锻压技术

基于田口方法与灰色关联分析的连杆热锻模具等效应力与磨损

英文标题：Equivalent stress and wear of connecting rod hot forging die based on Taguchi method and grey correlation analysis

分类号：TG76

出版年,卷(期):页码：2024,49(11):135-143

摘要：

以某H13钢连杆模具为研究对象，基于田口方法与灰色关联分析，采用有限元模拟研究发动机连杆锻造过程中，锻造温度、模具预热温度、摩擦因数、模具硬度和锻压速度这5个工艺参数对模具的磨损深度及等效应力的影响规律，基于田口设计方法设计25组正交试验，并基于灰色关联分析法进行多目标关联性分析。结果表明，优化的工艺参数组合为：锻压速度为550 mm·s-1、锻造温度为1200 ℃、模具预热温度为225 ℃、摩擦因数为0.30、模具硬度为56 HRC。与初始工艺相比，最优工艺参数组合下的磨损深度改善了28.3%、等效应力改善了9.1%。最后,通过实际生产验证，主要磨损部位及应力集中区域状况与现场生产情况基本一致,验证了优化模型的可行性。

For H13 steel connecting rod die, based on Taguchi method and grey correlation analysis, the influence laws of five process parameters, namely, forging temperature, die preheating temperature, friction factor, die hardness and forging speed, on the wear depth and equivalent stress of die during the forging process of engine connecting rod were studied by finite element simulation. Then, twenty-five sets of orthogonal test were designed based on Taguchi design method, and the multi-objective correlation analysis was conducted based on grey correlation analysis method. The results indicate that the optimized process parameters are the forging speed of 550 mm·s-1, the forging temperature of 1200 ℃,the die preheating temperature of 225 ℃,the friction factor of 0.30 and the die hardness of 56 HRC. Compared with the initial process, the wear depth and equivalent stress under the optimal process parameter combination are improved by 28.3% and 9.1%, respectively. Finally, through the actual production verification, the conditions of main wear positions and stress concentration areas are basically in line with the actual production conditions, which verifies the feasibility of the optimization model.

基金项目：

云南省科技厅重大科技专项计划（202202AC080006）

作者简介：邓伟(1976-)，男，学士,教授级高工，硕士生导师 E-mail：1323364897@qq.com 通信作者：贾德文(1977-)，男，博士,教授，硕士生导师 E-mail：27546658@qq.com

参考文献：

[1]Han X H, Hua L.3D FE modelling of contact pressure response in cold rotary forging[J].Tribology International,2013,57:115-123.

[2]Lange K, Cser L, Geiger M, et al. Tool life and tool quality in bulk metal forming[J]. CIRP Annals, 1992, 41(2): 667-675.

[3]李宝聚, 王兆辉,谭磊,等.热锻工艺参数对模具磨损影响的有限元分析[J].模具工业,2014,40(9):6-11.

Li B J, Wang Z H, Tan L, et al. Hot forging process parameters on the influence of mold wear finite element analysis [J]. Die & Mould Industry, 2014,40(9):6-11.

[4]刘洋,李峰光,刘建永,等.基于CAE分析的热锻模具磨损部位预测及验证[J].湖北汽车工业学院学报,2021,35(2):58-63,69.

Liu Y, Li F G, Liu J Y, et al. Prediction and verification of wear parts of hot forging dies based on CAE analysis [J]. Journal of Hubei University of Automotive Technology,2021,35(2):58-63,69.

[5]金飞翔，董奇，徐梦洁，等. 基于有限元铝合金复杂精密锻造模具失效分析及优化[J]. 锻压技术，2023，48（2）：180-184.

Jin F X，Dong Q，Xu M J，et al. Failure analysis and optimization on aluminum alloy complex precision forging mold based on finite element [J]. Forging & Stamping Technology，2023，48（2）：180-184.

[6]Kang J H, Park I W, Jae J S,et al. A study on a die wear model considering thermal softening:(I) Construction of the wear model[J].Journal of Materials Processing Technology, 1999,96(1-3): 53-58.

[7]Behrens B A. Finite element analysis of die wear in hot forging processes[J].CIRP Annals-Manufacturing Technology,2008,57(1):305-308.

[8]Lee R S, Jou J L. Application of numerical simulation for wear analysis of warm forging die [J]. Journal of Materials Processing Technology, 2003,140 (1-3):43-48.

[9]李宝聚.热锻模具磨损有限元分析与优化[D].济南：山东大学,2015.

Li B J. Finite Element Analysis and Optimization of Hot Forging Die Wear [D]. Jinan: Shandong University,2015.

[10]王兴,林育阳,贺利乐,等.基于神经网络粉末冶金摆线转子成形模具磨损分析与优化[J].热加工工艺,2022,51(16):44-48.

Wang X, Lin Y Y, He L L, et al. Wear analysis and optimization of powder metallurgy trochoidal rotor forming die based on neural network [J]. Hot Working Technology, 2022,51(16):44-48.

[11]Luo S Y, Zhu D H,Hua L, et al.Numerical analysis of die wear characteristics in hot forging of titanium alloy turbine blade[J].International Journal of Mechanical Sciences,2017,123：260-270.

[12]殷剑,黎诚,金康,等.基于响应面法和修正Archard磨损理论的汽车前下摆臂热锻模具应力与磨损分析[J].锻压技术,2022,47(6):231-238.

Yin J, Li C, Jin K, et al. Stress and wear analysis of hot forging die for automotive front hem arm based on response surface method and modified Archard wear theory [J]. Forging & Stamping Technology, 2022,47(6):231-238.

[13]齐贺闯,叶筱,亢燕铭,等.基于田口方法与灰色关联分析的碰撞射流通风系统优化[J].东华大学学报(自然科学版),2022,48(6):127-133,142.

Qi H C, Ye X, Kang Y M, et al. Optimization of collision jet ventilation system based on taguchi method and grey correlation analysis [J]. Journal of Donghua University(Natural Science),2022,48(6):127-133,142.

[14]Dehnad K. Quality Control, Robust Design, and the Taguchi Method[M]. Boston: Springer, 2012.

[15]梁强,张贤明,贾艳艳.基于灰色关联分析的直齿轮冷挤压成形工艺参数优化[J].塑性工程学报,2021,28(1):69-76.

Liang Q, Zhang X M, Jia Y Y. Optimization of cold extrusion process parameters of spur gear based on grey correlation analysis [J]. Journal of Plasticity Engineering, 2021,28(1):69-76.

[16]Du Y P, Zhao C Y, Tian Y, et al. Analytical considerations of flow boiling heat transfer in metal-foam filled tubes[J]. Heat and Mass Transfer, 2012, 48（1）: 165-173.

[17]Moffat R J. Describing the uncertainties in experimental results[J]. Experimental Thermal and Fluid Science, 1988, 1(1): 3-17.

服务与反馈：

【文章下载】【加入收藏】