Journal
SCIENCE AND TECHNOLOGY OF WELDING AND JOINING
Volume 25, Issue 4, Pages 282-289Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/13621718.2019.1687635
Keywords
Submerged arc welding; carbon steel; acicular ferrite; fraction; segmentation; deep learning; fully convolutional network; ResNet
Funding
- National Research Foundation of Korea (NRF) - Korea government (MSIT) [2017R1C1B5018334]
- Korea Institute of Energy Technology Evaluation and Planning (KETEP)
- Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea [201810102360]
- Korea Aerospace University [2019-01-006]
- National Research Foundation of Korea [2017R1C1B5018334] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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In this study, microstructures of weldment produced using carbon steel A516 grade 60 were analysed via a deep learning approach to measure the fraction of acicular ferrite which considerably influences on mechanical properties of carbon steel. The fully convolutional network was used to conduct the image segmentation. Submerged arc welding was used for welding, and the dataset was constructed using optical microscope. The model was compiled with ResNet, which is the state-of-the-art classifier used as an encoder. The model is trained to distinguish acicular ferrite from microstructures of dataset images and then estimate its accuracy. As a result, the mean intersection over union, which is a metric commonly used to evaluate image segmentation, was shown to be higher than 85%.
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