摘要
在由氟化钾和氢氧化钾构成的碱性处理液中,采用交流微弧氧化处理技术对MB8镁合金进行了电化学表面处理研究,建立了膜层厚度与处理参数之间的关系模型,确定了膜层的组织构成,讨论了膜层生长机理,优化了MB8镁合金交流微弧氧化快速形成致密膜层技术参数。结果表明:人工神经网络技术可很好地建立膜层厚度与处理参数之间的关系模型;膜层主要由氟化镁和氧化镁构成,致密膜层的最大平均厚度范围为35~36μm,采用遗传算法优化并得到实验验证的可形成35μm厚致密膜层的交流微弧氧化快速成膜技术参数为:氟化钾浓度为1 182 g·L-1、氢氧化钾浓度为393 g·L-1、调压器输出电压为61 V、处理液温度为34℃、处理时间为116 s,该快速成膜速度较其它处理技术的成膜速度至少提高了7倍。
In KF+KOH alkaline treatment solution, the electrochemical surface treatment of MB8 magnesium alloy was carried out utilizing AC micro-arc oxidation treatment technology. The relationship between coating thickness and treatment parameters was established, the structure of coating was determined, the growth mechanism of coating was discussed and the treatment parameters for quick forming of compact coating was optimized. The results show that: the artificial neural networks technique can establish the relationship model between coating thickness and treatment parameters perfectly. The coating is made up of MgF2 and MgO mainly and the maximum average thickness of compact coating is 35-36 μm. The treatment conditions for 35 μm thicked compact coating, optimized by a genetic algorithm and validated by further experiments, are 1 182 g-L-1 for concentration of KF, 393 g.L-1 for concentration of KOH, 61 V for voltage, 34 ℃ for temperature of treatment solution and 116 s for treatment time. This quick forming speed of coating is 7 times quicker than that of other treatment technology at least.
出处
《无机化学学报》
SCIE
CAS
CSCD
北大核心
2012年第10期2114-2120,共7页
Chinese Journal of Inorganic Chemistry
基金
国家自然科学基金(No.50974010)
北京自然科学基金(No.2102039)资助项目
关键词
MB8镁合金
交流微弧氧化
快速成膜
生长机理
MB$ magnesium alloy
AC micro-arc oxidation
quick forming of coating
growth mechanism of coating