Marohnić Tea, Basan Robert, Marković Ela
Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia.
Materials (Basel). 2023 Jul 15;16(14):5010. doi: 10.3390/ma16145010.
This paper introduces a novel method for estimating the cyclic stress-strain curves of steels based on their monotonic properties and plastic strain amplitudes, utilizing artificial neural networks (ANNs). ANNs were trained on a substantial number of experimental data for steels, collected from relevant literature, and divided into subgroups according to alloying elements content (unalloyed, low-alloy, and high-alloy steels). Only monotonic properties that were proven to be relevant for the estimation of points on the stress-strain curve were used. The performance of the developed ANNs was assessed using an independent set of data, and the results were compared to experimental values, values obtained by existing empirical estimation methods, and by previously developed ANNs. The results showed that the new approach which combines relevant monotonic properties and plastic strain amplitudes as inputs to ANNs for cyclic stress-strain curve estimation is better than the previously used approach where ANNs estimate the parameters of the Ramberg-Osgood material model separately. This shows that a more favorable approach to the estimation of cyclic stress-strain behavior would be to directly estimate corresponding material curves using monotonic properties. Additionally, this may also reduce inaccuracies resulting from simplified representations of the actual material behavior inherent in the material model.
本文介绍了一种基于钢材的单调特性和塑性应变幅值，利用人工神经网络（ANNs）估算钢材循环应力-应变曲线的新方法。基于从相关文献中收集的大量钢材实验数据对人工神经网络进行训练，并根据合金元素含量（非合金钢、低合金钢和高合金钢）将其分为子组。仅使用经证明与应力-应变曲线上各点估算相关的单调特性。使用一组独立数据评估所开发人工神经网络的性能，并将结果与实验值、现有经验估算方法获得的值以及先前开发的人工神经网络获得的值进行比较。结果表明，将相关单调特性和塑性应变幅值作为人工神经网络输入以估算循环应力-应变曲线的新方法，优于先前使用的人工神经网络分别估算兰伯格-奥斯古德材料模型参数的方法。这表明，估算循环应力-应变行为的更有利方法是使用单调特性直接估算相应的材料曲线。此外，这还可能减少材料模型中固有实际材料行为简化表示所导致的不准确性。
