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Development of Experimental Model fer Bead profile Prediction in GMA Welding  

Son Joon-Sik (Dept. of Mechanical Engineering, Mokpo National University)
Kim Ill-Soo (Dept. of Mechanical Engineering, Mokpo National University)
Park Chang-Eun (Dept. of Mechanical Engineering, Mokpo National University)
Kim In-Ju (Korea Institute of Industrial Technology)
Jeong Ho-Seong (Dept. of Mechanical Engineering, Mokpo National University)
Publication Information
Journal of Welding and Joining / v.23, no.4, 2005 , pp. 41-47 More about this Journal
Abstract
Generally, the use of robots in manufacturing industry has been increased during the past decade. GMA(Gas Metal Arc) welding process is an actively Vowing area, and many new procedures have been developed for use with high strength alloys. One of the basic requirement for the automatic welding applications is to investigate relationships between process parameters and bead geometry. The objective of this paper is to develop a new approach involving the use of neural network and multiple regression methods in the prediction of bead geometry for GMA welding process and to develop an intelligent system that visualize bead geometry in order to employ the robotic GMA welding processes. Examples of the simulation for GMA welding process are supplied to demonstrate and verify the proposed system developed using MATLAB. The developed system could be effectively implemented not oかy for estimating bead geometry, but also employed to monitor and control the bead geometry in real time.
Keywords
Bead geometry; GMA welding; Optimization; Intelligent system; Neural network; Robot;
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