Ikeuchi Daiki, Vargas-Uscategui Alejandro, Wu Xiaofeng, King Peter C
School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, NSW 2006, Australia.
Commonwealth Scientific and Industrial Research Organisation Manufacturing, Private Bag 10, Clayton, VIC 3169, Australia.
Materials (Basel). 2019 Sep 2;12(17):2827. doi: 10.3390/ma12172827.
Cold spray additive manufacturing is an emerging technology that offers the ability to deposit oxygen-sensitive materials and to manufacture large components in the solid state. For further development of the technology, the geometric control of cold sprayed components is fundamental but not yet fully matured. This study presents a neural network predictive modelling of a single-track profile in cold spray additive manufacturing to address the problem. In contrast to previous studies focusing only on key geometric feature predictions, the neural network model was employed to demonstrate its capability of predicting complete track profiles at both normal and off-normal spray angles, resulting in a mean absolute error of 8.3%. We also compared the track profile modelling results against the previously proposed Gaussian model and showed that the neural network model provided comparable predictive accuracy, even outperforming in the predictions at cold spray profile edges. The results indicate that a neural network modelling approach is well suited to cold spray profile prediction and may be used to improve geometric control during additive manufacturing with an appropriate process planning algorithm.
冷喷涂增材制造是一项新兴技术，它能够沉积对氧敏感的材料，并以固态方式制造大型部件。对于该技术的进一步发展而言，冷喷涂部件的几何控制至关重要，但尚未完全成熟。本研究提出了一种用于冷喷涂增材制造中单道轮廓的神经网络预测模型，以解决这一问题。与以往仅关注关键几何特征预测的研究不同，该神经网络模型用于展示其在正常和非正常喷涂角度下预测完整道次轮廓的能力，平均绝对误差为8.3%。我们还将道次轮廓建模结果与先前提出的高斯模型进行了比较，结果表明神经网络模型具有相当的预测精度，甚至在冷喷涂轮廓边缘的预测中表现更优。结果表明，神经网络建模方法非常适合冷喷涂轮廓预测，并且可以通过适当的工艺规划算法用于改善增材制造过程中的几何控制。
