Purpose

Viscosity is an important basic physical property of liquid solders. However, because of the very complex nonlinear relationship between the viscosity of the liquid ternary Sn-based lead-free solder and its determinants, a theoretical model for the viscosity of the liquid Sn-based solder alloy has not been proposed. This paper aims to address the viscosity issues that must be considered when developing new lead-free solders.

Design/methodology/approach

A BP neural network model was established to predict the viscosity of the liquid alloy and the predicted values were compared with the corresponding experimental data in the literature data. At the same time, the BP neural network model is compared with the existing theoretical model. In addition, a mathematical model for estimating the melt viscosity of ternary tin-based lead-free solders was constructed using a polynomial fitting method.

Findings

A reasonable BP neural network model was established to predict the melt viscosity of ternary tin-based lead-free solders. The viscosity prediction of the BP neural network agrees well with the experimental results. Compared to the Seetharaman and the Moelwyn–Hughes models, the BP neural network model can predict the viscosity of liquid alloys without the need to calculate the relevant thermodynamic parameters. In addition, a simple equation for estimating the melt viscosity of a ternary tin-based lead-free solder has been proposed.

Originality/value

The study identified nine factors that affect the melt viscosity of ternary tin-based lead-free solders and used these factors as input parameters for BP neural network models. The BP neural network model is more convenient because it does not require the calculation of relevant thermodynamic parameters. In addition, a mathematical model for estimating the viscosity of a ternary Sn-based lead-free solder alloy has been proposed. The overall research shows that the BP neural network model can be well applied to the theoretical study of the viscosity of liquid solder alloys. Using a constructed BP neural network to predict the viscosity of a lead-free solder melt helps to study the liquid physical properties of lead-free solders that are widely used in electronic information.

中文翻译：

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基于BP神经网络的三元锡基无铅焊料熔体粘度预测。

目的

粘度是液体焊料的重要基本物理性质。然而，由于液态三元锡基无铅焊料的粘度与其决定因素之间非常复杂的非线性关系，因此尚未提出液态锡基焊料合金的粘度的理论模型。本文旨在解决开发新的无铅焊料时必须考虑的粘度问题。

设计/方法/方法

建立了BP神经网络模型来预测液态合金的粘度，并将预测值与文献数据中的相应实验数据进行比较。同时，将BP神经网络模型与现有的理论模型进行了比较。另外，使用多项式拟合方法构建了用于估计三元锡基无铅焊料的熔融粘度的数学模型。

发现

建立了合理的BP神经网络模型来预测三元锡基无铅焊料的熔融粘度。BP神经网络的粘度预测与实验结果吻合良好。与Seetharaman模型和Moelwyn-Hughes模型相比，BP神经网络模型可以预测液态合金的粘度，而无需计算相关的热力学参数。另外，已经提出了用于估计三元锡基无铅焊料的熔融粘度的简单方程式。

创意/价值

该研究确定了影响三元锡基无铅焊料熔融粘度的九个因素，并将这些因素用作BP神经网络模型的输入参数。BP神经网络模型更加方便，因为它不需要计算相关的热力学参数。另外，已经提出了用于估计三元Sn基无铅焊料合金的粘度的数学模型。整体研究表明，BP神经网络模型可以很好地应用于液态焊料合金粘度的理论研究。使用构造的BP神经网络预测无铅焊料熔体的粘度有助于研究广泛用于电子信息中的无铅焊料的液体物理性质。