A Study on Development of STACO Model to Predict Bead Height in Tandem GMA Welding Process

Lee, Jongpyo; Kim, IllSoo; Park, Minho; Park, Cheolkyun; Kang, Bongyong; Shim, Jiyeon

doi:2014.32.6.544

J Weld Join > Volume 32(6); 2014 > Article

Special Issue

Journal of Welding and Joining 2014;32(6):544-549.
DOI: https://doi.org/10.5781/JWJ.2014.32.6.8 Published online December 31, 2014.

A Study on Development of STACO Model to Predict Bead Height in Tandem GMA Welding Process

Jongpyo Lee^*, IllSoo Kim^*, Minho Park^*, Cheolkyun Park^*, Bongyong Kang^**, Jiyeon Shim^**

Correspondence: IllSoo Kim,
Email: ilsookim@mokpo.ac.kr

Abstract

One of the main challenges of the automatic arc welding process which has been widely used in variousconstructions such as steel structures, bridges, autos, motorcycles, construction machinery, ships, offshore structures, pressure vessels, and pipelines is to create specific welding knowledge and techniques with high quality and productivity of the production-based industry. Commercially available automated arc welding systems use simple control techniques that focus on linear system models with a small subset of the larger set of welding parameters, thereby limiting the number of applications that can be automated. However, the correlations of welding parameters and bead geometry as welding quality have mostly been linked by a trial and error method to adjust the welding parameters. In addition, the systematic correlation between these parameters have not been identified yet.
To solve such problems, a new or modified models to determine the welding parameters for tandem GMA(Gas Metal Arc) welding process is required. In this study, A new predictive model called STACO model, has been proposed. Based on the experimental results, STACO model was developed with the help of a standard statistical package program, MINITAB software and MATLAB software. Cross-comparative analysis has been applied to verify the reliability of the developed model.

Key Words: STACO model, Tandem GMA welding, Neural networks