The high-temperature oxidation resistances of Ni-based superalloys with the compositions of Ni–(0–15)Co–(8–15)Cr–(0–5)Mo–(0–10)W–(3–8)Al–(0–5)Ti–(0–10)Ta–0.1C–0.01B were analyzed using an artificial neural network (ANN). The oxidation resistances of the alloys were evaluated based on the weight change measured during cyclic oxidation tests. An ANN was constructed with the contents of the alloying elements as input and mass gains as output. The ANN provided highly accurate regression results. The present results were compared with those obtained using response surface methodology (RSM) in a previous study. The regression model of the ANN could effectively detect the effect of an additional alloying element explicitly. The main and interaction effects of the alloying elements were plotted based on the results with random compositions. The optimum composition of superalloys with the highest oxidation resistance at high temperatures was determined using the trained ANN.

具有Ni–（0–15）Co–（8–15）Cr–（0–5）Mo–（0–10）W–（3–8）组成的镍基高温合金的高温抗氧化性使用人工神经网络（ANN）分析了Al–（0–5）Ti–（0–10）Ta–0.1C–0.01B。根据在循环氧化试验中测得的重量变化评估合金的抗氧化性。构造了人工神经网络，合金元素的含量作为输入，质量增益作为输出。人工神经网络提供了高度准确的回归结果。将当前结果与使用以前的研究中的响应面方法（RSM）获得的结果进行比较。人工神经网络的回归模型可以有效地检测出其他合金元素的影响。基于随机组成的结果，绘制了合金元素的主要作用和相互作用作用。