The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform
Wenbo Han - One of the best experts on this subject based on the ideXlab platform.
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Microstructural Feature and thermal shock behavior of hot pressed zrb2 sic zro2 composite
Materials Chemistry and Physics, 2009Co-Authors: Dejiang Chen, Xinghong Zhang, Jiecai Han, Changqing Hong, Wenbo HanAbstract:Abstract This paper investigated the thermal shock behavior of hot-pressed zirconium diboride (ZrB2–10 vol%SiC–10 vol%ZrO2) composite by means of water quenching. Under single thermal shock, the retained strength presented sharp degradation with the temperature difference (ΔT) above 500 °C. Differently, the retained strength decreased rapidly with ΔT above 300 °C under five-cycle thermal shock. Analysis of the Microstructural Features also showed discrepancy for the composite under different thermal shock conditions. Further discussion found that the thermal shock resistance of ZrB2–SiC–ZrO2 composite was influenced by the intrinsic properties as well as the oxidation behavior under high temperature.
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Microstructural Feature and thermal shock behavior of hot-pressed ZrB2–SiC–ZrO2 composite
Materials Chemistry and Physics, 2009Co-Authors: Dejiang Chen, Xinghong Zhang, Jiecai Han, Changqing Hong, Wenbo HanAbstract:Abstract This paper investigated the thermal shock behavior of hot-pressed zirconium diboride (ZrB2–10 vol%SiC–10 vol%ZrO2) composite by means of water quenching. Under single thermal shock, the retained strength presented sharp degradation with the temperature difference (ΔT) above 500 °C. Differently, the retained strength decreased rapidly with ΔT above 300 °C under five-cycle thermal shock. Analysis of the Microstructural Features also showed discrepancy for the composite under different thermal shock conditions. Further discussion found that the thermal shock resistance of ZrB2–SiC–ZrO2 composite was influenced by the intrinsic properties as well as the oxidation behavior under high temperature.
Dejiang Chen - One of the best experts on this subject based on the ideXlab platform.
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Microstructural Feature and thermal shock behavior of hot pressed zrb2 sic zro2 composite
Materials Chemistry and Physics, 2009Co-Authors: Dejiang Chen, Xinghong Zhang, Jiecai Han, Changqing Hong, Wenbo HanAbstract:Abstract This paper investigated the thermal shock behavior of hot-pressed zirconium diboride (ZrB2–10 vol%SiC–10 vol%ZrO2) composite by means of water quenching. Under single thermal shock, the retained strength presented sharp degradation with the temperature difference (ΔT) above 500 °C. Differently, the retained strength decreased rapidly with ΔT above 300 °C under five-cycle thermal shock. Analysis of the Microstructural Features also showed discrepancy for the composite under different thermal shock conditions. Further discussion found that the thermal shock resistance of ZrB2–SiC–ZrO2 composite was influenced by the intrinsic properties as well as the oxidation behavior under high temperature.
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Microstructural Feature and thermal shock behavior of hot-pressed ZrB2–SiC–ZrO2 composite
Materials Chemistry and Physics, 2009Co-Authors: Dejiang Chen, Xinghong Zhang, Jiecai Han, Changqing Hong, Wenbo HanAbstract:Abstract This paper investigated the thermal shock behavior of hot-pressed zirconium diboride (ZrB2–10 vol%SiC–10 vol%ZrO2) composite by means of water quenching. Under single thermal shock, the retained strength presented sharp degradation with the temperature difference (ΔT) above 500 °C. Differently, the retained strength decreased rapidly with ΔT above 300 °C under five-cycle thermal shock. Analysis of the Microstructural Features also showed discrepancy for the composite under different thermal shock conditions. Further discussion found that the thermal shock resistance of ZrB2–SiC–ZrO2 composite was influenced by the intrinsic properties as well as the oxidation behavior under high temperature.
Yu Sun - One of the best experts on this subject based on the ideXlab platform.
