Abstract
Wire-arc additive manufacturing (WAAM) technology has been widely recognized as a promising alternative for fabricating large-scale components, due to its advantages of high deposition rate and high material utilization rate. However, some anomalies may occur during the deposition process, such as humping, spattering, robot suspend, pores, cracking and so on. This study proposed to apply deep learning in the visual monitoring to diagnose different anomalies during WAAM process. The melt pool images of different anomalies were collected for training and validation by a visual monitoring system. The classification performance of several representative CNN (convolutional neural network) architectures, including ResNet, EfficientNet, VGG-16 and GoogLeNet, were investigated and compared. The classification accuracy of 97.62%, 97.45%, 97.15% and 97.25% was achieved by each model. The results proved that the CNN models are effective in classifying different types of melt pool images of WAAM. Our study is applicable beyond WAAM and should benefit other additive manufacturing or arc welding techniques.
Highlights
As a direct energy deposition (DED) process, Wire-arc additive manufacturing (WAAM) has emerged as a suitable alternative for fabricating mediumto-large size metal components [1,2,3]
The types of electrical arc adopted in WAAM mainly include gas metal arc (GMA), gas tungsten arc (GTA) and plasma arc (PA)
The classification accuracies of GoogLeNet, VGG-16, ResNet and EfficientNet trained by transfer learning are all above 97% (97.25%, 97.15%, 97.62%, and 97.45% respectively), and ResNet achieves the highest accuracy among those models, which is 97.62%
Summary
As a direct energy deposition (DED) process, WAAM has emerged as a suitable alternative for fabricating mediumto-large size metal components [1,2,3]. WAAM employs an electrical arc as the heat source to fuse welding wire and deposits layer-by-layer to form a three-dimensional object [4]. The types of electrical arc adopted in WAAM mainly include gas metal arc (GMA), gas tungsten arc (GTA) and plasma arc (PA). Compared to other forms of additive manufacturing, the major advantages of WAAM are its high deposition rate and high material utilization [5]. The deposition rate for laser and electron beam additive manufacturing is about 2–10 g/min, while the deposition rate of WAAM could reach above 160 g/min [6].
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