In this theoretical study, the prediction of the corrosion resistance and age-hardening behavior of an Al/TiC functional gradient material (FGM) has been investigated by using the artificial neural network (ANN). The input parameters have been selected as TiC volume fraction of the composite layers, aging periods of the composite, environmental conditions, and applied potential during the corrosion tests. Current and microhardness were used as the one output in the proposed network. Also, a new three-layered composite has been imaginarily designed to demonstrate the predictive capability and flexibilities of the ANN model as a case study. Artificially aging (T6) process and potentiodynamic scanning (PDS) tests were used for heat-treating and corrosion response of the FGS, respectively. The results showed that the generated PDS curves of the FGM and calculated corrosion parameters of the case study are quite near and in acceptable limits for similar composites obtained values in experimental studies. Besides, this study has been a great success in predicting peak-aging times and its corresponding hardness values more precisely.

中文翻译：

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预测 Al/TiC 功能梯度材料 (FGM) 时效硬化和腐蚀行为的人工神经网络 (ANN) 解决方案

在这项理论研究中，使用人工神经网络 (ANN) 研究了 Al/TiC 功能梯度材料 (FGM) 的耐腐蚀性和时效硬化行为的预测。输入参数被选择为复合层的 TiC 体积分数、复合材料的老化周期、环境条件和腐蚀测试期间的施加电位。电流和显微硬度被用作建议网络中的一个输出。此外，一个新的三层复合材料已被想象性地设计来展示 ANN 模型作为案例研究的预测能力和灵活性。人工时效 (T6) 工艺和动电位扫描 (PDS) 测试分别用于 FGS 的热处理和腐蚀响应。结果表明，FGM 生成的 PDS 曲线和案例研究的计算腐蚀参数非常接近并处于实验研究中获得的类似复合材料的可接受范围内。此外，这项研究在更准确地预测峰值时效时间及其相应的硬度值方面取得了巨大成功。