沉积(地质)
扫描仪
图层(电子)
人工神经网络
导线
直线(几何图形)
能量(信号处理)
计算机科学
激光扫描
人工智能
几何学
算法
光学
激光器
材料科学
地质学
数学
物理
复合材料
古生物学
统计
大地测量学
沉积物
作者
Liu Yang,Hoon Sohn,Zhanxiong Ma,Ikgeun Jeon,Peipei Liu,Jack C.P. Cheng
标识
DOI:10.1016/j.compind.2023.103882
摘要
Metal additive manufacturing (AM), such as laser direct energy deposition (DED), is gaining popularity because of its capability in manufacturing near-net-shaped complex components for various industrial applications. However, the geometry control during the DED process, especially at corners with sharp turns, remains a daunting task. To achieve geometry control, geometry estimation to identify the relationship between the process parameters and geometry attributes is vital. In this study, a real-time layer height estimation technique is developed for DED using a laser line scanner, vision camera, and domain adaptive neural networks (DaNN). An emphasis is placed on layer height estimation at sharp corners during multi-layer deposition. First, multi-layer straight-line deposition data is collected using laser line scanner and an initial layer height estimation model is constructed. Then, to efficiently achieve layer height estimation during corner deposition, an DaNN model is established using the multi-layer straight-line deposition data and the constructed initial model. The actual traverse speed at the corners is measured using a vision camera and fed into the DaNN model as one of input features. Finally, the DaNN model is updated online to further improve estimation accuracy during corner deposition. The proposed technique has been validated by DED experiments and the results show that the layer height can be estimated in 0.018 s with an average accuracy of 25.7 µm when multiple layers with an average height of 250 µm are deposited at corners with different angles.
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