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台阶轴连续感应淬火组织硬度分布预测及其工艺优化 被引量:3

Prediction on microstructure,hardness and process parameter optimization in continuous induction hardening of stepped shaft
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摘要 建立了S45C钢台阶轴连续感应淬火过程的分区模拟模型,加载相应的物性和工艺参数,采用感应加热结束时刻轴径向温度分布、Maynier模型、Carsi修正模型和钢CCT曲线拟合预测其淬硬层深度分布,通过联动分析各方法的模拟结果优化了连续感应淬火工艺参数。结果表明,感应淬硬层中关键点b的100%、50%和0%马氏体的模拟深度分别为1. 31、1. 49和2. 97 mm,误差分别为-12. 67%、-13. 87%和-1. 00%,关键点e分别为1. 44、2. 02、2. 54 mm,误差分别为-4. 00%、-3. 38%和-18. 06%,与试验结果较吻合。通过感应淬火工艺参数改变和物理模型调整等探讨了零件各点处的轴径淬硬层变化,给出拟优化的感应淬火工艺参数。初步探讨了换热系数h变化对感应淬硬层的影响。 A physical model of continuous induction hardening process for S45C steel step shaft was established,which was reasonably divided into areas and loaded with corresponding physical properties and process parameters. The hardening depth distribution of the hardened layer was predicted using the radial-axis temperature distribution at the end of the heating phase,the Maynier model,the Carsi correction model and the steel CCT curve fitting. The parameters of continuous induction hardening process were optimized by jointly analyzing the simulation results of each method. The results show that the depths of 100%,50% and 0% martensite in the induction hardened layer of the tested key point b are 1. 31,1. 49 and 2. 97 mm with errors of -12. 67%,-13. 87% and -1. 00%,respectively,as well as the key point e are 1. 44,2. 02 and 2. 54 mm with errors of -4. 00%,-3. 38% and -18. 06%,which are in good agreement with the test results. The quasi-optimized induction hardening process parameters were discussed by changing the induction hardening process parameter and adjusting the physical model. The effect of heat exchange coefficient h change on the induction hardening layer was preliminarily investigated.
作者 高宇 高兴旺 张根元 Gao Yu;Gao Xingwang;Zhang Genyuan(College of Mechanical and Electrical Engineering,Hohai University,Changzhou Jiangsu 213022,China)
出处 《金属热处理》 CAS CSCD 北大核心 2020年第5期215-221,共7页 Heat Treatment of Metals
关键词 连续感应淬火 台阶轴 淬硬层深度 预测法 工艺优化 continuous induction hardening stepped shaft hardened layer prediction methods process optimization
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