摘要
为抑制感应钎涂层的基体热效应,以涂层感应重熔过程为研究对象,建立二维有限元模型,研究了涂层材料和厚度对感应重熔温度场的影响规律。在本研究条件下得出以下结论:当Ti49Zr49Be涂层厚度为0.1~0.5 mm时,涂层熔化界面的推移方式受涂层厚度影响显著,随着涂层厚度的增加,基体敏感深度和高于敏感温度的持续时间均减小;相较于高熔点Ni71CrSi镍基涂层,低熔点Ni65CrP镍基涂层重熔过程中的基体敏感深度较浅、持续时间较短;相较于相同熔点的Ni65CrP镍基涂层,Ti49Zr49Be钛基涂层重熔过程中的基体敏感深度较浅、持续时间较短。因此,降低涂层材料熔点、适当增加涂层厚度,均有利于减小热影响深度、缩短热影响时间,从而抑制感应钎涂过程中的基体热效应。
To suppress the substrate thermal effects in induction brazing,a two-dimensional finite element model was established to study the induction remelting process of the coating.The influence of the coating materials and thickness on the temperature field was investigated.Under the present conditions,the following conclusions were drawn:when the thickness of the Ti49Zr49Be coating was between 0.1 and 0.5 mm,the movement of the coating melting interface was significantly affected by the coating thickness.With an increase in coating thickness,both the sensitive depth of the substrate and the duration above the sensitive temperature decreased.Compared to the high-melting-point Ni71CrSi nickel-based coating,the low-melting-point Ni65CrP nickel-based coating exhibited a shallower sensitive depth and shorter duration during the remelting process.Similarly,compared to the Ni65CrP nickel-based coating with the same melting point,the Ti49Zr49Be titanium-based coating showed a shallower sensitive depth and shorter duration during the remelting process.Therefore,reducing the melting point of the coating material and appropriately increasing the coating thickness are both beneficial in reducing the sensitive depth and duration,thereby suppressing the substrate thermal effects in induction brazing.
作者
赵梦琪
石秋生
陈林
杨冠军
ZHAO Mengqi;SHI Qiusheng;Chen Lin;YANG Guanjun(State Key Laboratory for Mechanical Behavior of Materials,School of Materials Science and Engineering,Xi'an Jiaotong University,Xi'an 710049,China)
出处
《中国材料进展》
CAS
CSCD
北大核心
2023年第6期506-513,共8页
Materials China
基金
国家科技重大专项(2017-Ⅶ-0012-0107)。
关键词
感应重熔
涂层特性
有限元法
温度分布
数值模拟
induction remelting
coating characteristics
finite element method
temperature distribution
numerical study