Model summary for E.

The application of additive manufacturing technologies for producing parts from polymer composite materials has gained significant attention due to the ability to create fully functional components that leverage the advantages of both polymer matrices and fiber reinforcements while maintaining the benefits of additive technology. Polymer composites are among the most advanced and widely used composite materials, offering high strength and stiffness with low mass and variable resistance to different media. This study aims to experimentally investigate the impact of selected process parameters, namely, wall thickness, raster angle, printing temperature, and build plate temperature, on the flexural properties of carbon fiber reinforced polyamide (CFrPA) fused deposition modeling (FDM) printed samples, as per ISO 178 standards. Additionally, regression and artificial neural network (ANN) models have been developed to predict these flexural properties. ANN models are developed for both normal and augmented inputs, with the architecture and hyperparameters optimized using random search technique. Response surface methodology (RSM), which is based on face centered composite design, is employed to analyze the effects of process parameters. The RSM results indicate that the raster angle and build plate temperature have the greatest impact on the flexural properties, resulting in an increase of 51% in the flexural modulus. The performance metrics of the optimized RSM and ANN models, characterized by low MSE, RMSE, MAE, and MAPE values and high R2 values, suggest that these models provide highly accurate and reliable predictions of flexural strength and modulus for the CFrPA material. The study revealed that ANN models with augmented inputs outperform both RSM models and ANN models with normal inputs in predicting these properties.

以聚合物复合材料（polymer composite materials）为原料制备构件的增材制造（additive manufacturing）技术，凭借可制备兼具聚合物基体与纤维增强体优势、同时保留增材制造技术益处的全功能构件这一特性，已受到广泛关注。聚合物复合材料作为当前最先进且应用最广泛的复合材料之一，兼具高强度、高刚度与低质量的特性，且对不同介质具备可变耐受性。本研究旨在依据ISO 178标准，通过实验探究选定工艺参数——即壁厚、填充角度（raster angle）、打印温度及热床温度（build plate temperature）——对碳纤维增强聚酰胺（CFrPA）熔融沉积成型（FDM）试样的弯曲性能的影响。此外，本研究还构建了回归分析与人工神经网络（ANN）模型，用于预测该类弯曲性能。针对常规输入与增强输入两种场景，本研究分别构建了人工神经网络模型，并采用随机搜索技术（random search technique）优化模型架构与超参数。本研究采用基于面中心复合设计的响应面法（RSM）分析工艺参数的影响效应。响应面法结果表明，填充角度与热床温度对弯曲性能的影响最为显著，可使弯曲模量提升51%。经低均方误差（Mean Squared Error, MSE）、均方根误差（Root Mean Squared Error, RMSE）、平均绝对误差（Mean Absolute Error, MAE）与平均绝对百分比误差（Mean Absolute Percentage Error, MAPE）以及高决定系数（Coefficient of Determination, R²）表征的优化后响应面法与人工神经网络模型的性能指标显示，上述模型可对碳纤维增强聚酰胺材料的弯曲强度与弯曲模量实现高精度且可靠的预测。研究还发现，采用增强输入的人工神经网络模型在预测该类性能时，性能优于响应面法模型与采用常规输入的人工神经网络模型。