The microstructural processes occurring in metals and alloys during hot deformation are： DRX （dynamic recrystallization）, superplastic deformation, dynamic recovery, wedge cracking, void formation, inter-crystallin...The microstructural processes occurring in metals and alloys during hot deformation are： DRX （dynamic recrystallization）, superplastic deformation, dynamic recovery, wedge cracking, void formation, inter-crystalline cracking, prior particle boundary （FFB） cracking, and flow instability processes. Deformation characteristics of materials are interpreted as follows： in the low temperature （T≤0.25 of melting temperature） and high strain rate regime of 10 to 100 s-1, void formation occurs at hard particles and leads to ductile fracture. Many researchers used the currently defined statistical approaches to characterize images and extract useful information from the captured images. For more suitable of specific tasks, some researchers are introducing new texture features. HOS （higher-order statistics） estimate properties of three or more pixels occurring at specific locations relative to each other. GLRLMs （gray level run-length matrices） are popular method of HOS to extract texture features. This paper deals with texture features of GLRLM to predict strain rate values for Aluminum/Silicon Carbide.展开更多
文摘The microstructural processes occurring in metals and alloys during hot deformation are： DRX （dynamic recrystallization）, superplastic deformation, dynamic recovery, wedge cracking, void formation, inter-crystalline cracking, prior particle boundary （FFB） cracking, and flow instability processes. Deformation characteristics of materials are interpreted as follows： in the low temperature （T≤0.25 of melting temperature） and high strain rate regime of 10 to 100 s-1, void formation occurs at hard particles and leads to ductile fracture. Many researchers used the currently defined statistical approaches to characterize images and extract useful information from the captured images. For more suitable of specific tasks, some researchers are introducing new texture features. HOS （higher-order statistics） estimate properties of three or more pixels occurring at specific locations relative to each other. GLRLMs （gray level run-length matrices） are popular method of HOS to extract texture features. This paper deals with texture features of GLRLM to predict strain rate values for Aluminum/Silicon Carbide.
