Artificial neural networks (ANN) are developed and employed to characterize a wide range of metallic materials. Focus is given to the evaluation of stress–strain behavior via sphere-to-flat indentation. Each ANN is trained using a supervised machine learning procedure comprised of two steps: (i) generation of a training dataset via calibrated finite element model, and (ii) validation using experimental data retrieved from indentations tests. The developed frameworks aim to establish a fast and low-cost tool for the assessment of loading conditions in industrial applications. The best proposed solution is able to predict stress–strain behavior with a quasi-instantaneous response and errors of less than 3%. Moreover, outputs are attained with minute costs (processing and memory bandwidth) when compared to finite element simulations.

人工神经网络(ANN) 被开发并用于表征各种金属材料。重点是通过球体到平面的压痕评估应力-应变行为。每个人工神经网络都使用监督机器学习程序进行训练，该程序包括两个步骤：（i）通过校准的有限元模型生成训练数据集，以及（ii）使用从压痕测试中检索到的实验数据进行验证。开发的框架旨在建立一种快速且低成本的工具来评估工业应用中的负载条件。提出的最佳解决方案能够以准瞬时响应和小于 3% 的误差来预测应力应变行为。此外，与有限元模拟相比，可以以极低的成本（处理和内存带宽）获得输出。