Abstract We introduce an augmentation approach for the prediction of phase transformation temperatures that combines thermodynamic considerations and thermodynamic databases. Using the example of the bainitic start temperature, B s , we demonstrate the improvement of prediction accuracy that this augmentation scheme can provide. The training and testing dataset available from already published experimental measurements provides a varying set of alloying elements and measured bainitic start temperatures. In terms of a minimalistic thermodynamic model, we explain the benefit of augmenting the presented data set by the chemical potential of carbon in the ferritic phase μ α at an estimated start temperature. To evaluate this augmentation scheme, we determine the prediction accuracy of sets of artificial neural networks (ANNs) for the unaugmented dataset, for the – only a posteriori accessible – dataset which is augmented with the chemical potential at the measured bainitic start temperature, and the prediction accuracy for the dataset augmented by an estimated μ α , approximated with two different approaches. While the dataset which is augmented with the chemical potential at the measured bainitic start temperatures would not be practically usable for the prediction of a not yet measured bainitic start temperature, it provides theoretical limits of the achievable accuracy gain due to the augmentation. The developed approximation schemes for μ α at B s are usable to predict B s for a given composition. We distinguish two levels of computational expense, which provide a mean absolute error of either about 14 °C or about 4 °C, thus reaching the regime of experimental measurement accuracy.

中文翻译：

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一种用于钢中相变温度预测的实用数据集增强方法

摘要 我们介绍了一种结合热力学考虑和热力学数据库的相变温度预测增强方法。使用贝氏体起始温度 B s 的例子，我们证明了这种增强方案可以提供的预测精度的提高。从已发表的实验测量中获得的训练和测试数据集提供了一组不同的合金元素和测量的贝氏体起始温度。在极简热力学模型方面，我们解释了在估计的起始温度下通过铁素体相 μ α 中碳的化学势来增加所呈现数据集的好处。为了评估这个增强方案，我们确定了一组人工神经网络 (ANN) 对未增强数据集的预测精度，对于仅后验可访问的数据集，该数据集在测量的贝氏体起始温度下增加了化学势，并且数据集的预测精度增强了通过估计的 μ α ，用两种不同的方法近似。虽然在测量的贝氏体起始温度下用化学势增强的数据集实际上不能用于预测尚未测量的贝氏体起始温度，但它提供了由于增强而可实现的精度增益的理论限制。在 B s 处开发的 μ α 近似方案可用于预测给定成分的 B s。我们区分两个级别的计算费用，