加载速率、裂纹宽度和裂纹长度比对PMMA I型断裂韧性的人工智能预测

Attasit Wiangkham, Prasert Aengchuan, Atthaphon Ariyarit
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引用次数: 0

摘要

目前,人工智能方法在解决材料断裂韧性等复杂工程问题中发挥着巨大的作用,断裂韧性是工程设计中需要考虑的参数之一。断裂韧性试验可以制备材料并以多种方式进行试验配置,从而根据制备方法的不同产生不同的断裂韧性。在本研究中,加载速率影响下PMMA的断裂韧性是可根据材料的实际载荷特性和根据试件准备的裂纹几何形状(裂纹宽度和裂纹长度比)进行调整的测试配置之一,并采用人工智能模型之一的广义回归神经网络(GRNN)和高斯过程回归(GPR)模型预测这些因素的影响。与传统断裂韧性预测相比。结果表明,与传统的断裂韧性预测相比,人工智能预测能够更准确地预测所研究因素对PMMA断裂韧性的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Intelligence Predictions Effect of Loading Rate, Crack Width and Crack Length Ratio on Mode I Fracture Toughness of PMMA
Present, artificial intelligence methods play a huge role in solving complex engineering problems such as the fracture toughness of materials, which is one of the parameters to be considered for engineering design. Fracture toughness tests can be prepared materials and test configured in a variety of ways, resulting in different fracture toughness depending on the preparation method. In this study, fracture toughness of PMMA under the effect of loading rate is one of the testing configs that can be adjusted according to the actual load characteristics of the material and the crack geometry (crack width and crack length ratio) according to crack preparation to test specimens and the effect of these factors was predicted with generalized regression neural network (GRNN) and Gaussian processes regression (GPR) models which are one of the artificial intelligence models, compared to traditional fracture toughness predictions. The results showed that artificial intelligence prediction was able to more accurately predict the effect of the factors studied on the fracture toughness of PMMA compared to the traditional fracture toughness prediction.
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