Using a total of 297 segmented sections,we reconstructed the three-dimensional(3D) structure of pure iron and obtained the largest dataset of 16254 3D complete grains reported to date.The mean values of equivalent sph...Using a total of 297 segmented sections,we reconstructed the three-dimensional(3D) structure of pure iron and obtained the largest dataset of 16254 3D complete grains reported to date.The mean values of equivalent sphere radius and face number of pure iron were observed to be consistent with those of Monte Carlo simulated grains,phase-field simulated grains,Ti-alloy grains,and Ni-based super alloy grains.In this work,by finding a balance between automatic methods and manual refinement,we developed an interactive segmentation method to segment serial sections accurately in the reconstruction of the 3D microstructure;this approach can save time as well as substantially eliminate errors.The segmentation process comprises four operations:image preprocessing,breakpoint detection based on mathematical morphology analysis,optimized automatic connection of the breakpoints,and manual refinement by artificial evaluation.展开更多
Time-temperature-transformation(TTT)diagram plays a critical role in designing appropriate heat treatment process of steels by describing the relationship among holding time,temperature,and quantities of phase transfo...Time-temperature-transformation(TTT)diagram plays a critical role in designing appropriate heat treatment process of steels by describing the relationship among holding time,temperature,and quantities of phase transformation.Making predictions for TTT diagrams of new steel rapidly and accurately is therefore of much practical importance,especially for costly and time-consuming experimental determination.Here,TTT diagrams for carbon and low-alloy steels were predicted using machine learning methods.Five commonly used machine learning(ML)algorithms,backpropagation artificial neural network(BP network),LibSVM,k-nearest neighbor,Bagging,and Random tree,were adopted to select appropriate models for the prediction.The results illustrate that Bagging is the optimal model for the prediction of pearlite transformation and bainite transformation,and BP network is the optimal model for martensite transformation.Finally,the ML framework composed of Bagging and BP network models was applied to predict the entire TTT diagram.Additionally,the ML models show superior performance on the prediction of testing samples than the commercial software JMatPro.展开更多
基金financial support from the National Natural Science Foundation of China(Nos.51371030 and 51571020)the National Key Research and Development Program of China(No.2016YFB0700505)the National High Technology Research and Development Program of China(No.2015AA034201)
文摘Using a total of 297 segmented sections,we reconstructed the three-dimensional(3D) structure of pure iron and obtained the largest dataset of 16254 3D complete grains reported to date.The mean values of equivalent sphere radius and face number of pure iron were observed to be consistent with those of Monte Carlo simulated grains,phase-field simulated grains,Ti-alloy grains,and Ni-based super alloy grains.In this work,by finding a balance between automatic methods and manual refinement,we developed an interactive segmentation method to segment serial sections accurately in the reconstruction of the 3D microstructure;this approach can save time as well as substantially eliminate errors.The segmentation process comprises four operations:image preprocessing,breakpoint detection based on mathematical morphology analysis,optimized automatic connection of the breakpoints,and manual refinement by artificial evaluation.
基金the financial support from the National Natural Science Foundation of China(Grant No.92060102).
文摘Time-temperature-transformation(TTT)diagram plays a critical role in designing appropriate heat treatment process of steels by describing the relationship among holding time,temperature,and quantities of phase transformation.Making predictions for TTT diagrams of new steel rapidly and accurately is therefore of much practical importance,especially for costly and time-consuming experimental determination.Here,TTT diagrams for carbon and low-alloy steels were predicted using machine learning methods.Five commonly used machine learning(ML)algorithms,backpropagation artificial neural network(BP network),LibSVM,k-nearest neighbor,Bagging,and Random tree,were adopted to select appropriate models for the prediction.The results illustrate that Bagging is the optimal model for the prediction of pearlite transformation and bainite transformation,and BP network is the optimal model for martensite transformation.Finally,the ML framework composed of Bagging and BP network models was applied to predict the entire TTT diagram.Additionally,the ML models show superior performance on the prediction of testing samples than the commercial software JMatPro.
