Abstract This study derives a uniaxial tensile flow from spherical indentation data using an artificial neural network (ANN) combined with finite element (FE) analysis. The feasibility of the FE-based simulations is confirmed through experimental indentation for various steels. Parametric studies of the FE simulation are performed to generate an ANN training database. An encoding for feature extraction and a hyperparameter optimization is implemented to design the ANN with high predictive performance. The indentation load–depth curves are converted into hardening parameters through the trained ANN. The predictive performance of the FE–ANN model using real-life indentation data is investigated in-depth with thorough error evaluation, and verified by uniaxial tensile tests. The emphasis is made that the mean absolute percentage error between the experimental and simulated indentation data is required to be meticulously controlled below 1% to accurately predict the tensile properties. The validations demonstrate that the applied FE–ANN modeling approach is very robust and captures the tensile properties well. Furthermore, the Taguchi orthogonal array (OA) method that can achieve high efficiency and fidelity with less training data is discussed. The FE–ANN model is concisely designed using the Taguchi OA method and can predict elasticity as well as plasticity.

中文翻译：

---

使用基于有限元的压痕和优化的人工神经网络预测单轴拉伸流

摘要 本研究使用人工神经网络 (ANN) 结合有限元 (FE) 分析从球形压痕数据导出单轴拉伸流。通过对各种钢的实验压痕证实了基于有限元模拟的可行性。执行有限元模拟的参数研究以生成人工神经网络训练数据库。实现了用于特征提取和超参数优化的编码，以设计具有高预测性能的 ANN。压痕载荷-深度曲线通过经过训练的人工神经网络转换为硬化参数。FE-ANN 模型使用真实压痕数据的预测性能通过彻底的误差评估进行了深入研究，并通过单轴拉伸试验进行了验证。强调需要将实验和模拟压痕数据之间的平均绝对百分比误差严格控制在 1% 以下，以准确预测拉伸性能。验证表明，所应用的 FE-ANN 建模方法非常稳健，并能很好地捕捉拉伸性能。此外，讨论了可以以较少的训练数据实现高效率和保真度的田口正交阵列（OA）方法。FE-ANN 模型是使用田口 OA 方法简洁设计的，可以预测弹性和塑性。此外，讨论了可以以较少的训练数据实现高效率和保真度的田口正交阵列（OA）方法。FE-ANN 模型是使用田口 OA 方法简洁设计的，可以预测弹性和塑性。此外，讨论了可以以较少的训练数据实现高效率和保真度的田口正交阵列（OA）方法。FE-ANN 模型是使用田口 OA 方法简洁设计的，可以预测弹性和塑性。