Modeling of the Evolution of the Microstructure and the Hardness Penetration Depth for a Hypoeutectoid Steel Processed by Grind-Hardening

被引:10
作者
Guo, Yu [1 ]
Liu, Minghe [2 ]
Yin, Mingang [1 ]
Yan, Yutao [1 ]
机构
[1] Northeastern Univ, Sch Mech Engn & Automat, Shenyang 110819, Peoples R China
[2] Shenyang Jianzhu Univ, Sch Mech Engn, Shenyang 110168, Peoples R China
基金
中国国家自然科学基金;
关键词
grind-hardening process; cellular automata; microstructure evolution; prediction of hardness penetration depth; CELLULAR-AUTOMATA MODEL; STATIC RECRYSTALLIZATION; PHASE-TRANSFORMATION; RESIDUAL-STRESS; SIMULATION; LAYER; AUSTENITIZATION; PREDICTION; CHATTER; GROWTH;
D O I
10.3390/met10091182
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Grind-hardening processing is an emerging approach that combines the grinding and surface quenching process. During the process, the hardened layer-mainly martensite-is produced on the surface of the workpiece to achieve the purpose of surface strengthening. Above all, the surface temperature field of the hypoeutectoid-1045 steel workpiece was determined by finite element method for fully revealing the formation mechanism of the hardened layer. Further, the cellular automata approach was applied to dynamically simulate the transformation of both austenitization and martensitization from the initial microstructure. The hardness penetration depth was also predicted. Finally, a grind-hardening experiment was conducted to assess the theoretical study. Results showed that a combination of the finite element method and the cellular automata approach can effectively simulate the microstructure transformation of hardened layer. The microstructure and the hardness penetration depth were affected by the maximum grinding temperature and the heating rate. Research on the influence of grinding parameters showed that the hardness penetration depth increased as the depth of the wheel cut and feeding speed increased. Experiments revealed that the difference between predicted value and experimental value of the hardness penetration depth varied between 2.83% and 7.31%, which confirmed the effectiveness of the predicted model.
引用
收藏
页码:1 / 17
页数:17
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