Properties Prediction and Design of Tire Tread Composites Using Machine Learning,Macromolecular Theory and Simulations

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Properties Prediction and Design of Tire Tread Composites Using Machine Learning
Macromolecular Theory and Simulations ( IF 1.8 ) Pub Date : 2020-02-26 , DOI: 10.1002/mats.201900063
Song Pang _{1,

2} , Jinlian Luo ₃ , Youping Wu _{1,

4}

Affiliation

The present study focuses on exploring the relationship between various properties of tire tread composites and filler system using machine learning. Four different types of machine learning algorithms, such as multiple linear regression (MLR), artificial neural network (ANN), support vector machine regression (SVR), and classification and regression tree, are used for predicting 0 °C tanδ, 60 °C tanδ, tensile strength, and Shore A hardness of natural rubber nanocomposites from carbon nanotubes dosage, silica dosage, and total filler equivalent. The results showed that the introduction of interaction terms and square terms into the inputs evidently improved the prediction capability of MLR, ANN and SVR, and MLR possessed the smallest prediction errors (<5%). The established MLR models are further used to design tire tread composites with high 0 °C tanδ, low 60 °C tanδ, and appropriate Shore A hardness and tensile strength. The predicted values are in good agreement with the experimental results, indicating that the established MLR models can be used for properties prediction and design of tire tread composites effectively. Moreover, k‐fold cross‐validation is proved to be a reliable technique to evaluate the predictive capability of the MLR models.

中文翻译：

基于机器学习的轮胎胎面复合材料性能预测与设计

本研究着重探讨使用机器学习的轮胎胎面复合材料的各种性能与填充系统之间的关系。四种不同类型的机器学习算法，例如多元线性回归（MLR），人工神经网络（ANN），支持向量机回归（SVR）以及分类和回归树，可用于预测0°Ctanδ，60°C天然橡胶纳米复合材料的tanδ，拉伸强度和肖氏A硬度取决于碳纳米管的用量，二氧化硅的用量和总填料当量。结果表明，在输入中引入交互项和平方项明显提高了MLR，ANN和SVR的预测能力，而MLR的预测误差最小（<5％）。已建立的MLR模型可进一步用于设计tanδ高0°C，tanδ低60°，肖氏A硬度和拉伸强度合适的轮胎胎面复合材料。预测值与实验结果吻合良好，表明所建立的MLR模型可有效地用于轮胎胎面复合材料的性能预测和设计。此外，k倍交叉验证被证明是评估MLR模型预测能力的可靠技术。

更新日期：2020-02-26

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