Monticeli Francisco M, Neves Roberta M, Ornaghi Heitor L, Almeida José Humberto S
Department of Aeronautical Engineering, Technological Institute of Aeronautics (ITA), São José dos Campos 30161-970, Brazil.
PGPROTEC, University of Caxias do Sul, Caxias do Sul 95070-560, Brazil.
Polymers (Basel). 2022 Sep 4;14(17):3668. doi: 10.3390/polym14173668.
The effects of processing parameters on conventional molding techniques are well-known. However, the fabrication of a carbon fibre (CF)/epoxy composite via additive manufacturing (AM) is in the early development stages relative to fabrications based on resin infusion. Accordingly, we introduce predictions of the flexural strength, modulus, and strain for high-performance 3D printable CF/epoxy composites. The data prediction is analyzed using approaches based on an artificial neural network, analysis of variance, and a response surface methodology. The predicted results present high reliability and low error level, getting closer to experimental results. Different input data can be included in the system with the trained neural network, allowing for the prediction of different output parameters. The following factors that influence the AM composite processing were considered: vacuum pressure, printing speed, curing temperature, printing space, and thickness. We further demonstrate fast and streamlined fabrications of various composite materials with tailor-made properties, as the influence of each processing parameter on the desirable properties.
加工参数对传统成型技术的影响是众所周知的。然而，相对于基于树脂灌注的制造方法，通过增材制造（AM）制备碳纤维（CF）/环氧树脂复合材料仍处于早期发展阶段。因此，我们介绍了高性能3D可打印CF/环氧树脂复合材料的弯曲强度、模量和应变的预测。使用基于人工神经网络、方差分析和响应面方法的方法对数据预测进行了分析。预测结果具有高可靠性和低误差水平，与实验结果更为接近。经过训练的神经网络可以将不同的输入数据纳入系统，从而实现对不同输出参数的预测。考虑了以下影响增材制造复合材料加工的因素：真空压力、打印速度、固化温度、打印间距和厚度。我们进一步展示了具有定制性能的各种复合材料的快速且简化的制造过程，以及每个加工参数对所需性能的影响。
