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A novel immiscible high entropy alloy strengthened via L1_(2)-nanoprecipitate 被引量:1
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作者 WANG Zheng-qin FAN Ming-yu +5 位作者 ZHANG Yang LI Jun-peng LIU Li-yuan HAN Ji-hong LI Xing-hao ZHANG Zhong-wu 《Journal of Central South University》 SCIE EI CAS CSCD 2024年第6期1808-1822,共15页
The low-cost Fe-Cu,Fe-Ni,and Cu-based high-entropy alloys exhibit a widespread utilization prospect.However,these potential applications have been limited by their low strength.In this study,a novel Fe_(31)Cu_(31)Ni_(... The low-cost Fe-Cu,Fe-Ni,and Cu-based high-entropy alloys exhibit a widespread utilization prospect.However,these potential applications have been limited by their low strength.In this study,a novel Fe_(31)Cu_(31)Ni_(28)Al_(4)Ti_(3)Co_(3) immiscible high-entropy alloy(HEA)was developed.After vacuum arc melting and copper mold suction casting,this HEA exhibits a unique phase separation microstructure,which consists of striped Cu-rich regions and Fe-rich region.Further magnification of the striped Cu-rich region reveals that it is composed of a Cu-rich dot-like phase and a Fe-rich region.The aging alloy is further strengthened by a L1_(2)-Ni_(3)(AlTi)nanoprecipitates,achieving excellent yield strength(1185 MPa)and uniform ductility(~8.8%).The differential distribution of the L1_(2) nanoprecipitate in the striped Cu-rich region and the external Fe-rich region increased the strength difference between these two regions,which increased the strain gradient and thus improved hetero-deformation induced(HDI)hardening.This work provides a new route to improve the HDI hardening of Fe-Cu alloys. 展开更多
关键词 heterogeneous microstructure precipitation strengthening high-entropy alloy phase separation mechanical property
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乘用车关门声品质风格划分评价研究 被引量:6
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作者 王政钦 毕锦烟 +1 位作者 黄涛 涂梨娥 《汽车技术》 CSCD 北大核心 2020年第7期30-34,共5页
以10款具有不同关门声品质感的乘用车为研究对象,基于语义细分法、成对比较法的主观评价试验结果和心理声学客观参量(尖锐度积分、响度能量比、响度能量变化率、响度变化时间)建立了主、客观预测模型,为了提高模型预测精度,将评价模型分... 以10款具有不同关门声品质感的乘用车为研究对象,基于语义细分法、成对比较法的主观评价试验结果和心理声学客观参量(尖锐度积分、响度能量比、响度能量变化率、响度变化时间)建立了主、客观预测模型,为了提高模型预测精度,将评价模型分为"豪华感"和"时尚感"两类。将该模型与传统多元回归映射模型进行对比,结果表明,该模型样本内预测精度最大提升18.5%,样本外预测精度最大提升40.3%,基于风格类型划分的关门声品质预测模型具有更高的预测精度。 展开更多
关键词 关门声 声品质 主观评价 风格划分 预测模型
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