Vanerio Daniele, Guagliano Mario, Bagherifard Sara
Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy.
R&D Department, Caracol AM S.r.l., Barlassina, Italy.
NPJ Adv Manuf. 2025;2(1):8. doi: 10.1038/s44334-025-00018-z. Epub 2025 Mar 18.
This study investigates an artificial neural network (ANN) for predicting cross-sectional geometry in fused granulate fabrication (FGF), a key polymer-based technology in large-format additive manufacturing (LFAM). Critical process parameters-layer height, transverse speed, and screw speed-were systematically varied to study their effects on bead morphology. A full factorial design generated a robust training dataset, and cross-sectional images were processed for model training. The ANN architecture, featuring two hidden layers, was paired with image processing techniques to manage computational demands. Results showed strong agreement between predicted and experimental cross-sections, with a mean absolute error of 8.88%, highlighting the ANN's capability in capturing geometry. This approach advances prior LFAM studies by predicting full cross-sectional images rather than contour points, improving complex shape prediction. The findings demonstrate the ANN's effectiveness for FGF profiles and its potential to enhance geometric precision and generate complex shapes across LFAM technologies.
本研究探究了一种人工神经网络（ANN），用于预测熔融颗粒制造（FGF）中的横截面几何形状，FGF是大幅面增材制造（LFAM）中一项关键的基于聚合物的技术。系统地改变了关键工艺参数——层高、横向速度和螺杆速度，以研究它们对熔珠形态的影响。全因子设计生成了一个强大的训练数据集，并对横截面图像进行处理以进行模型训练。具有两个隐藏层的ANN架构与图像处理技术相结合，以管理计算需求。结果表明，预测横截面与实验横截面之间具有很强的一致性，平均绝对误差为8.88%，突出了ANN捕捉几何形状的能力。这种方法通过预测完整的横截面图像而非轮廓点，改进了复杂形状预测，从而推进了先前的LFAM研究。研究结果证明了ANN对FGF轮廓的有效性及其在提高几何精度和在LFAM技术中生成复杂形状方面的潜力。
