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金属材料热变形中的塑性流动失稳研究进展 被引量:16

Research progress on plastic flow instability during hot deformation of metal materials
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摘要 塑性流动失稳是金属材料热变形过程中调控微观组织均匀性、提高构件综合力学性能面临的关键基础性问题。对金属材料热变形过程中流动失稳相关的实验和建模方面目前所取得的研究进展进行了综述和分析,指出塑性流动失稳的主要类型有局部流动、宏观剪切带和微裂纹,并且分别分析了不同类型失稳的形成机理;对变形温度、应变速率、变形量及变形前的微观组织对不同类型流动失稳的影响规律进行了总结;从理论和应用两方面,对现有的流动失稳预测准则和模型进行了总结和比较。此外,针对金属材料热成形,如何实现复杂加载路径下考虑变形前组织非均质性影响的流动失稳预测将成为今后发展的一个重要方向。 Plastic flow instability is an important fundamental problem in controlling microstructure homogeneity and improving the comprehensive mechanical properties of components during hot deformation of metal materials. The research progresses made so far on the experiments and modelling of flow instability during hot deformation of metal materials were summarized and analyzed. It is indicated that the main types of plastic flow instability are flow localization,macro shear band and micro cracks. And the formation mechanisms of different types of instability were analyzed respectively;the influence rules of deformation temperature,strain rate,deformation reduction and initial microstructure on different types of flow instability were summarized;the existing prediction criterion and models of flow instability were reviewed and compared from the aspects of theory and application. Besides,for the hot deformation of metal materials,how to achieve the flow instability prediction considering the effect of heterogeneity of the microstructure before deformation under complex loading paths becomes an impartant trend of further development.
作者 姜雪琦 樊晓光 詹梅 李宏伟 王俐 JIANG Xue-qi;FAN Xiao-guang;ZHAN Mei;LI Hong-wei;WANG Li(Shaanxi Key Laboratory of High-Performance Precision Forming Technology and Equipment,School of Materials Science and Engineering,Northwestern Polytechnical University,Xi'an 710072,China)
出处 《塑性工程学报》 CAS CSCD 北大核心 2020年第7期33-51,共19页 Journal of Plasticity Engineering
基金 国家自然科学基金资助项目(51575449) 新金属材料国家重点实验室开放基金资助项目(2019-ZD04)。
关键词 金属材料 热变形 塑性流动失稳 形成机理 预测模型 metal materials hot deformation plastic flow instability formation mechanism prediction model
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