Helium bubbles,which are typical radiation microstructures observed in metals or alloys,are usually investigated using transmission electron microscopy(TEM).However,the investigation requires human inputs to locate an...Helium bubbles,which are typical radiation microstructures observed in metals or alloys,are usually investigated using transmission electron microscopy(TEM).However,the investigation requires human inputs to locate and mark the bubbles in the acquired TEM images,rendering this task laborious and prone to error.In this paper,a machine learning method capable of automatically identifying and analyzing TEM images of helium bubbles is proposed,thereby improving the efficiency and reliability of the investigation.In the proposed technique,helium bubble clusters are first determined via the densitybased spatial clustering of applications with noise algorithm after removing the background and noise pixels.For each helium bubble cluster,the number of helium bubbles is determined based on the cluster size depending on the specific image resolution.Finally,the helium bubble clusters are analyzed using a Gaussian mixture model,yielding the location and size information on the helium bubbles.In contrast to other approaches that require training using numerous annotated images to establish an accurate classifier,the parameters used in the established model are determined using a small number of TEM images.The results of the model formulated according to the proposed approach achieved a higher F1 score validated through some helium bubble images manually marked.Furthermore,the established model can identify bubble-like objects that humans cannot facilely identify.This computationally efficient method achieves object recognition for material structure identification that may be advantageous to scientific work.展开更多
In the present study,samples of a titanium carbide nanoparticle-reinforced nickel alloy(Ni-TiC_(NP)composite)were irradiated with 1 MeV He ions at 700°C.The evolution of He bubbles and nanohardness was characteri...In the present study,samples of a titanium carbide nanoparticle-reinforced nickel alloy(Ni-TiC_(NP)composite)were irradiated with 1 MeV He ions at 700°C.The evolution of He bubbles and nanohardness was characterized using transmission electron microscopy(TEM)and nanoindentation,respectively.TEM images showed that the size and number density of He bubbles in the grains were affected by the He ion fluence.The number density first increased significantly and then decreased with increasing ion dose,while the size exhibited an inverse trend.Moreover,the swelling induced by He bubbles continuously increased with increasing ion dose.He bubbles also formed in the grain boundaries,interior of the TiC nanoparticles,and interfaces between the TiC nanoparticles and Ni matrix.Nanoindentation measurements indicated a decrease in nanohardness after irradiation,which is attributed to the disappearance of intrinsic dislocation lines caused by He ion irradiation.展开更多
基金supported by the National Natural Science Foundation of China(Nos.12005128,81830052)Construction Project of Shanghai Key Laboratory of Molecular Imaging(No.18DZ2260400)and Shanghai Municipal Education Commission(Class II Plateau Disciplinary Construction Program of Medical Technology of SUMHS,2018–2020).
文摘Helium bubbles,which are typical radiation microstructures observed in metals or alloys,are usually investigated using transmission electron microscopy(TEM).However,the investigation requires human inputs to locate and mark the bubbles in the acquired TEM images,rendering this task laborious and prone to error.In this paper,a machine learning method capable of automatically identifying and analyzing TEM images of helium bubbles is proposed,thereby improving the efficiency and reliability of the investigation.In the proposed technique,helium bubble clusters are first determined via the densitybased spatial clustering of applications with noise algorithm after removing the background and noise pixels.For each helium bubble cluster,the number of helium bubbles is determined based on the cluster size depending on the specific image resolution.Finally,the helium bubble clusters are analyzed using a Gaussian mixture model,yielding the location and size information on the helium bubbles.In contrast to other approaches that require training using numerous annotated images to establish an accurate classifier,the parameters used in the established model are determined using a small number of TEM images.The results of the model formulated according to the proposed approach achieved a higher F1 score validated through some helium bubble images manually marked.Furthermore,the established model can identify bubble-like objects that humans cannot facilely identify.This computationally efficient method achieves object recognition for material structure identification that may be advantageous to scientific work.
基金suported by the National Natural Science Foundation of China(Nos.11705264,11975304,12022515,and 12175323)。
文摘In the present study,samples of a titanium carbide nanoparticle-reinforced nickel alloy(Ni-TiC_(NP)composite)were irradiated with 1 MeV He ions at 700°C.The evolution of He bubbles and nanohardness was characterized using transmission electron microscopy(TEM)and nanoindentation,respectively.TEM images showed that the size and number density of He bubbles in the grains were affected by the He ion fluence.The number density first increased significantly and then decreased with increasing ion dose,while the size exhibited an inverse trend.Moreover,the swelling induced by He bubbles continuously increased with increasing ion dose.He bubbles also formed in the grain boundaries,interior of the TiC nanoparticles,and interfaces between the TiC nanoparticles and Ni matrix.Nanoindentation measurements indicated a decrease in nanohardness after irradiation,which is attributed to the disappearance of intrinsic dislocation lines caused by He ion irradiation.
