Metastable austenite plays a critical role in achieving improved combinations of high strength and high ductility/toughness in the design of advanced high-strength steels(AHSS). The thermal stability of metastable aus...Metastable austenite plays a critical role in achieving improved combinations of high strength and high ductility/toughness in the design of advanced high-strength steels(AHSS). The thermal stability of metastable austenite determines the transformation characteristics of AHSS and thus primarily determines the microstructure evolution during complex processes, e.g., the quenching and partitioning process, to achieve the desirable microstructure. This study provides a review of the thermal stability of austenite and its influence on martensitic transformation from both experimental and theoretical modeling perspectives. From the experimental perspective, factors affecting the thermal stability are analyzed,the relative sensitivities are compared, and their corresponding mechanisms are discussed. From the theoretical modeling perspective, the most representative kinetic models that describe athermal and isothermal martensitic transformation are reviewed. The advantages, shortcomings, and applicability of each model are discussed. The systematic review of both experimental and theoretical aspects reveals critical factors in tailoring the stability of metastable austenite and, therefore, provides guidance for the design of advanced steels.展开更多
The effect of chemical composition and processing parameters on the formation of acicular ferrite and/or bainite has been investigated.In particular,this paper deals with the influence that N through its combination w...The effect of chemical composition and processing parameters on the formation of acicular ferrite and/or bainite has been investigated.In particular,this paper deals with the influence that N through its combination with V,as V(C,N) precipitates,has on the decomposition of austenite.Likewise,the intragranular nucleation potency of V(C,N) precipitates is analyzed through the continuous cooling transformation diagrams (CCT) of two C-Mn-V steels with different contents of N.Results reported in this work allow us to conclude that acicular ferrite can only be achieved alloying with vanadium and nitrogen,meanwhile bainite is promoted in steels with a low level of nitrogen.It is concluded that higher strength values are obtained in acicular ferrite than in bainitic steel but a similar brittle-ductile transition temperature (BDT),and lower values of impact absorbed energy (KV) has been recorded in nitrogen-rich steel.展开更多
Nowadays there is a continuous demand,particularly from the automotive industry,for cheaper,lighter and more reliable components.It is not surprising then that steel research has been focused during the last decades i...Nowadays there is a continuous demand,particularly from the automotive industry,for cheaper,lighter and more reliable components.It is not surprising then that steel research has been focused during the last decades in new qualities and processes.This paper is dealing with the use of vanadium microalloyed steels on one of those new processes,warm forging.For its low precipitation temperature and its recognised ability to strengthen steel microstructures via austenite grain growth control,precipitation hardening and interference of the static recrystallization process,vanadium in microalloyed steels seem to be an appropriate candidate for warm forging.展开更多
In the rolling production of steel,predicting the performance of new products is challenging due to the low variety of data distributions resulting from standardized manufacturing processes and fixed product categorie...In the rolling production of steel,predicting the performance of new products is challenging due to the low variety of data distributions resulting from standardized manufacturing processes and fixed product categories.This scenario poses a significant hurdle for machine learning models,leading to what is commonly known as the“cold-start problem”.To address this issue,we propose a knowledge graph attention neural network for steel manufacturing(SteelKGAT).By leveraging expert knowledge and a multi-head attention mechanism,SteelKGAT aims to enhance prediction accuracy.Our experimental results demonstrate that the SteelKGAT model outperforms existing methods when generalizing to previously unseen products.Only the SteelKGAT model accurately captures the feature trend,thereby offering correct guidance in product tuning,which is of practical significance for new product development(NPD).Additionally,we employ the Integrated Gradients(IG)method to shed light on the model's predictions,revealing the relative importance of each feature within the knowledge graph.Notably,this work represents the first application of knowledge graph attention neural networks to address the cold-start problem in steel rolling production.By combining domain expertise and interpretable predictions,our knowledge-informed SteelKGAT model provides accurate insights into the mechanical properties of products even in cold-start scenarios.展开更多
基金financially supported by the National Natural Science Foundation of China (Nos. 51961130389 and U1808208)the National Key Research and Development Program (No.2017YFB0304402)。
文摘Metastable austenite plays a critical role in achieving improved combinations of high strength and high ductility/toughness in the design of advanced high-strength steels(AHSS). The thermal stability of metastable austenite determines the transformation characteristics of AHSS and thus primarily determines the microstructure evolution during complex processes, e.g., the quenching and partitioning process, to achieve the desirable microstructure. This study provides a review of the thermal stability of austenite and its influence on martensitic transformation from both experimental and theoretical modeling perspectives. From the experimental perspective, factors affecting the thermal stability are analyzed,the relative sensitivities are compared, and their corresponding mechanisms are discussed. From the theoretical modeling perspective, the most representative kinetic models that describe athermal and isothermal martensitic transformation are reviewed. The advantages, shortcomings, and applicability of each model are discussed. The systematic review of both experimental and theoretical aspects reveals critical factors in tailoring the stability of metastable austenite and, therefore, provides guidance for the design of advanced steels.
