Hydraulic simulation is one of the critical methods to research the filling mechanism of molten metal in the casting process.However,it only performs on test pieces with relatively simple structures due to the limitat...Hydraulic simulation is one of the critical methods to research the filling mechanism of molten metal in the casting process.However,it only performs on test pieces with relatively simple structures due to the limitation of the preparation method.In this study,the method of photocuring additive manufacturing was used to prepare the complex casting mould from transparent photosensitive resin.The pouring test was carried out under different centrifugal conditions,and the filling process of the gating system,support bars and other positions in the vertical direction was recorded and analyzed.The experimental results show that the internal liquid level and the filling process of the test piece prepared by this method can be observed clearly.The angle between the liquid surface and the horizontal plane in the test piece gradually increases as the centrifugal rotational speed increases,which means the filling process is carried out from outside to inside at high rotational speed.The velocity of the fluid entering the runner increases with the increase of rotational speed,but the filling speeds is less affected by the centrifugal speed at other positions.The liquid flow is continuous and stable during the forward filling process,without splashing or interruption of liquid droplets.展开更多
1.Introduction Titanium(Ti)and its alloy have become a critical structural material in aerospace,weaponry,and equipment industries due to their high strength,low density,and excellent corrosion resistance[1-3].
Interface issues have consistently impeded efforts to balance a trade-off between the conductivity functionality and mechanical properties of Cu-matrix composites.Combining first-principles simulations,this study addr...Interface issues have consistently impeded efforts to balance a trade-off between the conductivity functionality and mechanical properties of Cu-matrix composites.Combining first-principles simulations,this study addresses this challenge by preparing a new Cumatrix composite reinforced with MXene,Cu/Ag@MXene composite block(CuAM-CB),which improves the compatibility between metallic Cu and nonmetallic MXene facilitated by Ag modification anchored in situ onto MXene nanosheets,thus realizing element-coupled reinforcement of Ag at the Cu/MXene heterointerfaces.Benefiting from the strong interaction between Ag and C atoms from in situ self-reduction,as well as the excellent compatibility between Ag and Cu atoms(both IB group metals),Ag atoms act as a mediator for the electron transport and mechanical connection at the Cu/MXene heterointerfaces,enabling CuAM-CB to achieve integrated high conductivity functionality(up to 95%IACS)and strong mechanical properties(with a strength-plasticity product of∼18 GPa%).展开更多
In the era of big data,reinforcement learning(RL)has emerged as a powerful data-driven optimization approach in materials science,enabling unprecedented advances in material design and performance improvement.Unlike t...In the era of big data,reinforcement learning(RL)has emerged as a powerful data-driven optimization approach in materials science,enabling unprecedented advances in material design and performance improvement.Unlike traditional trial-and-error and physics-based approaches,RL agents autonomously identify optimal strategies across high-dimensional and dynamic design spaces by iterative interactions with complex environments.This capability makes RL especially effective for target optimization and sequential decision-making in challenging materials science problems.In this review,we present a comprehensive overview of fundamental RL algorithms,including Q-learning,deep Q-networks(DQN),actor-critic methods,and deep deterministic policy gradient(DDPG).Then,the core mechanisms,advantages,limitations,and representative applications of RL in materials discovery,property optimization,process control,and manufacturing are discussed systematically.Lastly,key future research directions and opportunities are outlined.The perspectives presented herein aim to foster interdisciplinary collaboration and drive innovation at the frontier of AI‑driven materials science.展开更多
基金This work was financially supported by the National Science and Technology Major Project of China(Grant No.J2019-Ⅶ-0002-0142)the National Natural Science Foundation of China(Grant No.52175333).
