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Open AccessArticle

Prediction of the Weld Qualities Using Surface Appearance Image in Resistance Spot Welding

1
Joining R&D Group, Korea Institute of Industrial Technology, 156 Gaetbeol-ro (Songdo-dong), Yeonsu-Gu, Incheon 21999, Korea
2
Carbon & Light Materials Application R&D Group, Korea Institute of Industrial Technology, Jeonju 54853, Korea
*
Author to whom correspondence should be addressed.
Metals 2019, 9(8), 831; https://doi.org/10.3390/met9080831
Received: 10 July 2019 / Revised: 23 July 2019 / Accepted: 25 July 2019 / Published: 26 July 2019
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Abstract

The quality of the resistance spot weld is predicted qualitatively using information from the weld’s external apparent image. The predicting tool used for weld qualities was a convolution neural network (CNN) algorithm with excellent performance in pattern recognition. A heat trace image of the weld surface was used as information on the external apparent image of welds. The materials used in the experiment were advanced high strength steel (AHSS) with 980 MPa strength, and uncoated cold-rolled (CR) steel sheets and galvannealed (GA) steel sheets were used. The quantitatively predicted weld quality information contained tensile shear strength, nugget diameter, fracture mode of welds, and expulsion occurrence. The predicted performance of the verification step of the model determined through the learning process was as follows; the predicted error rate for tensile shear strength and nugget diameter were 2.2% and 2.6%, respectively. And the predicted accuracy on fracture mode and expulsion occurrence was 100%. View Full-Text
Keywords: resistance spot welding; weld quality convolution neural network; surface appearance image resistance spot welding; weld quality convolution neural network; surface appearance image
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Choi, S.-G.; Hwang, I.; Kim, Y.-M.; Kang, B.; Kang, M. Prediction of the Weld Qualities Using Surface Appearance Image in Resistance Spot Welding. Metals 2019, 9, 831.

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