中文摘要：

目的寻求最佳的微弧氧化工艺参数,提高钛合金的高温抗氧化性能。方法进行3因素3水平正交试验（3因素包括电压、氧化反应时间和电解液浓度）,通过XRD和SEM表征微弧氧化涂层的物相和显微结构,采用650℃×100 h循环氧化试验评价涂层的抗高温氧化性能,最终利用极差分析法分析各因素对涂层试样氧化增重的影响主次,并得到最优参数组合。利用回归分析建立氧化增重与试验各参数之间的数学模型,并分析模型的显著性。结果不同工艺参数下制得的微弧氧化涂层表面形貌特征不同,涂层物相以金红石相和锐钛矿相二氧化钛为主。3个因素对涂层抗高温氧化性的影响由大到小依次为：电压〉时间〉电解液浓度。建立的氧化增重W与各参数（电压V、反应时间t、电解液浓度E）间的二次函数方程模型为：W=0.008 39（V-396.6）＋0.1698t-64.5E-0.000 108（V-396.6）2-0.0044t2＋700E2＋0.0017。结论最佳参数组合为：电压480 V,时间25 min,电解液浓度0.04 mol/L。通过回归分析得到的氧化增重与各参数间的数学模型显著。

英文摘要：

Objective To determine the optimal MAO technological parameters and to improve the the anti-oxidation property of Ti alloy at high temperature. Methods The orthogonal table of L9（ 33） was used to design the test. The coating structure and phase were characterized by SEM and XRD,respectively. Thermal cyclic oxidation at 650 ℃ for 100 h was carried out to evaluate the anti-oxidation property of the coating at high temperature. Range analysis was used to sort the MAO technological parameters according to their effects on the oxidation weight gain of the coating sample,obtaining the combination of optimal parameters. Mathematical model was built between the oxidation weight gain and technological parameters by regression analysis,and the significance of the model was analyzed. Results The surface morphology characteristics of MAO coatings prepared with different technological parameters was different,and the major phases of Ti O2 in the coating were rutile and anatase phases. The effects of the three factors on the anti-oxidation property of the coating at high temperature were in the order of time electrolyte concentration. The quadratic function equation model established between the oxidation weight gain and different parameters（ V,reaction time t,electrolyte concentration E） was W = 0. 008 39（ V-396. 6） ＋ 0. 1698t-64. 5E-0. 000 108（ V-396. 6）2-0. 0044t2＋ 700E2＋ 0. 0017. Conclusion The optimized combination of technological parameters was： 480 V,reaction time 25 min,electrolyte concentration 0. 04 mol / L. The mathematical model obtained from the oxidation weight gain and different parameters through regression analysis was significant.