文摘Hydraulic simulation is one of the critical methods to research the filling mechanism of molten metal in the casting process.However,it only performs on test pieces with relatively simple structures due to the limitation of the preparation method.In this study,the method of photocuring additive manufacturing was used to prepare the complex casting mould from transparent photosensitive resin.The pouring test was carried out under different centrifugal conditions,and the filling process of the gating system,support bars and other positions in the vertical direction was recorded and analyzed.The experimental results show that the internal liquid level and the filling process of the test piece prepared by this method can be observed clearly.The angle between the liquid surface and the horizontal plane in the test piece gradually increases as the centrifugal rotational speed increases,which means the filling process is carried out from outside to inside at high rotational speed.The velocity of the fluid entering the runner increases with the increase of rotational speed,but the filling speeds is less affected by the centrifugal speed at other positions.The liquid flow is continuous and stable during the forward filling process,without splashing or interruption of liquid droplets.
基金supported by the National Natural Science Foundation of China(Nos.52301029 and 52274359)the Fundamental Research Funds for the Central Universities(No.06500165)+2 种基金the Guangdong Basic and Applied Basic Research Foun-dation(No.2022A1515140006)the Young Elite Scientists Sponsorship Program by CAST(No.2023QNRC001)the Beijing Young Elite Scientists Sponsorship Program by BMES。
文摘1.Introduction Titanium(Ti)and its alloy have become a critical structural material in aerospace,weaponry,and equipment industries due to their high strength,low density,and excellent corrosion resistance[1-3].
基金financially supported by the Central government guides local science and technology development(CN)(No.[2019]4011)the Construction project of anti-fatigue manufacturing technology innovation ability of key components in aerospace(CN)(No.Qian financial workers[2022]92)+3 种基金the Construction of collaborative innovation platform for fatigue resistance manufacturing technology and quality reliability of key components(CN)(No.2016034)the National natural science foundation of China(CN)(No.12105059)the Guizhou provincial science and technology projects(CN)(No.ZK[2022]097)the Talented program of guizhou university(CN)(No.20210032).
文摘Interface issues have consistently impeded efforts to balance a trade-off between the conductivity functionality and mechanical properties of Cu-matrix composites.Combining first-principles simulations,this study addresses this challenge by preparing a new Cumatrix composite reinforced with MXene,Cu/Ag@MXene composite block(CuAM-CB),which improves the compatibility between metallic Cu and nonmetallic MXene facilitated by Ag modification anchored in situ onto MXene nanosheets,thus realizing element-coupled reinforcement of Ag at the Cu/MXene heterointerfaces.Benefiting from the strong interaction between Ag and C atoms from in situ self-reduction,as well as the excellent compatibility between Ag and Cu atoms(both IB group metals),Ag atoms act as a mediator for the electron transport and mechanical connection at the Cu/MXene heterointerfaces,enabling CuAM-CB to achieve integrated high conductivity functionality(up to 95%IACS)and strong mechanical properties(with a strength-plasticity product of∼18 GPa%).
基金supported by the National Natural Science Foundation of China(Nos.52571028,52301029)the Fundamental Research Funds for the Central Universities(No.06500165)+2 种基金the Guangdong Basic and Applied Basic Research Foundation(No.2022A1515140006)the AVIC Heavy Machinery Innovation Fund(ZJQT-2025-06)the Young Elite Scientists Sponsorship Program by CAST(No.2023QNRC001).
文摘In the era of big data,reinforcement learning(RL)has emerged as a powerful data-driven optimization approach in materials science,enabling unprecedented advances in material design and performance improvement.Unlike traditional trial-and-error and physics-based approaches,RL agents autonomously identify optimal strategies across high-dimensional and dynamic design spaces by iterative interactions with complex environments.This capability makes RL especially effective for target optimization and sequential decision-making in challenging materials science problems.In this review,we present a comprehensive overview of fundamental RL algorithms,including Q-learning,deep Q-networks(DQN),actor-critic methods,and deep deterministic policy gradient(DDPG).Then,the core mechanisms,advantages,limitations,and representative applications of RL in materials discovery,property optimization,process control,and manufacturing are discussed systematically.Lastly,key future research directions and opportunities are outlined.The perspectives presented herein aim to foster interdisciplinary collaboration and drive innovation at the frontier of AI‑driven materials science.
