Manufacturing technologies
Non-contact inspection, Quality assurance, Traceability

Burn-through prediction and weld depth estimation by deep learning model
monitoring the molten pool in gas metal arc welding with gap fluctuation
Division of Materials and Manufacturing Science, Graduate School of Engineering
Associate Professor Kazufumi Nomura
https://researchmap.jp/kzfmnmr?lang=en

Abstract
In a single bevel gas metal arc welding with gap fluctuation, a
deep learning model was constructed using the monitoring image during the welding to predict the welding quality. We utilized
Python and the library Keras and created a CNN (Convolutionalneural network) model using the top surface image including themolten pool as an input. The classification model was used to predict the burn-through, and the regression model was used to estimate the penetration depth. As a result, the excessive penetration
and burn-through could be predicted in advance and more than
95% of estimated results of penetration depth were less 1 mm error
for stepped and tapered sample shapes.

Manufacturing technologies▍ Background & Results
The arc welding method is an indispensable technique in manufacturing industrial fields because of its usability and high energy
efficiency among other joining processes. However, in actual, many
problems remain due to various disturbances and uncertainties associated with the arc welding process, and a complete guarantee
of joint performance has not yet been accomplished. For example,
an uncontrollable gap occurs between the base metals due to theinfluence of the component accuracy and welding thermal strain.
As a result, welding defects such as excessive penetration andburn-through may occur on the backside of the plate. In addition to
the presence or absence of burn-through, penetration depth, which
is an important indicator of welded joint performance, becomes
unstable for the same reason.
In this study, the welding quality was predicted or estimated by
the CNN model using the molten pool image obtained by a single
camera for the single bevel groove gas metal arc welding including the gap fluctuation of tapered or stepped. The input image wasa top-surface image including the molten pool obtained using aband-pass filter of 980 nm every 0.1 sec during the welding. First,
we constructed a burn-through prediction model as a classification problem, thereby, excessive penetration and burn-throughcould be predicted in advance. In addition, we constructed thepenetration depth estimation model as a regression problem. As aresult of the estimation of each test sample by using training data
composed of both tapered and stepped gap samples, it was foundthat approximately more than 95% were less 1 mm error, and morethan 87% were less 0.5 mm error for both shapes. They were goodestimation results.

▍ Significance of the research and Future perspective
This study can estimate the invisible welding quality only bymonitoring the arc process with one camera, and it can be applied
to various welding processes other than the targeted joint in thisstudy. The output is expected to be expanded not only to the penetration state but also to the presence or absence of defects. Thistechnique will be an innovative monitoring tool for quality assurance of welding work.

P a t e n t
Nomura, Kazufumi; Matsumura, Takumi; Asai, Satoru et al. Burn-through prediction and weld depth estimation by deep learning model
monitoring the molten pool in gas metal arc welding with gap fluctuation, JOURNAL OF MANUFACTURING PROCESSES, 61 (2021) 590-600.
T r e a t i s e doi: 10.1016/j.jmapro.2020.10.019
Nomura, Kazufumi; Matsumura, Takumi; Asai, Satoru. Study on Weld Depth Estimation by Molten Pool Monitoring and Deep Learning Model in MAG
Welding Process(Review article), JOURNAL OF THE JAPAN WELDING SOCIETY, 90, 8 (2021) 13-17 (in Japanese)
URL http://www.mapse.eng.osaka-u.ac.jp/miea/
K e y w o r d AI, CNN, camera, welding, penetration depth