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modeling the correlation between microstructure and the properties of the ti 6al 4v alloy based on an artificial neural network
Materials Science and Engineering A-structural Materials Properties Microstructure and Processing, 2011Co-Authors: Yu Sun, Weidong Zeng, Yuanfei Han, Yongqing Zhao, Gui Wang, Matthew S Dargusch, Ping GuoAbstract:Modeling the relationship between microstructure and mechanical properties of materials is fairly difficult in that the correlation between them presents highly non-linear and complicated interactions. In this work, the influence of microstructure on the mechanical properties of the Ti–6Al–4V alloy has been investigated using experimental data obtained from the Ti–6Al–4V alloy using forging, heat treatment experiments and tensile tests at room temperature. The Microstructural Feature parameters utilized were acquired with the help of Image Pro software. Furthermore, a relational model was established correlating microstructure and mechanical properties for the Ti–6Al–4V alloy using an artificial neural network (ANN) technique. In the proposed model, the input data consisted of quantitative Microstructural Feature parameters, including the volume fraction of α phase, the thickness of the α phase and the Ferret ratio. Whereas the tensile properties are the outputs of the model, such as ultimate tensile strength, yield strength, elongation and reduction in area. The structure of 3–16–4 in the ANN model was determined. The percentage errors between experimental and predicted values are all less than 5%, which indicates that the ANN model established in the present work possesses the desired prediction ability. In order to test the generalization capability of the ANN model, the combined influence of Microstructural Feature parameters on the mechanical properties of Ti–6Al–4V alloy has been studied using the established ANN model. The research results demonstrate that the proposed method utilizing an artificial neural network offers an accurate correlation between microstructure and mechanical properties for the Ti–6Al–4V alloy, which can be effectively and extensively used for other metals and alloys.
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Modeling the correlation between microstructure and the properties of the Ti–6Al–4V alloy based on an artificial neural network
Materials Science and Engineering: A, 2011Co-Authors: Yu Sun, Weidong Zeng, Yuanfei Han, Yongqing Zhao, Gui Wang, Matthew S Dargusch, Ping GuoAbstract:Modeling the relationship between microstructure and mechanical properties of materials is fairly difficult in that the correlation between them presents highly non-linear and complicated interactions. In this work, the influence of microstructure on the mechanical properties of the Ti–6Al–4V alloy has been investigated using experimental data obtained from the Ti–6Al–4V alloy using forging, heat treatment experiments and tensile tests at room temperature. The Microstructural Feature parameters utilized were acquired with the help of Image Pro software. Furthermore, a relational model was established correlating microstructure and mechanical properties for the Ti–6Al–4V alloy using an artificial neural network (ANN) technique. In the proposed model, the input data consisted of quantitative Microstructural Feature parameters, including the volume fraction of α phase, the thickness of the α phase and the Ferret ratio. Whereas the tensile properties are the outputs of the model, such as ultimate tensile strength, yield strength, elongation and reduction in area. The structure of 3–16–4 in the ANN model was determined. The percentage errors between experimental and predicted values are all less than 5%, which indicates that the ANN model established in the present work possesses the desired prediction ability. In order to test the generalization capability of the ANN model, the combined influence of Microstructural Feature parameters on the mechanical properties of Ti–6Al–4V alloy has been studied using the established ANN model. The research results demonstrate that the proposed method utilizing an artificial neural network offers an accurate correlation between microstructure and mechanical properties for the Ti–6Al–4V alloy, which can be effectively and extensively used for other metals and alloys.
Ping Guo - One of the best experts on this subject based on the ideXlab platform.
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modeling the correlation between microstructure and the properties of the ti 6al 4v alloy based on an artificial neural network
Materials Science and Engineering A-structural Materials Properties Microstructure and Processing, 2011Co-Authors: Yu Sun, Weidong Zeng, Yuanfei Han, Yongqing Zhao, Gui Wang, Matthew S Dargusch, Ping GuoAbstract:Modeling the relationship between microstructure and mechanical properties of materials is fairly difficult in that the correlation between them presents highly non-linear and complicated interactions. In this work, the influence of microstructure on the mechanical properties of the Ti–6Al–4V alloy has been investigated using experimental data obtained from the Ti–6Al–4V alloy using forging, heat treatment experiments and tensile tests at room temperature. The Microstructural Feature parameters utilized were acquired with the help of Image Pro software. Furthermore, a relational model was established correlating microstructure and mechanical properties for the Ti–6Al–4V alloy using an artificial neural network (ANN) technique. In the proposed model, the input data consisted of quantitative Microstructural Feature parameters, including the volume fraction of α phase, the thickness of the α phase and the Ferret ratio. Whereas the tensile properties are the outputs of the model, such as ultimate tensile strength, yield strength, elongation and reduction in area. The structure of 3–16–4 in the ANN model was determined. The percentage errors between experimental and predicted values are all less than 5%, which indicates that the ANN model established in the present work possesses the desired prediction ability. In order to test the generalization capability of the ANN model, the combined influence of Microstructural Feature parameters on the mechanical properties of Ti–6Al–4V alloy has been studied using the established ANN model. The research results demonstrate that the proposed method utilizing an artificial neural network offers an accurate correlation between microstructure and mechanical properties for the Ti–6Al–4V alloy, which can be effectively and extensively used for other metals and alloys.
