X-ray Computed Tomography(XCT)enables non-destructive acquisition of the internal structure of materials,and image segmentation plays a crucial role in analyzing material XCT images.This paper proposes an image segmen...X-ray Computed Tomography(XCT)enables non-destructive acquisition of the internal structure of materials,and image segmentation plays a crucial role in analyzing material XCT images.This paper proposes an image segmentation method based on the Segment Anything model(SAM).We constructed a dataset of carbide in nickel-based single crystal superalloys XCT images and preprocessed the images using median filtering,histogram equalization,and gamma correction.Subsequently,SAM was fine-tuned to adapt to the task of material XCT image segmentation,resulting in Material-SAM.We compared the performance of threshold segmentation,SAM,U-Net model,and Material-SAM.Our method achieved 88.45%Class Pixel Accuracy(CPA)and 88.77%Dice Similarity Coefficient(DSC)on the test set,outperforming SAM by 5.25%and 8.81%,respectively,and achieving the highest evaluation.Material-SAM demonstrated lower input requirements compared to SAM,as it only required three reference points for completing the segmentation task,which is one-fifth of the requirement of SAM.Material-SAM exhibited promising results,highlighting its potential as a novel method for material XCT image segmentation.展开更多
Al-Si alloys are widely used in automotive casting components while microporosity has always been a detrimental defect that leads to property degradation.In this study,a coupled three-dimensional cellular automata(CA)...Al-Si alloys are widely used in automotive casting components while microporosity has always been a detrimental defect that leads to property degradation.In this study,a coupled three-dimensional cellular automata(CA)model has been used to predict the hydrogen porosity as functions of cooling rate and initial hydrogen concentration.By quantifying the pore characteristics,it has been found that the average equivalent pore diameter decreases from 40.43 to 23.98μm and the pore number density increases from 10.3 to 26.6 mm^(−3)as the cooling rate changes from 2.6 to 19.4℃/s at the initial hydrogen concentration of 0.25 mL/100 g.It is also notable that the pore size increases as the initial hydrogen concentration changes from 0.15 to 0.25 mL/100 g while the pore number remains stable.In addition,the linear regression between secondary dendrite arm spacing and the equivalent pore diameter has been studied for the first time,matching well with experiments.This work exhibits the application of CA model in future process optimization and robust condition design for advanced automotive parts made of Al-Si alloys.展开更多
基金This work was supported by the National Natural Science Foundation of China(Grant Number 52073030)National Natural Science Foundation of China-Guangxi Joint Fund(U20A20276).
文摘X-ray Computed Tomography(XCT)enables non-destructive acquisition of the internal structure of materials,and image segmentation plays a crucial role in analyzing material XCT images.This paper proposes an image segmentation method based on the Segment Anything model(SAM).We constructed a dataset of carbide in nickel-based single crystal superalloys XCT images and preprocessed the images using median filtering,histogram equalization,and gamma correction.Subsequently,SAM was fine-tuned to adapt to the task of material XCT image segmentation,resulting in Material-SAM.We compared the performance of threshold segmentation,SAM,U-Net model,and Material-SAM.Our method achieved 88.45%Class Pixel Accuracy(CPA)and 88.77%Dice Similarity Coefficient(DSC)on the test set,outperforming SAM by 5.25%and 8.81%,respectively,and achieving the highest evaluation.Material-SAM demonstrated lower input requirements compared to SAM,as it only required three reference points for completing the segmentation task,which is one-fifth of the requirement of SAM.Material-SAM exhibited promising results,highlighting its potential as a novel method for material XCT image segmentation.
基金supported by the National Natural Science Foundation of China(grant number 52073030)the National Natural Science Foundation of China-Guangxi Joint Fund(U20A20276).
文摘Al-Si alloys are widely used in automotive casting components while microporosity has always been a detrimental defect that leads to property degradation.In this study,a coupled three-dimensional cellular automata(CA)model has been used to predict the hydrogen porosity as functions of cooling rate and initial hydrogen concentration.By quantifying the pore characteristics,it has been found that the average equivalent pore diameter decreases from 40.43 to 23.98μm and the pore number density increases from 10.3 to 26.6 mm^(−3)as the cooling rate changes from 2.6 to 19.4℃/s at the initial hydrogen concentration of 0.25 mL/100 g.It is also notable that the pore size increases as the initial hydrogen concentration changes from 0.15 to 0.25 mL/100 g while the pore number remains stable.In addition,the linear regression between secondary dendrite arm spacing and the equivalent pore diameter has been studied for the first time,matching well with experiments.This work exhibits the application of CA model in future process optimization and robust condition design for advanced automotive parts made of Al-Si alloys.
