FeCrAl合金化学机械抛光粗糙度预测模型研究

doi:10.3901/JME.2023.23.310

摘要/Abstract

摘要： FeCrAl合金由于具有较好的抗辐照能力以及耐高温蒸汽腐蚀等优点，已成为事故容错材料包壳乃至第四代反应堆的重要候选材料之一。然而，在化学机械抛光过程中，FeCrAl合金表面晶界高差的出现影响了其表面粗糙度，难以获得超光滑表面。因此，为揭示FeCrAl合金晶界高差的形成和演化机理，建立了考虑晶界高差的化学机械抛光粗糙度预测模型。首先，计算了加工过程中单颗磨粒的材料去除体积及有效磨粒数量；随后，基于弹塑性接触理论，得到了任意两种晶向之间的晶界高差稳定值，并实现了对表面粗糙度稳定值S_a的预测；最后通过工艺实验对提出的预测模型进行了验证。结果表明：在加工后，基于预测模型计算的表面粗糙度稳定值与实验获得的表面粗糙度值变化趋势一致，模型误差在17%以内。基于预测模型对工艺参数进行优化后，最终表面粗糙度稳定值S_a降低至1.2 nm，获得了超光滑表面。

关键词: FeCrAl合金, 化学机械抛光, 粗糙度, 预测模型, 晶界高差

Abstract: FeCrAl alloy has become one of the most critical candidate materials used on accident-tolerant fuel (ATF) cladding and fourth-generation power reactors, which requires excellent irradiation resistance and anti-corrosion ability in high-temperature steam. However, it is difficult to achieve an ultra-smooth surface during chemical mechanical polishing (CMP) owing to the grain boundary step. Therefore, a roughness prediction model is established to clarify the formation and evolution mechanism of the grain boundary step. Firstly, the volume of material removal by a single abrasive and the number of effective abrasives are calculated during the process. Then, the stable grain boundary step between the grain orientations is obtained based on the elastic-plastic contact theory, and the prediction of stable roughness S_a is realized. Finally, experiments are conducted to validate the model. The results show that the trend of the calculation is consistent with stable surface roughness obtained by the experiments, and the error between them is within 17%. The parameters are optimized based on the prediction model, and an ultra-smooth surface is obtained with the stable surface roughness S_a 1.2 nm.

Key words: FeCrAl alloy, CMP, surface roughness, predictive model, grain boundary step

中图分类号:

TG175

郭江, 杨哲, 张鹏飞, 李琳光, 俞学雯, 潘博. FeCrAl合金化学机械抛光粗糙度预测模型研究[J]. 机械工程学报, 2023, 59(23): 310-319.

GUO Jiang, YANG Zhe, ZHANG Pengfei, LI Linguang, YU Xuewen, PAN Bo. Prediction of Surface Roughness in Chemical Mechanical Polishing of FeCrAl[J]. Journal of Mechanical Engineering, 2023, 59(23): 310-319.

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