Prediction of the welding process parameters and the weld bead geometry for robotic welding applications with adaptive neuro-fuzzy models

Vu, Minh Duc, My, Chu Anh, Nguyen, The Nguyen, Duong, Xuan Bien, Le, Chi Hieu, Gao, James, Zlatov, Nikolay, Hristov, Georgi, Nguyen, Van Anh, Mahmud, Jamaluddin and Packianather, Michael S. ORCID: https://orcid.org/0000-0002-9436-8206 2023. Prediction of the welding process parameters and the weld bead geometry for robotic welding applications with adaptive neuro-fuzzy models. Presented at: The International Conference on Intelligent Systems & Networks, 18-19 March 2023. Published in: Nguyen, T.D.L., Verdú, E., Le, A.N. and Ganzha, M. eds. Lecture Notes in Networks and Systems , vol.752 Singapore: Springer, pp. 148-158. 10.1007/978-981-99-4725-6_20

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Abstract

The weld bead geometry is the important information for determining the quality and mechanical properties of the weldment. The welding process parameters or variables that affect the weld bead geometry in the conventional arc welding process include the following: the welding voltage U, the welding current I, the wire feed speed WFS, the contact tip to work distance D, and the welding speed S. Modeling and predicting the weld bead geometry play an important role in welding process planning, to determine the optimal welding process parameters for achieving the improved weld quality. There have been lots of efforts and studies to develop modeling solutions and simulations to determine the weld bead geometry (Height H and Width W) from the welding process parameters (U, I, WFS, D, S) as the inputs. The welding process parameters can be determined based on the experiences, and the conventional analysis of variance (ANOVA); however, the high welding quality and accuracy are not always obtained. With the advancement of computer vision technologies, digital images from cameras and videos can be used for training the deep learning models, to accurately identify and classify objects. The digital images for evaluating the welding quality and the characteristics of welding objects can be captured via the use of the high-speed camera, and there are emerging data acquisition systems that can handle a huge dataset. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) model is proposed to determine weld bead geometry from the main welding process parameters U, I and S. The proposed ANFIS model was successfully developed for the first basic investigations, as the foundation for further developments of innovative robotic welding systems which can be used for higher educations or research in Smart Manufacturing, with potentials for industrial applications.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Publisher:	Springer
ISBN:	978-981-99-4724-9
Last Modified:	31 Oct 2023 11:00
URI:	https://orca.cardiff.ac.uk/id/eprint/163396

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