Abstract In this paper, a machine learning-based approach has been proposed to integrate artificial intelligence during the designing of fiber-reinforced polymeric composites. With the help of the proposed approach, an artificial neural network (ANN) model has been developed to achieve the targeted filler content for cotton fiber/polypropylene composite while satisfying the required targeted properties. Previously obtained experimental data sets were trained on the TensorFlow backend using Keras library in Python, followed by hyperparameter tuning and k-fold cross-validation method for acquiring a better performing model to predict the amount of targeted filler content. The developed approach proved to be very efficient and reduced the time and effort of the material characterization for numerous samples, and it will help materials designers to design their future experiments effectively. The developed approach in this paper can be extended for other composite materials if the necessary experimental data are available to train the ANN model.

中文翻译：

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基于人工神经网络力学性能的棉纤维/PP复合材料的最佳填料含量

摘要 在本文中，提出了一种基于机器学习的方法，以在纤维增强聚合物复合材料的设计过程中集成人工智能。借助所提出的方法，开发了人工神经网络 (ANN) 模型，以实现棉纤维/聚丙烯复合材料的目标填料含量，同时满足所需的目标特性。之前获得的实验数据集使用 Python 中的 Keras 库在 TensorFlow 后端进行训练，然后进行超参数调整和 k 折交叉验证方法，以获得性能更好的模型来预测目标填充物含量的数量。事实证明，所开发的方法非常有效，并减少了大量样品材料表征的时间和精力，它将帮助材料设计师有效地设计他们未来的实验。如果必要的实验数据可用于训练 ANN 模型，本文中开发的方法可以扩展到其他复合材料。