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Machine learning-based predictive modeling of contact heat transfer

机译：基于机器学习的接触传热预测建模

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Heat transfer phenomena at the interface between two contacting solids are highly complex involving multiple influencing factors. Over the years, a large amount of experiments were carried out to determine the contact heat transfer coefficients between two dissimilar joint materials. However, there are still no existing theoretical or physics-based models that satisfactorily predict the contact heat transfer coefficients. By taking advantage of the existing data, in contrast, machine learning promises a powerful method, capable of predicting the contact heat transfer coefficients for different material pairs and contact conditions. This research introduces a robust machine learning-based model that succeeds in precisely estimating the heat transfer across the interfaces between glass and steel, a material pair widely used in hot forming of glass. The data used for training and validating the machine learning models were determined experimentally by means of infrared thermography. The datasets consisted of contact heat transfer coefficients with dependence on three factors - interfacial temperature, contact pressure, and surface finishes. Aim of this study is to analyze the prediction accuracy and interpretability of various supervised learning algorithms in order to realize the machine learning models that are able to capture the underlying physics governing the heat transfer phenomena at the glass-mold interface. Finally, the results were compared with those estimated by a theoretical model and a numerical simulation model. The comparison demonstrates enhancements in prediction accuracy enabled by the data-driven method. This study indicates accurate and efficient strategies for solving thermal problems in hot glass forming processes.
机译：在两个接触固体之间的界面处的传热现象是高度复杂的涉及多种影响因素。多年来，进行了大量的实验，以确定两个不同的接合材料之间的接触传热系数。然而，仍然仍然没有现有的基于理论或物理学的模型，其令人满意地预测接触传热系数。通过利用现有数据，相比之下，机器学习承诺是一种强大的方法，能够预测不同材料对和接触条件的接触传热系数。本研究介绍了一种坚固的机器学习的模型，其成功地精确地估计了玻璃和钢之间的接口的热传递，这是一种广泛用于玻璃热成形的材料对。用于训练和验证机器学习模型的数据通过红外热成像实验确定。数据集由接触传热系数组成，依赖于三种因素 - 界面温度，接触压力和表面光洁度。本研究的目的是分析各种监督学习算法的预测准确性和可解释性，以实现能够在玻璃模具界面处捕获控制传热现象的底层物理学的机器学习模型。最后，将结果与理论模型和数值模拟模型进行了比较。比较显示了数据驱动方法启用的预测精度的增强。本研究表明了求解热玻璃形成过程中热问题的准确和有效的策略。

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