Predicting the critical buckling loads of functionally graded material (FGM) plates using an analytical method requires solving complex equations with various modes of deformation to determine the minimum loads. The approach is too complex for application in engineering practice. In this paper, a data-driven model using the artificial neural network (ANN) is proposed for the critical buckling load of FGM plates, as an alternative tool for practicing engineers. A database is first developed for randomly selected inputs using an analytical solution based on first-order shear deformation theory for simply supported FGM plates. The database is then divided into a training dataset with 80% of the data and a testing dataset with 20% of the data for developing and validating, respectively, the ANN model. The ANN model developed using six hidden layers with 32 nodes in each layer is found to match the data with a coefficient of determination of 99.95%. Using the ANN model, the stochastic characteristic of the critical buckling load is examined with respect to randomness of the input parameters. The study reveals that along with the dimensional parameters, the critical buckling load is significantly affected by the randomness of the volume fraction ratio and ratio of the modulus of elasticity of the ceramic and the metal.

使用分析方法预测功能梯度材料 (FGM) 板的临界屈曲载荷需要求解具有各种变形模式的复杂方程，以确定最小载荷。该方法对于工程实践中的应用来说过于复杂。在本文中，针对 FGM 板的临界屈曲载荷提出了使用人工神经网络 (ANN) 的数据驱动模型，作为实践工程师的替代工具。首先使用基于简支 FGM 板的一阶剪切变形理论的解析解为随机选择的输入开发数据库。然后将数据库分成一个训练数据集，其中包含 80% 的数据，以及一个包含 20% 数据的测试数据集，分别用于开发和验证 ANN 模型。发现使用六个隐藏层（每层有 32 个节点）开发的 ANN 模型与数据匹配，确定系数为 99.95%。使用 ANN 模型，根据输入参数的随机性检查临界屈曲载荷的随机特性。研究表明，与尺寸参数一起，临界屈曲载荷受陶瓷与金属的体积分数比和弹性模量比的随机性显着影响。