Some active metal oxides(Al_(2)O_(3),TiO_(2),and Cr_(2)O_(3))were selected as dopants to the Al_(2)O_(3)-based ceramic shells for investment casting of K417G superalloy.The effects of dopant types and contents(0,2,5,a...Some active metal oxides(Al_(2)O_(3),TiO_(2),and Cr_(2)O_(3))were selected as dopants to the Al_(2)O_(3)-based ceramic shells for investment casting of K417G superalloy.The effects of dopant types and contents(0,2,5,and 8 wt.%)on the wettability and interfacial reaction between the alloy and shell were investigated by a sessile-drop experiment.The results show that increasing the Al_(2)O_(3) doping contents(0−8 wt.%)reduces the porosity(21.74%−10.08%)and roughness(3.22−1.34μm)of the shell surface.The increase in Cr_(2)O_(3) dopant content(2−8 wt.%)further exacerbates the interfacial reaction,leading to an increase in the thickness of the reaction layer(2.6−3.1μm)and a decrease in the wetting angle(93.9°−91.0°).The addition of Al_(2)O_(3) and TiO_(2) dopants leads to the formation of Al_(2)TiO_(5) composite oxides in the reaction products,which effectively inhibits the interfacial reaction.The increase in TiO_(2) dopant contents(0−8 wt.%)further promotes the formation of Al_(2)TiO_(5),which decreases the thickness of the interfacial reaction layer(3.9−1.2μm)and increases the wetting angle(95.0°−103.8°).The introduced dopants enhance the packing density of the shell surface,while simultaneously suppress the diffusion of active metal elements from the alloy matrix to the interface.展开更多
Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy...Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy plates.First,finite element simulations of casting processes were carried out with various parameters to build a dataset.Subsequently,different machine learning algorithms were employed to achieve high precision in predicting temperature fields,mushy zone locations,mushy zone inclination angle,and billet grain size.Finally,the process parameters were quickly optimized using a strategy consisting of random generation,prediction,and screening,allowing the mushy zone to be controlled to the desired target.The optimized parameters are 1234℃for heating mold temperature,47 mm/min for casting speed,and 10 L/min for cooling water flow rate.The optimized mushy zone is located in the middle of the second heat insulation section and has an inclination angle of roughly 7°.展开更多
基金supported by the National Natural Science Foundation of China (No. 52374292)China Baowu Low Carbon Metallurgy Innovation Foundation, China (No. BWLCF202309)the Natural Science Foundation of Changsha City, China (No. KQ2208271)。
文摘Some active metal oxides(Al_(2)O_(3),TiO_(2),and Cr_(2)O_(3))were selected as dopants to the Al_(2)O_(3)-based ceramic shells for investment casting of K417G superalloy.The effects of dopant types and contents(0,2,5,and 8 wt.%)on the wettability and interfacial reaction between the alloy and shell were investigated by a sessile-drop experiment.The results show that increasing the Al_(2)O_(3) doping contents(0−8 wt.%)reduces the porosity(21.74%−10.08%)and roughness(3.22−1.34μm)of the shell surface.The increase in Cr_(2)O_(3) dopant content(2−8 wt.%)further exacerbates the interfacial reaction,leading to an increase in the thickness of the reaction layer(2.6−3.1μm)and a decrease in the wetting angle(93.9°−91.0°).The addition of Al_(2)O_(3) and TiO_(2) dopants leads to the formation of Al_(2)TiO_(5) composite oxides in the reaction products,which effectively inhibits the interfacial reaction.The increase in TiO_(2) dopant contents(0−8 wt.%)further promotes the formation of Al_(2)TiO_(5),which decreases the thickness of the interfacial reaction layer(3.9−1.2μm)and increases the wetting angle(95.0°−103.8°).The introduced dopants enhance the packing density of the shell surface,while simultaneously suppress the diffusion of active metal elements from the alloy matrix to the interface.
基金financially supported by the National Key Research and Development Program of China (No. 2023YFB3812601)the National Natural Science Foundation of China (No. 51925401)the Young Elite Scientists Sponsorship Program by CAST, China (No. 2022QNRC001)。
文摘Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy plates.First,finite element simulations of casting processes were carried out with various parameters to build a dataset.Subsequently,different machine learning algorithms were employed to achieve high precision in predicting temperature fields,mushy zone locations,mushy zone inclination angle,and billet grain size.Finally,the process parameters were quickly optimized using a strategy consisting of random generation,prediction,and screening,allowing the mushy zone to be controlled to the desired target.The optimized parameters are 1234℃for heating mold temperature,47 mm/min for casting speed,and 10 L/min for cooling water flow rate.The optimized mushy zone is located in the middle of the second heat insulation section and has an inclination angle of roughly 7°.
