The study of the impact phenomenon on rubber-like materials has been traditionally related to lumped parameter modeling or discrete Finite Element models that require experimentation associated with the material behavior at a level of constitutive modeling, and additional testing to validate their operation in case of engineering applications. This article presents an Artificial Neural Network approach to predict and simulate the low-velocity impact force in a thermoplastic elastomer material. Neural network models were trained and validated with experimental data obtained from impact tests in a modified Charpy apparatus. An experimental setup and a data acquisition procedure were set out to record the impact forces on elastomer specimens. The coefficient of determination R2, the Root Mean Square Error, and the Maximum Absolute Error measures were implemented as error functions to evaluate the performance of the neural networks regarding experimental data. Results show that the proposed method helps to predict and derive impact force curves within the range of the training data, since errors below 1% regarding experimental values were obtained. The results also demonstrate that the neural networks can simulate impact force curves within the range of the experimental values without the need to involve parameters of material strain-rate sensitivity. In addition, the approach was tested in another material, and the corresponding results show good prediction capabilities since errors below 1% were obtained. Therefore, it is concluded that the presented artificial neural models, and the approach, could be useful to create solution spaces for low-velocity impact responses of thermoplastic elastomers.

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一种预测弹性体材料中低速冲击力的人工神经网络方法

对橡胶类材料的冲击现象的研究传统上与集总参数建模或离散有限元模型相关，这些模型需要在本构建模水平上进行与材料行为相关的实验，并在工程情况下进行额外的测试以验证其操作应用程序。本文介绍了一种人工神经网络方法来预测和模拟热塑性弹性体材料中的低速冲击力。神经网络模型使用从改进的夏比装置中的冲击测试中获得的实验数据进行训练和验证。设置了实验装置和数据采集程序以记录对弹性体试样的冲击力。决定系数 R2，均方根误差，并且最大绝对误差测量被实施为误差函数，以评估神经网络关于实验数据的性能。结果表明，所提出的方法有助于预测和推导出训练数据范围内的冲击力曲线，因为获得了低于 1% 的实验值误差。结果还表明，神经网络可以在实验值范围内模拟冲击力曲线，而无需涉及材料应变率敏感性参数。此外，该方法在另一种材料中进行了测试，由于获得了低于 1% 的误差，因此相应的结果显示出良好的预测能力。因此，得出的结论是，所提出的人工神经模型和方法，