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Vision-based melt pool monitoring for wire-arc additive manufacturing using deep learning method

Journal Article


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.

Publication Date


  • 2022

Citation


  • Xia, C., Pan, Z., Li, Y., Chen, J., & Li, H. (2022). Vision-based melt pool monitoring for wire-arc additive manufacturing using deep learning method. International Journal of Advanced Manufacturing Technology, 120(1-2), 551-562. doi:10.1007/s00170-022-08811-2

Scopus Eid


  • 2-s2.0-85123986206

Start Page


  • 551

End Page


  • 562

Volume


  • 120

Issue


  • 1-2

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.

Publication Date


  • 2022

Citation


  • Xia, C., Pan, Z., Li, Y., Chen, J., & Li, H. (2022). Vision-based melt pool monitoring for wire-arc additive manufacturing using deep learning method. International Journal of Advanced Manufacturing Technology, 120(1-2), 551-562. doi:10.1007/s00170-022-08811-2

Scopus Eid


  • 2-s2.0-85123986206

Start Page


  • 551

End Page


  • 562

Volume


  • 120

Issue


  • 1-2