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Cr-Co-Mo-Ni齿轮钢模锻件晶粒度预测模型研究
引用本文:程爱民,李京社,高向宙,刘成松,杨树峰.Cr-Co-Mo-Ni齿轮钢模锻件晶粒度预测模型研究[J].工业加热,2014(6):11-15.
作者姓名:程爱民  李京社  高向宙  刘成松  杨树峰
作者单位:北京科技大学钢铁冶金新技术国家重点实验室,北京 100083; 北京科技大学冶金与生态工程学院,北京100083
摘    要:模锻件各部位受应力和温度作用的差异性会形成不同的晶粒尺寸。以Cr-Co-Mo-Ni齿轮钢模锻件为对象,结合某锻造厂的实际模锻工艺参数,利用DEFORM软件中的神经网络技术,建立了Cr-Co-Mo-Ni齿轮钢晶粒尺寸和峰值应力的预测模型,并将计算结果与工业试验结果进行了验证对比。结果表明,该模型对于晶粒尺寸的预测最大误差为6.56%,模型精度较高,能够较好地用于模锻过程不同工艺参数下对晶粒尺寸的预测,继而为改善模锻件晶粒尺寸均匀性提供重要的理论基础。

关 键 词:Cr-Co-Mo-Ni齿轮钢  晶粒度  神经网络  预测模型

Prediction Model on Grain Size of Die Forging for Cr-Co-Mo-Ni Gear Steel
CHENG Aimin,LI Jingshe,GAO Xiangzhou,LIU Chengsong,YANG Shufeng.Prediction Model on Grain Size of Die Forging for Cr-Co-Mo-Ni Gear Steel[J].Industrial Heating,2014(6):11-15.
Authors:CHENG Aimin  LI Jingshe  GAO Xiangzhou  LIU Chengsong  YANG Shufeng
Affiliation:CHENG Aimin, LI Jingshe, GAO Xiangzhou, LIU Chengsong, YANG Shufeng (1. State Key Laboratory of Advanced Metallurgy, University of Science and Technology Beijing, Beijing 100083, China; 2. School of Metallurgical and Ecological Engineering, University of Science and Technology Beijing, Beijing 100083, China)
Abstract:Because of the otherness from stress and temperature, the grain sizes at different position of die forging would be different. Based on an actual die forging process in a certain forge plant, the Cr-Co-Mo-Ni gear steel was regarded as the object of study. A neural network prediction model from DEFORM software for grain size and peak stress of Cr-Co-Mo-Ni gear steel was established. The verification results from industrial test showed that the maximum error of prediction model is 6.56%, which had a high accuracy and could precast the grain size under different process parameters in die forging process. This research could provide an important theoretical basis for optimizing the grain size uniformity of die forging.
Keywords:Cr-Co-Mo-Ni gear steel  grain size  neural network  prediction model
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