UC知道求材料方面英语论文翻译，急
来源：百度知道 编辑：UC知道 时间：2024/05/09 15:36:21
Gaussian Process Modelling of Austenite Formation in Steel
We introduce the Gaussian process model for the empirical modelling of the formation of austenite during the continuous heating of steels. A previous paper has examined the application of neural networks to this problem, but the Gaussian process model is a more general probabilistic model which avoids some of the arbitrariness of neural networks, and is somewhat more amenable to interpretation. We demonstrate that the model leads to an improvement in the significance of the trends of the Ac1 and Ac3 temperatures as a function of the chemical composition and heating rate. In some cases, these predicted trends are more plausible than those obtained with the neural network analysis. Additionally, we show that many of the trace alloying elements present in steels are irrelevant in determining the austenite formation temperatures.
We introduce the Gaussian process model for the empirical modelling of the formation of austenite during the continuous heating of steels. A previous paper has examined the application of neural networks to this problem, but the Gaussian process model is a more general probabilistic model which avoids some of the arbitrariness of neural networks, and is somewhat more amenable to interpretation. We demonstrate that the model leads to an improvement in the significance of the trends of the Ac1 and Ac3 temperatures as a function of the chemical composition and heating rate. In some cases, these predicted trends are more plausible than those obtained with the neural network analysis. Additionally, we show that many of the trace alloying elements present in steels are irrelevant in determining the austenite formation temperatures.
高斯过程中形成的造型的奥氏体钢
我们引进了高斯过程模型的实证模型的形成,在连续供暖的奥氏体钢。先前的应用进行了模糊神经网络来解决这个问题,但是高斯过程模型是一种更一般的概率模型,该模型避免一些任意的神经网络,现在可以解释。我们证实模式导致改善的趋势significance Ac1 Ac3温度作为函数,对材料的化学成分、升温速率。在一些案例中,这些预测趋势比那些获得更可信的神经网络进行分析。此外,我们发现许多微量合金元素在钢是不相干的确定奥氏体形成的温度。