Ultra-fine glass fiber felt (fiber diameter ⩽3 μm) is prepared by the flame blowing process with superior thermal insulation and sound insulation. It is widely used in construction and aerospace by improving its uniformity and fiber diameter to further enhance its thermal and acoustic insulation properties. In this article, the purpose is further to create a smart manufacturing system using artificial neural network to provide analysis, judgment, and optimization for the manufacture of aerospace-grade ultra-fine glass fiber felt. When there were 11 neurons in the hidden layer, both the relative error Z values of the uniformity and the fiber diameter were the smallest, which were 0.0382 and 0.0073, respectively. So the structure 3−[11]1–2 with the back-propagation training algorithm was the most adaptive model, which was proved by comparing the mean relative error. In addition, after comparison with the measured data, the predicted and measured values are very similar and the error between them is small, so this structure has been confirmed to have a high accuracy. Finally, three-dimensional planes for the predicted uniformity and fiber diameter as a function of each process parameters are established. The predictive quality was pretty satisfactory, which can be applied to predict new data in the same knowledge domain.

中文翻译：

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基于人工神经网络的超细玻璃纤维毡工艺参数优化与预测

超细玻璃纤维毡（纤维直径⩽3 μm）采用火焰吹制工艺制备，具有优异的隔热和隔音性能。它通过改善其均匀性和纤维直径以进一步增强其隔热和隔音性能而广泛用于建筑和航空航天。在本文中，旨在进一步打造一个利用人工神经网络的智能制造系统，为航空级超细玻璃纤维毡的制造提供分析、判断和优化。当隐藏层有11个神经元时，均匀性和纤维直径的相对误差Z值最小，分别为0.0382和0.0073。所以带有反向传播训练算法的结构 3−[11]1-2 是适应性最强的模型，这是通过比较平均相对误差来证明的。另外，经过与实测数据的比较，预测值与实测值非常相似，误差很小，因此该结构已被证实具有较高的精度。最后，建立预测的均匀性和纤维直径作为每个工艺参数的函数的三维平面。预测质量相当令人满意，可用于预测同一知识领域的新数据。建立了作为每个工艺参数函数的预测均匀性和纤维直径的三维平面。预测质量相当令人满意，可用于预测同一知识领域的新数据。建立了作为每个工艺参数函数的预测均匀性和纤维直径的三维平面。预测质量相当令人满意，可用于预测同一知识领域的新数据。