Joint quality assessment by machine learning using characterized surface thermal radiation images of laser welding process

T. Kikuchi; R. Takabatake; K. Katayama; H. Ikenoue; D. Nakamura

doi:10.1117/12.3000891

12 March 2024 Joint quality assessment by machine learning using characterized surface thermal radiation images of laser welding process

T. Kikuchi, R. Takabatake, K. Katayama, H. Ikenoue, D. Nakamura

Author Affiliations +

Proceedings Volume 12878, High-Power Laser Materials Processing: Applications, Diagnostics, and Systems XIII; 128780E (2024) https://doi.org/10.1117/12.3000891
Event: SPIE LASE, 2024, San Francisco, California, United States

Abstract

In recent years, laser welding has been widely used as an alternative to arc welding because of its high power and faster welding speed with local heating. In the welding process, particularly for e-mobility applications, the demand for quality control via all-point inspection is increasing. The laser process enables real-time observation of the welding area during processing, making all-point inspection possible. In this study, we investigated the possibility of predicting weld bead width from a set of images acquired using a CMOS camera with a band-pass filter. Machine learning was used for the prediction, and the prediction accuracy was determined using the Root Mean Squared Error (RMSE). The laser parameters, such as irradiation power and scan speed, and 13 feature values, such as the area, centroid, and rotation angle of the light emission acquired from the images and were used as training data. The RMSE of 0.16 mm was achieved for a bead width of 0.5-1.5 mm, confirming that the prediction was sufficiently accurate. Furthermore, we conducted an analysis with and without spectroscopic images to verify whether spectroscopic images are effective for the evaluation of laser welding using machine learning.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

T. Kikuchi, R. Takabatake, K. Katayama, H. Ikenoue, and D. Nakamura "Joint quality assessment by machine learning using characterized surface thermal radiation images of laser welding process", Proc. SPIE 12878, High-Power Laser Materials Processing: Applications, Diagnostics, and Systems XIII, 128780E (12 March 2024); https://doi.org/10.1117/12.3000891

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available