Multi-pass ultrasonic impact treatment(UIT)was applied to modify the microstructure and improve the mechanical and tribological characteristics at the near-surface region of commercially pure Ti(CP-Ti)specimens produc...Multi-pass ultrasonic impact treatment(UIT)was applied to modify the microstructure and improve the mechanical and tribological characteristics at the near-surface region of commercially pure Ti(CP-Ti)specimens produced by the laser powder bed fusion(L-PBF)method.UIT considerably refined the L-PBF process-related acicular martensites(α′-M)and produced a well-homogenized and dense surface microstructure,where the porosity content of 1-,3-,and 5-pass UITed samples was reduced by 43,60,and 67%,respectively.The UITed samples showed an enhancement in their near-surface mechanical properties up to a depth of about 300μm.The nanoindentation results for the 3-pass UITed sample revealed an increase of about 53,45,and 220%in its nanohardness,H/E_(r),and H_(3)/E_(r)^(2)indices,respectively.The stylus profilometry results showed that performing the UIT removed the L-PBF-related features/defects and offered a smooth surface.The roughness average(R_(a))and the skewness(R_(sk))of the 3-pass UITed sample were found to be lower than those of the L-PBFed sample by 95 and 223%,respectively.Applying the UIT also enhanced the material ratio,where the maximum load-bearing capacity(~100%)in as-L-PBFed(as-built)and 3-pass UITed samples was obtained at 60-and 10-µm depths,respectively.The tribological investigations showed that applying the UIT resulted in a significant reduction of wear rate and average coefficient of friction(COF)of CP-Ti.For instance,under the normal pressures of 0.05 and 0.2 MPa,the wear rate and COF of the 3-pass UITed sample were lower than those of the L-PBFed sample by 65 and 58%,and 20 and 17%,respectively.展开更多
This paper presents a new framework for object-based classification of high-resolution hyperspectral data.This multi-step framework is based on multi-resolution segmentation(MRS)and Random Forest classifier(RFC)algori...This paper presents a new framework for object-based classification of high-resolution hyperspectral data.This multi-step framework is based on multi-resolution segmentation(MRS)and Random Forest classifier(RFC)algorithms.The first step is to determine of weights of the input features while using the object-based approach with MRS to processing such images.Given the high number of input features,an automatic method is needed for estimation of this parameter.Moreover,we used the Variable Importance(VI),one of the outputs of the RFC,to determine the importance of each image band.Then,based on this parameter and other required parameters,the image is segmented into some homogenous regions.Finally,the RFC is carried out based on the characteristics of segments for converting them into meaningful objects.The proposed method,as well as,the conventional pixel-based RFC and Support Vector Machine(SVM)method was applied to three different hyperspectral data-sets with various spectral and spatial characteristics.These data were acquired by the HyMap,the Airborne Prism Experiment(APEX),and the Compact Airborne Spectrographic Imager(CASI)hyperspectral sensors.The experimental results show that the proposed method is more consistent for land cover mapping in various areas.The overall classification accuracy(OA),obtained by the proposed method was 95.48,86.57,and 84.29%for the HyMap,the APEX,and the CASI datasets,respectively.Moreover,this method showed better efficiency in comparison to the spectralbased classifications because the OAs of the proposed method was 5.67 and 3.75%higher than the conventional RFC and SVM classifiers,respectively.展开更多
文摘Multi-pass ultrasonic impact treatment(UIT)was applied to modify the microstructure and improve the mechanical and tribological characteristics at the near-surface region of commercially pure Ti(CP-Ti)specimens produced by the laser powder bed fusion(L-PBF)method.UIT considerably refined the L-PBF process-related acicular martensites(α′-M)and produced a well-homogenized and dense surface microstructure,where the porosity content of 1-,3-,and 5-pass UITed samples was reduced by 43,60,and 67%,respectively.The UITed samples showed an enhancement in their near-surface mechanical properties up to a depth of about 300μm.The nanoindentation results for the 3-pass UITed sample revealed an increase of about 53,45,and 220%in its nanohardness,H/E_(r),and H_(3)/E_(r)^(2)indices,respectively.The stylus profilometry results showed that performing the UIT removed the L-PBF-related features/defects and offered a smooth surface.The roughness average(R_(a))and the skewness(R_(sk))of the 3-pass UITed sample were found to be lower than those of the L-PBFed sample by 95 and 223%,respectively.Applying the UIT also enhanced the material ratio,where the maximum load-bearing capacity(~100%)in as-L-PBFed(as-built)and 3-pass UITed samples was obtained at 60-and 10-µm depths,respectively.The tribological investigations showed that applying the UIT resulted in a significant reduction of wear rate and average coefficient of friction(COF)of CP-Ti.For instance,under the normal pressures of 0.05 and 0.2 MPa,the wear rate and COF of the 3-pass UITed sample were lower than those of the L-PBFed sample by 65 and 58%,and 20 and 17%,respectively.
文摘This paper presents a new framework for object-based classification of high-resolution hyperspectral data.This multi-step framework is based on multi-resolution segmentation(MRS)and Random Forest classifier(RFC)algorithms.The first step is to determine of weights of the input features while using the object-based approach with MRS to processing such images.Given the high number of input features,an automatic method is needed for estimation of this parameter.Moreover,we used the Variable Importance(VI),one of the outputs of the RFC,to determine the importance of each image band.Then,based on this parameter and other required parameters,the image is segmented into some homogenous regions.Finally,the RFC is carried out based on the characteristics of segments for converting them into meaningful objects.The proposed method,as well as,the conventional pixel-based RFC and Support Vector Machine(SVM)method was applied to three different hyperspectral data-sets with various spectral and spatial characteristics.These data were acquired by the HyMap,the Airborne Prism Experiment(APEX),and the Compact Airborne Spectrographic Imager(CASI)hyperspectral sensors.The experimental results show that the proposed method is more consistent for land cover mapping in various areas.The overall classification accuracy(OA),obtained by the proposed method was 95.48,86.57,and 84.29%for the HyMap,the APEX,and the CASI datasets,respectively.Moreover,this method showed better efficiency in comparison to the spectralbased classifications because the OAs of the proposed method was 5.67 and 3.75%higher than the conventional RFC and SVM classifiers,respectively.
