In order to accelerate the research on the property optimization of titanium alloy based on high-throughput methods,it is necessary to reveal the relationship between hardness and other mechanical properties which is ...In order to accelerate the research on the property optimization of titanium alloy based on high-throughput methods,it is necessary to reveal the relationship between hardness and other mechanical properties which is still unclear.In this work,taking Ti20C alloy as research object,almost all the microstructure of dual-phase titanium alloys were covered by traversing over 100 heat treatment schemes.Then,massive experiments including microstructure characterization and performance test were conducted,obtaining 51,590 pieces of microstructure data and 3591 pieces of mechanical property data.Subsequently,based on large-scale data-driven technology,the quantitative mapping relationship between hardness and other mechanical properties was deeply discussed.The results of random forest models showed that the correlation between hardness(H)and Charpy impact energy(A_(k))(or elongation,A)was hardly dependent on the microstructure types,while the relationship between H and tensile strength(R_(m))(or yield strength,R_(p0.2))was highly dependent on microstructure types.Specifically,combined with statistical analysis,it was found that the relationship between H and Ak(or A)were negatively linear.Interestingly,the relationship between H and strength was positively linear for equiaxed microstructure,and strength was linked to d^(−1/2)(d,equivalent circle diameter)ofα-grains in the form of classical Hall–Petch formula;but for other microstructures,the relationships were quadratic.Furthermore,the above rules were nearly the same in the rolling direction and transverse direction.Finally,a"four-quadrant partition map"between H and R_(p0.2)/R_(m) was established as a versatile material-screening tool,which can provide guidance for on-demand selection of titanium alloys.展开更多
The finite element(FE)-based simulation of welding characteristics was carried out to explore the relationship among welding assembly properties for the parallel T-shaped thin-walled parts of an antenna structure.The ...The finite element(FE)-based simulation of welding characteristics was carried out to explore the relationship among welding assembly properties for the parallel T-shaped thin-walled parts of an antenna structure.The effects of welding direction,clamping,fixture release time,fixed constraints,and welding sequences on these properties were analyzed,and the mapping relationship among welding characteristics was thoroughly examined.Different machine learning algorithms,including the generalized regression neural network(GRNN),wavelet neural network(WNN),and fuzzy neural network(FNN),are used to predict the multiple welding properties of thin-walled parts to mirror their variation trend and verify the correctness of the mapping relationship.Compared with those from GRNN and WNN,the maximum mean relative errors for the predicted values of deformation,temperature,and residual stress with FNN were less than 4.8%,1.4%,and 4.4%,respectively.These results indicate that FNN generated the best predicted welding characteristics.Analysis under various welding conditions also shows a mapping relationship among welding deformation,temperature,and residual stress over a period of time.This finding further provides a paramount basis for the control of welding assembly errors of an antenna structure in the future.展开更多
Conventional soil maps contain valuable knowledge on soil–environment relationships.Such knowledge can be extracted for use when updating conventional soil maps with improved environmental data.Existing methods take ...Conventional soil maps contain valuable knowledge on soil–environment relationships.Such knowledge can be extracted for use when updating conventional soil maps with improved environmental data.Existing methods take all polygons of the same map unit on a map as a whole to extract the soil–environment relationship.Such approach ignores the difference in the environmental conditions represented by individual soil polygons of the same map unit.This paper proposes a method of mining soil–environment relationships from individual soil polygons to update conventional soil maps.The proposed method consists of three major steps.Firstly,the soil–environment relationships represented by each individual polygon on a conventional soil map are extracted in the form of frequency distribution curves for the involved environmental covariates.Secondly,for each environmental covariate,these frequency distribution curves from individual polygons of the same soil map unit are synthesized to form the overall soil–environment relationship for that soil map unit across the mapped area.And lastly,the extracted soil–environment relationships are applied to updating the conventional soil map with new,improved environmental data by adopting a soil land inference model(SoLIM)framework.This study applied the proposed method to updating a conventional soil map of the Raffelson watershed in La Crosse County,Wisconsin,United States.The result from the proposed method was compared with that from the previous method of taking all polygons within the same soil map unit on a map as a whole.Evaluation results with independent soil samples showed that the proposed method exhibited better performance and produced higher accuracy.展开更多
基金This work was financially supported by the National Natural Science Foundation of China(Nos.51901102 and 52101005).
