文章摘要
低碳低合金钢热处理退火工艺参数优化研究
Study of Optimizing Process Parameters during Annealing of Low Carbon Low Alloy Steel
Received:September 10, 2012  Revised:November 10, 2012
DOI:
中文关键词: 退火  铁素体  珠光体  灰色系统理论  优化
英文关键词: annealing  ferrite  pearlite  grey system theory  optimization
基金项目:安徽省教育厅自然科学研究重大项目(KJ2011ZD09)
Author NameAffiliation
ZHAO Mao-yu 1.Department of Mechanical Engineering, Hefei University, Hefei 230601, China
2.School of Chemical and Materials Science, University Science and Technology of China, Hefei 230026, China 
CHEN Qian-wang School of Chemical and Materials Science, University Science and Technology of China, Hefei 230026, China 
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中文摘要:
      合理的热处理退火工艺参数匹配是获得均匀、细化微观组织材料的关键。以低碳低合金钢20CrMnTi为研究对象,在临界-亚临界温度Ac附近加热和保温试样,获得细小的片状珠光体和细化的铁素体晶粒。以加热温度和保温时间为设计变量,通过正交试验设计退火工艺方案,获取材料的塑性数据(硬度、屈服强度、断面收缩率和伸长率)。通过多层次分析法计算多目标综合塑性的权重,然后运用灰色系统理论计算退火工艺参数的优化组合,进一步用优化的工艺参数组合退火处理试样。应用扫描电镜测试并优化工艺处理材料的微观组织,获得分布均匀的铁素体和细小片状渗碳体晶粒组织;通过拉伸应力-应变试验曲线分析材料力学性能,均证明该材料的综合塑性明显提高,表明该优化退火工艺参数的方法是正确、可行的。
英文摘要:
      A suitable matching of annealing process parameters is the key factor to attain finer microstructure of material. Low carbon low alloy steel (20CrMnTi) was heated near Ac temperature to achieve fine lamellar pearlite and ferrite grains. Annealing temperature and time were used as independent variables, and material property data (hardness, yield strength, elongation and contraction of cross sectional area) were acquired by orthogonal experiment. The weight of multi-object qualities had been calculated by analytic hierarchy process (AHP), then the optimization process parameters were calculated by the grey theory system. Furthermore, the samples were annealed by optimized parameters. Optimized materials demonstrate that the microstructure is composed of even ferrite grains and the smaller lamellar cementites, and the microstructure morphologies tested by SEM, and mechanical properties by the tensile stress-strain curve show that the material integrated plasticities are obviously increased by optimizing annealing process. Finally, the new optimizing method is proved to be correct and feasible.
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