Artificial intelligence is playing an increasing role in materials testing, whether it is in a new material design, designing new testing methods, or creating a model to predict materials properties. In this research, the artificial intelligence was from an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS), which was applied to predict mixed mode I/II fracture toughness of polymethyl methacrylate material (PMMA). The predictive modeling was based on the factors of thickness, width, crack length to width ratio of the specimen, and mode mixity angle. The training, validation, and testing process of the model used a total of 96 data points per factor. The efficiency of the ANN model in the modeling process, R², MSE and MAPE, was 0.9905, 0.0859, and 4.7911 for mode I fracture toughness and 0.9848, 0.0161 and 4.1994 for mode II fracture toughness. The efficiency of the ANFIS model in the modeling process, R², MSE and MAPE, for mode I fracture toughness was 0.9953, 0.0415, and 3.2601, while for mode II fracture toughness was 0.9894, 0.0112, and 3.0894. The model application is used to predict the fracture toughness at different levels of factors from the modeling process, with results showing that the fracture toughness from the prediction model is slightly different from the experimental values.

人工智能在材料测试中扮演着越来越重要的角色，无论是在新材料设计，设计新测试方法还是创建模型来预测材料特性方面。在这项研究中，人工智能来自人工神经网络（ANN）和自适应神经模糊推理系统（ANFIS），该系统用于预测聚甲基丙烯酸甲酯材料（PMMA）的I / II混合模式断裂韧性。预测建模基于厚度，宽度，裂纹长度与样品的宽度比以及众数混合角等因素。该模型的训练，验证和测试过程每个因素总共使用96个数据点。ANN模型在建模过程中的效率R ²，对于I型断裂韧性，MSE和MAPE分别为0.9905、0.0859和4.7911，对于II型断裂韧性，分别为0.9848、0.0161和4.1994。ANFIS模型在建模过程R ²，MSE和MAPE中对I型断裂韧性的效率为0.9953、0.0415和3.2601，而对II型断裂韧性的效率为0.9894、0.0112和3.0894。该模型应用程序通过建模过程预测了不同水平因素下的断裂韧性，结果表明，预测模型的断裂韧性与实验值略有不同。