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

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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.

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Funding

China Scholarship Council (NO. 201704910782) and National Natural Science Foundation of China (No. 51775313) provided financial support.

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Authors and Affiliations

  1. Key Laboratory for Liquid-Solid Structure Evolution and Materials Processing, Institute of Materials Joining, Shandong University, Jinan, 250061, China

    Chunyang Xia & Ji Chen

  2. School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong, Wollongong, NSW, 2522, Australia

    Chunyang Xia, Zengxi Pan, Yuxing Li & Huijun Li

Authors
  1. Chunyang Xia
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  2. Zengxi Pan
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  3. Yuxing Li
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  4. Ji Chen
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  5. Huijun Li
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Contributions

All the authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Chunyang Xia, Zengxi Pan and Yuxing Li. The first draft of the manuscript was written by Chunyang Xia, and all the authors commented on previous versions of the manuscript. Ji Chen helped improve the language and figures when revising the manuscript. All the authors read and approved the final manuscript.

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Correspondence to Chunyang Xia.

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Xia, C., Pan, Z., Li, Y. et al. Vision-based melt pool monitoring for wire-arc additive manufacturing using deep learning method. Int J Adv Manuf Technol 120, 551–562 (2022). https://doi.org/10.1007/s00170-022-08811-2

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  • DOI: https://doi.org/10.1007/s00170-022-08811-2

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