文摘In order to accelerate the research on the property optimization of titanium alloy based on high-throughput methods,it is necessary to reveal the relationship between hardness and other mechanical properties which is still unclear.In this work,taking Ti20C alloy as research object,almost all the microstructure of dual-phase titanium alloys were covered by traversing over 100 heat treatment schemes.Then,massive experiments including microstructure characterization and performance test were conducted,obtaining 51,590 pieces of microstructure data and 3591 pieces of mechanical property data.Subsequently,based on large-scale data-driven technology,the quantitative mapping relationship between hardness and other mechanical properties was deeply discussed.The results of random forest models showed that the correlation between hardness(H)and Charpy impact energy(A_(k))(or elongation,A)was hardly dependent on the microstructure types,while the relationship between H and tensile strength(R_(m))(or yield strength,R_(p0.2))was highly dependent on microstructure types.Specifically,combined with statistical analysis,it was found that the relationship between H and Ak(or A)were negatively linear.Interestingly,the relationship between H and strength was positively linear for equiaxed microstructure,and strength was linked to d^(−1/2)(d,equivalent circle diameter)ofα-grains in the form of classical Hall–Petch formula;but for other microstructures,the relationships were quadratic.Furthermore,the above rules were nearly the same in the rolling direction and transverse direction.Finally,a"four-quadrant partition map"between H and R_(p0.2)/R_(m) was established as a versatile material-screening tool,which can provide guidance for on-demand selection of titanium alloys.
基金The Natural Science Foundation of Jiangsu Province,China(No.BK20200470)China Postdoctoral Science Foundation(No.2021M691595)Innovation and Entrepreneurship Plan Talent Program of Jiangsu Province(No.AD99002).
文摘The finite element(FE)-based simulation of welding characteristics was carried out to explore the relationship among welding assembly properties for the parallel T-shaped thin-walled parts of an antenna structure.The effects of welding direction,clamping,fixture release time,fixed constraints,and welding sequences on these properties were analyzed,and the mapping relationship among welding characteristics was thoroughly examined.Different machine learning algorithms,including the generalized regression neural network(GRNN),wavelet neural network(WNN),and fuzzy neural network(FNN),are used to predict the multiple welding properties of thin-walled parts to mirror their variation trend and verify the correctness of the mapping relationship.Compared with those from GRNN and WNN,the maximum mean relative errors for the predicted values of deformation,temperature,and residual stress with FNN were less than 4.8%,1.4%,and 4.4%,respectively.These results indicate that FNN generated the best predicted welding characteristics.Analysis under various welding conditions also shows a mapping relationship among welding deformation,temperature,and residual stress over a period of time.This finding further provides a paramount basis for the control of welding assembly errors of an antenna structure in the future.
基金supported by the National Natural Science Foundation of China (41431177 and 41422109)the Innovation Project of State Key Laboratory of Resources and Environmental Information System of China (O88RA20CYA)the Outstanding Innovation Team in Colleges and Universities in Jiangsu Province, China
文摘Conventional soil maps contain valuable knowledge on soil–environment relationships.Such knowledge can be extracted for use when updating conventional soil maps with improved environmental data.Existing methods take all polygons of the same map unit on a map as a whole to extract the soil–environment relationship.Such approach ignores the difference in the environmental conditions represented by individual soil polygons of the same map unit.This paper proposes a method of mining soil–environment relationships from individual soil polygons to update conventional soil maps.The proposed method consists of three major steps.Firstly,the soil–environment relationships represented by each individual polygon on a conventional soil map are extracted in the form of frequency distribution curves for the involved environmental covariates.Secondly,for each environmental covariate,these frequency distribution curves from individual polygons of the same soil map unit are synthesized to form the overall soil–environment relationship for that soil map unit across the mapped area.And lastly,the extracted soil–environment relationships are applied to updating the conventional soil map with new,improved environmental data by adopting a soil land inference model(SoLIM)framework.This study applied the proposed method to updating a conventional soil map of the Raffelson watershed in La Crosse County,Wisconsin,United States.The result from the proposed method was compared with that from the previous method of taking all polygons within the same soil map unit on a map as a whole.Evaluation results with independent soil samples showed that the proposed method exhibited better performance and produced higher accuracy.
