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基于使用人工神经网络的单调性特性对钢的循环应力-应变曲线的估计
Tea Marohnić1, Robert Basan1, Ela Marković1
1Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia.
Materials (Basel, Switzerland)
|July 29, 2023
概括
本研究介绍了一种新的人工神经网络 (ANN) 方法,用于使用单调性质来估计钢的循环应力-张力曲线. 这种直接估计方法通过提高准确性和减少模型固有的不准确性,优于传统方法.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算材料科学科学 计算材料科学
背景情况:
- 循环应力-应变行为对于材料疲劳寿命预测至关重要.
- 由于复杂的材料反应,精确估计周期性应力-应变曲线具有挑战性.
- 现有的方法通常依赖于具有固有的局限性的经验模型.
研究的目的:
- 开发一种基于人工神经网络 (ANN) 的新方法,用于估计钢的循环应力-张力曲线.
- 利用单调材料特性和塑料拉伸幅度作为ANN模型的直接输入.
- 将拟议方法的性能与现有的经验和基于ANN的方法进行比较.
主要方法:
- 根据合金含量收集和分组广泛的实验钢铁数据.
- 训练ANN使用相关的单调性质和塑性应变幅度.
- 使用独立数据集验证ANN模型性能,并与实验和经验结果进行比较.
主要成果:
- 与现有方法相比,开发的直接估计周期应力-应变曲线的ANN方法显示出更高的准确性.
- 这种新方法显示出比以前的ANN方法更好的性能,该方法单独估计了拉姆伯格-奥斯古德模型参数.
- 使用单调性质的直接估计可以减少与简化材料模型相关的不准确性.
结论:
- 通过ANNs直接估计循环应力-应变曲线使用单调性质是一个更有利和更准确的方法.
- 这种新的方法比传统的经验和参数估计技术有了显著的改进.
- 这些发现表明,在工程应用中,有了更可靠的疲劳寿命评估的途径.
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