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Modeling the correlation between microstructure and the properties of the Ti–6Al–4V alloy based on an artificial neural network
Materials Science and Engineering: A, 2011Co-Authors: Yu Sun, Weidong Zeng, Yuanfei Han, Yongqing Zhao, Gui Wang, Matthew S Dargusch, Ping GuoAbstract:Modeling the relationship between microstructure and mechanical properties of materials is fairly difficult in that the correlation between them presents highly non-linear and complicated interactions. In this work, the influence of microstructure on the mechanical properties of the Ti–6Al–4V alloy has been investigated using experimental data obtained from the Ti–6Al–4V alloy using forging, heat treatment experiments and tensile tests at room temperature. The Microstructural Feature parameters utilized were acquired with the help of Image Pro software. Furthermore, a relational model was established correlating microstructure and mechanical properties for the Ti–6Al–4V alloy using an artificial neural network (ANN) technique. In the proposed model, the input data consisted of quantitative Microstructural Feature parameters, including the volume fraction of α phase, the thickness of the α phase and the Ferret ratio. Whereas the tensile properties are the outputs of the model, such as ultimate tensile strength, yield strength, elongation and reduction in area. The structure of 3–16–4 in the ANN model was determined. The percentage errors between experimental and predicted values are all less than 5%, which indicates that the ANN model established in the present work possesses the desired prediction ability. In order to test the generalization capability of the ANN model, the combined influence of Microstructural Feature parameters on the mechanical properties of Ti–6Al–4V alloy has been studied using the established ANN model. The research results demonstrate that the proposed method utilizing an artificial neural network offers an accurate correlation between microstructure and mechanical properties for the Ti–6Al–4V alloy, which can be effectively and extensively used for other metals and alloys.
Sang-in Lee - One of the best experts on this subject based on the ideXlab platform.
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hydrogen storage behavior and Microstructural Feature of a tife zrcr2 alloy
Journal of Alloys and Compounds, 2021Co-Authors: Sang-in Lee, Jihyun Hong, Young-su Lee, Dong-ik Kim, Jin-yoo Suh, Young Whan Cho, Byeongchul Hwang, Joonho Lee, Jae-hyeok ShimAbstract:Abstract The Microstructural Feature as well as the hydrogen storage properties of as-cast and annealed TiFe-6 wt% ZrCr2 alloy samples has been investigated. Three phases, TiFe with the BCC structure, TiFe2 with the C14 Laves hexagonal structure and Ti2Fe with a cubic structure, are observed in both alloy samples, although the amount of TiFe2 as a second phase is significantly reduced with a fragmented shape in the annealed sample. Both samples are hydrogenated under 31 bar of hydrogen at room temperature without a harsh activation process, although the annealed sample is hydrogenated after an incubation period of approximately 40 h. The pressure-composition-temperature curves of both samples are not much different from each other, exhibiting a maximum capacity of 1.7 wt% H2. No significant degradation of hydrogen capacity is observed during 50 cycles of hydrogen sorption for the as-cast sample. The second phase TiFe2 regions seem to assist the first hydrogenation of the alloy by acting as gateways for supplying hydrogen to the inside of the alloy.
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Hydrogen storage behavior and Microstructural Feature of a TiFe–ZrCr2 alloy
Journal of Alloys and Compounds, 2021Co-Authors: Sang-in Lee, Jihyun Hong, Young-su Lee, Dong-ik Kim, Jin-yoo Suh, Young Whan Cho, Byeongchul Hwang, Joonho Lee, Jae-hyeok ShimAbstract:Abstract The Microstructural Feature as well as the hydrogen storage properties of as-cast and annealed TiFe-6 wt% ZrCr2 alloy samples has been investigated. Three phases, TiFe with the BCC structure, TiFe2 with the C14 Laves hexagonal structure and Ti2Fe with a cubic structure, are observed in both alloy samples, although the amount of TiFe2 as a second phase is significantly reduced with a fragmented shape in the annealed sample. Both samples are hydrogenated under 31 bar of hydrogen at room temperature without a harsh activation process, although the annealed sample is hydrogenated after an incubation period of approximately 40 h. The pressure-composition-temperature curves of both samples are not much different from each other, exhibiting a maximum capacity of 1.7 wt% H2. No significant degradation of hydrogen capacity is observed during 50 cycles of hydrogen sorption for the as-cast sample. The second phase TiFe2 regions seem to assist the first hydrogenation of the alloy by acting as gateways for supplying hydrogen to the inside of the alloy.