Abstract A machine learning based model is proposed to describe the temperature and strain rate dependent response of polypropylene. A hybrid modeling approach is taken by combining mechanism-based and data-based modeling. The “big data” required for machine learning is generated using a custom-made robot-assisted testing system. Numerous large deformation experiments are performed on mildly-notched tensile specimens for temperatures ranging from 20 to 80 °C, and strain rates ranging from 10−3 to 10−1/s. Without making any a priori assumptions on the specific mathematical form, the function relating the stress to the viscous strain, the viscous strain rate and temperature is identified using machine learning. In particular, a back propagation algorithm with Bayesian regularization is employed to identify a suitable neural network function based on the results from more than 40 experiments. The neural network model is employed in series with a temperature-dependent spring to describe the stress-strain response of polypropylene. The resulting constitutive equations are solved numerically to demonstrate that the identified model is capable to predict the experimentally-observed stress-strain response for strains of up to 0.6.

中文翻译：

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描述聚丙烯的温度和速率相关应力应变响应的神经网络模型

摘要 提出了一种基于机器学习的模型来描述聚丙烯的温度和应变率相关响应。通过结合基于机制和基于数据的建模采用混合建模方法。机器学习所需的“大数据”是使用定制的机器人辅助测试系统生成的。在温度范围为 20 至 80 °C 和应变速率范围为 10-3 至 10-1/s 的温和缺口拉伸试样上进行了大量大变形实验。无需对特定数学形式进行任何先验假设，使用机器学习识别将应力与粘性应变、粘性应变率和温度相关联的函数。特别是，使用贝叶斯正则化的反向传播算法根据 40 多个实验的结果确定合适的神经网络函数。神经网络模型与温度相关弹簧串联使用，以描述聚丙烯的应力应变响应。对所得本构方程进行数值求解，以证明识别的模型能够预测实验观察到的应变为 0.6 的应力应变响应。