This study systematically investigates the hot deformation behavior and microstructural evolution of CoNiV medium-entropy alloy(MEA)in the temperature range of 950-1100℃ and strain rates of 0.001-1 s^(-1).The Arrheni...This study systematically investigates the hot deformation behavior and microstructural evolution of CoNiV medium-entropy alloy(MEA)in the temperature range of 950-1100℃ and strain rates of 0.001-1 s^(-1).The Arrhenius model and machine learning model were developed to forecast flow stresses at various conditions.The predictive capability of both models was assessed using the coefficients of determination(R^(2)),average absolute relative error(AARE),and root mean square error(RMSE).The findings show that the osprey optimization algorithm convolutional neural network(OOA-CNN)model outperforms the Arrhenius model,achieving a high R^(2) value of 0.99959 and lower AARE and RMSE values.The flow stress that the OOA-CNN model predicted was used to generate power dissipation maps and instability maps under different strains.Finally,combining the processing map and microstructure characterization,the ideal processing domain was identified as 1100℃ at strain rates of 0.01-0.1 s^(-1).This study provided key insights into optimizing the hot working process of CoNiV MEA.展开更多
High/medium entropy alloys(H/MEAs)have shown unique strengthening behavior and mechanical prop-erties because of the presence of massive local chemical orderings.Nevertheless,dynamic interactions between chemical shor...High/medium entropy alloys(H/MEAs)have shown unique strengthening behavior and mechanical prop-erties because of the presence of massive local chemical orderings.Nevertheless,dynamic interactions between chemical short-range orders(CSROs)and dislocations,and the underlying atomic strengthening mechanism remain elusive.In this work,we first developed a novel machine learning-embedded atom method(ML-EAM)potential of the CoNiV system,trained on a comprehensive first-principles dataset,which enables accurate and efficient modeling of CSRO formation and dislocation dynamics.Then,we in-vestigated the strengthening mechanisms of CSROs in CoNiV MEA through machine learning-augmented molecular dynamics(MD)simulations.Hybrid MD/Monte Carlo simulations reveal that CSRO domains possess an L1_(2)(NiCo)_(3) V structure,whose size increases with lowering annealing temperatures.These domains significantly enhance strength by impeding dislocation motion through complex energy path-ways,increasing depinning forces,and reducing mobility.Moreover,the MD simulations combined with theoretical analysis elucidate the competition between CSRO-assisted strengthening(via antiphase bound-ary formation)and solid solution weakening(via reduced atomic misfit volume).Phonon-drag effects are also amplified by CSROs,further resisting dislocation glide.Our results demonstrate that L1_(2)-CSROs strengthen CoNiV MEA primarily through antiphase boundary and phonon-drag contributions,providing new insights for designing high-performance multi-principal-element alloys via tailoring CSROs.展开更多
基金supported by the National Natural Science Foundation of China(NSFC)(Grant No.51901078)the Central Guidance for Local Scientific and Technological Development Funding Project(Grant No.236Z1003G)+3 种基金the Science and Technology Plan Project of Tangshan City(Grant No.24130207C)the Natural Science Foundation of Hebei Province(Grant No.E2022209070)the High-level Talent Project of Hebei(Grant No.E2019100007)the Open Project Program of Key Laboratory of Ministry of Education for Modern Metallurgy Technology(Grant No.2024YJKF02).
文摘This study systematically investigates the hot deformation behavior and microstructural evolution of CoNiV medium-entropy alloy(MEA)in the temperature range of 950-1100℃ and strain rates of 0.001-1 s^(-1).The Arrhenius model and machine learning model were developed to forecast flow stresses at various conditions.The predictive capability of both models was assessed using the coefficients of determination(R^(2)),average absolute relative error(AARE),and root mean square error(RMSE).The findings show that the osprey optimization algorithm convolutional neural network(OOA-CNN)model outperforms the Arrhenius model,achieving a high R^(2) value of 0.99959 and lower AARE and RMSE values.The flow stress that the OOA-CNN model predicted was used to generate power dissipation maps and instability maps under different strains.Finally,combining the processing map and microstructure characterization,the ideal processing domain was identified as 1100℃ at strain rates of 0.01-0.1 s^(-1).This study provided key insights into optimizing the hot working process of CoNiV MEA.
基金financially supported by the National Natural Science Foundation of China(Nos.52071024,52271003 and 52101188)the National Science Fund for Distinguished Young Scholars of China(No.52225103)+2 种基金the Funds for Creative Research Groups of China(No.51921001)the Projects of International Cooperation and Exchanges NSFC(Nos.51961160729 and 52061135207)the Fundamental Research Fund for the Central Universities of China,and the State Key Laboratory for Advanced Metals and Materials.
文摘High/medium entropy alloys(H/MEAs)have shown unique strengthening behavior and mechanical prop-erties because of the presence of massive local chemical orderings.Nevertheless,dynamic interactions between chemical short-range orders(CSROs)and dislocations,and the underlying atomic strengthening mechanism remain elusive.In this work,we first developed a novel machine learning-embedded atom method(ML-EAM)potential of the CoNiV system,trained on a comprehensive first-principles dataset,which enables accurate and efficient modeling of CSRO formation and dislocation dynamics.Then,we in-vestigated the strengthening mechanisms of CSROs in CoNiV MEA through machine learning-augmented molecular dynamics(MD)simulations.Hybrid MD/Monte Carlo simulations reveal that CSRO domains possess an L1_(2)(NiCo)_(3) V structure,whose size increases with lowering annealing temperatures.These domains significantly enhance strength by impeding dislocation motion through complex energy path-ways,increasing depinning forces,and reducing mobility.Moreover,the MD simulations combined with theoretical analysis elucidate the competition between CSRO-assisted strengthening(via antiphase bound-ary formation)and solid solution weakening(via reduced atomic misfit volume).Phonon-drag effects are also amplified by CSROs,further resisting dislocation glide.Our results demonstrate that L1_(2)-CSROs strengthen CoNiV MEA primarily through antiphase boundary and phonon-drag contributions,providing new insights for designing high-performance multi-principal-element alloys via tailoring CSROs.
