primary trends in the design of investment casting processes.However,the design of gating and riser systems still lacks precise quantitative evaluation criteria.Numerical si...Automation and intelligence have become the primary trends in the design of investment casting processes.However,the design of gating and riser systems still lacks precise quantitative evaluation criteria.Numerical simulation plays a significant role in quantitatively evaluating current processes and making targeted improvements,but its limitations lie in the inability to dynamically reflect the formation outcomes of castings under varying process conditions,making real-time adjustments to gating and riser designs challenging.In this study,an automated design model for gating and riser systems based on integrated parametric 3D modeling-simulation framework is proposed,which enhances the flexibility and usability of evaluating the casting process by simulation.Firstly,geometric feature extraction technology is employed to obtain the geometric information of the target casting.Based on this information,an automated design framework for gating and riser systems is established,incorporating multiple structural parameters for real-time process control.Subsequently,the simulation results for various structural parameters are analyzed,and the influence of these parameters on casting formation is thoroughly investigated.Finally,the optimal design scheme is generated and validated through experimental verification.Simulation analysis and experimental results show that using a larger gate neck(24 mm in side length) and external risers promotes a more uniform temperature distribution and a more stable flow state,effectively eliminating shrinkage cavities and enhancing process yield by 15%.展开更多
Visual recognition of cardiac images is important for cardiac pathology diagnosis and treatment.Due to the limited availability of annotated datasets,traditional methods usually extract features directly from twodimen...Visual recognition of cardiac images is important for cardiac pathology diagnosis and treatment.Due to the limited availability of annotated datasets,traditional methods usually extract features directly from twodimensional slices of three-dimensional(3D)heart images,followed by pathological classification.This process may not ensure the overall anatomical consistency in 3D heart.A new method for classification of cardiac pathology is therefore proposed based on 3D parametric model reconstruction.First,3D heart models are reconstructed based on multiple 3D volumes of cardiac imaging data at the end-systole(ES)and end-diastole(ED)phases.Next,based on these reconstructed 3D hearts,3D parametric models are constructed through the statistical shape model(SSM),and then the heart data are augmented via the variation in shape parameters of one 3D parametric model with visual knowledge constraints.Finally,shape and motion features of 3D heart models across two phases are extracted to classify cardiac pathology.Comprehensive experiments on the automated cardiac diagnosis challenge(ACDC)dataset of the Statistical Atlases and Computational Modelling of the Heart(STACOM)workshop confirm the superior performance and efficiency of this proposed approach.展开更多
基金financially supported by the National Key Research and Development Program of China (2022YFB3706802)。
文摘Automation and intelligence have become the primary trends in the design of investment casting processes.However,the design of gating and riser systems still lacks precise quantitative evaluation criteria.Numerical simulation plays a significant role in quantitatively evaluating current processes and making targeted improvements,but its limitations lie in the inability to dynamically reflect the formation outcomes of castings under varying process conditions,making real-time adjustments to gating and riser designs challenging.In this study,an automated design model for gating and riser systems based on integrated parametric 3D modeling-simulation framework is proposed,which enhances the flexibility and usability of evaluating the casting process by simulation.Firstly,geometric feature extraction technology is employed to obtain the geometric information of the target casting.Based on this information,an automated design framework for gating and riser systems is established,incorporating multiple structural parameters for real-time process control.Subsequently,the simulation results for various structural parameters are analyzed,and the influence of these parameters on casting formation is thoroughly investigated.Finally,the optimal design scheme is generated and validated through experimental verification.Simulation analysis and experimental results show that using a larger gate neck(24 mm in side length) and external risers promotes a more uniform temperature distribution and a more stable flow state,effectively eliminating shrinkage cavities and enhancing process yield by 15%.
基金Project supported by the National Natural Science Foundation of China(Nos.72091511,62172047,and 61802020)。
文摘Visual recognition of cardiac images is important for cardiac pathology diagnosis and treatment.Due to the limited availability of annotated datasets,traditional methods usually extract features directly from twodimensional slices of three-dimensional(3D)heart images,followed by pathological classification.This process may not ensure the overall anatomical consistency in 3D heart.A new method for classification of cardiac pathology is therefore proposed based on 3D parametric model reconstruction.First,3D heart models are reconstructed based on multiple 3D volumes of cardiac imaging data at the end-systole(ES)and end-diastole(ED)phases.Next,based on these reconstructed 3D hearts,3D parametric models are constructed through the statistical shape model(SSM),and then the heart data are augmented via the variation in shape parameters of one 3D parametric model with visual knowledge constraints.Finally,shape and motion features of 3D heart models across two phases are extracted to classify cardiac pathology.Comprehensive experiments on the automated cardiac diagnosis challenge(ACDC)dataset of the Statistical Atlases and Computational Modelling of the Heart(STACOM)workshop confirm the superior performance and efficiency of this proposed approach.