基金VANITECthe Spanish Ministerio de Ciencia e Innovacion for their financial support
文摘The effect of chemical composition and processing parameters on the formation of acicular ferrite and/or bainite has been investigated.In particular,this paper deals with the influence that N through its combination with V,as V(C,N) precipitates,has on the decomposition of austenite.Likewise,the intragranular nucleation potency of V(C,N) precipitates is analyzed through the continuous cooling transformation diagrams (CCT) of two C-Mn-V steels with different contents of N.Results reported in this work allow us to conclude that acicular ferrite can only be achieved alloying with vanadium and nitrogen,meanwhile bainite is promoted in steels with a low level of nitrogen.It is concluded that higher strength values are obtained in acicular ferrite than in bainitic steel but a similar brittle-ductile transition temperature (BDT),and lower values of impact absorbed energy (KV) has been recorded in nitrogen-rich steel.
基金VANITECthe Spanish Ministerio de Ciencia e Innovacion for their financial support
文摘Nowadays there is a continuous demand,particularly from the automotive industry,for cheaper,lighter and more reliable components.It is not surprising then that steel research has been focused during the last decades in new qualities and processes.This paper is dealing with the use of vanadium microalloyed steels on one of those new processes,warm forging.For its low precipitation temperature and its recognised ability to strengthen steel microstructures via austenite grain growth control,precipitation hardening and interference of the static recrystallization process,vanadium in microalloyed steels seem to be an appropriate candidate for warm forging.
基金supported by the National Key R&D Program(No.2021YFB3702404)National Natural Science Foundation of China(Nos.52311530082 and U22A20106)support provided by“Xingliao Talent Plan”project(Grant No.XLYC2203027)is gratefully acknowledged.
文摘In the rolling production of steel,predicting the performance of new products is challenging due to the low variety of data distributions resulting from standardized manufacturing processes and fixed product categories.This scenario poses a significant hurdle for machine learning models,leading to what is commonly known as the“cold-start problem”.To address this issue,we propose a knowledge graph attention neural network for steel manufacturing(SteelKGAT).By leveraging expert knowledge and a multi-head attention mechanism,SteelKGAT aims to enhance prediction accuracy.Our experimental results demonstrate that the SteelKGAT model outperforms existing methods when generalizing to previously unseen products.Only the SteelKGAT model accurately captures the feature trend,thereby offering correct guidance in product tuning,which is of practical significance for new product development(NPD).Additionally,we employ the Integrated Gradients(IG)method to shed light on the model's predictions,revealing the relative importance of each feature within the knowledge graph.Notably,this work represents the first application of knowledge graph attention neural networks to address the cold-start problem in steel rolling production.By combining domain expertise and interpretable predictions,our knowledge-informed SteelKGAT model provides accurate insights into the mechanical properties of products even in cold-start scenarios.
