Artificial intelligence regressors to predict the weld penetration in metal laser welding

Victor Hayot; Andre Alves Ferreira; Sylvain Lecler; Grégoire Chabrol

doi:10.1117/12.3017109

20 June 2024 Artificial intelligence regressors to predict the weld penetration in metal laser welding

Victor Hayot, Andre Alves Ferreira, Sylvain Lecler, Grégoire Chabrol

Proceedings Volume 13005, Laser + Photonics for Advanced Manufacturing; 130050F (2024) https://doi.org/10.1117/12.3017109
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

Laser welding is a manufacturing process widely used in the industry to efficiently join parts together, generally through a characteristic deep penetration melt pool. Its benefits, like no-contact welding, no tool wear and fast processing, are very relevant for many industrial applications. Nonetheless, finding the optimal parameters for each specific processes remains challenging and time-consuming. Involving many physical phenomena, such as laser-matter interaction, thermodynamics and fluid mechanics, the process parameters have many nonlinear interactions. In these circumstances, a cost and time-effective Design of Experiment (DoE) is nearly impossible to generate. Furthermore, thorough weld characterisation, from geometrical to metallurgical analysis, remains a labor-intensive and expensive task. In this study, we compared different regressors powered by Artificial Intelligence such as Gradient Boosted Decision Trees, Gaussian Process Regressors, Perceptrons trained on readily available data from previous trials done at IREPA LASER, to predict the depth of penetration of the weld. To develop the model with industrial use in mind, the material, the processing parameters and the optical setup were used as the input parameters. A R² of 0.94 and a Mean Squared Error of 0.25mm² are obtained from the model developed. Scores are then compared to the state of the art, taking into consideration the size and number of parameters of the dataset used.

Conference Presentation

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

Citation Download Citation

Victor Hayot, Andre Alves Ferreira, Sylvain Lecler, and Grégoire Chabrol "Artificial intelligence regressors to predict the weld penetration in metal laser welding", Proc. SPIE 13005, Laser + Photonics for Advanced Manufacturing, 130050F (20 June 2024); https://doi.org/10.1117/12.3017109

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