• Journal of Welding and Joining
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• v.15 no.3
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• pp.118-127
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• 1997

With the trend towards welding automation and robotization, mathematical models for studying the influence of various variables on the weld bead geometry in gas metal arc (GMA) welding process are required. Partial penetration, single-pass bead-on-plate welds using the GMA welding process were fabricated in 12mm mild steel plates employed four different process variables. Experimental results has been designed to investigate the analytical and empirical formulae, and develop mathematical equations for understanding the relationship between process variables and weld bead geometry. The relationships can be usefully employed not only for open loop process control, but also for adaptive control provided that dynamic sensing of process output is performed.

• Journal of Welding and Joining
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• v.23 no.4
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• pp.41-47
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• 2005

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.

• Journal of Welding and Joining
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• v.28 no.6
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• pp.58-62
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• 2010

In this study, the bead-on-plate welding were conducted by using GMA welding and plasma-GMA welding on Al 5052 and, Al 6061 plates. For these processes, the maximum welding speeds for full penetration were compared, and various weld qualities such as bead appearance, cross-section, cracks and porosities were examined. Faster welding speed, cleaner bead surface and more stable drop transfer could be achieved by plasma-GMA welding, compared with GMA welding.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.04a
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• pp.36-41
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• 1997

With the trend towards welding automation and robozation, mathematical models for studying the influence of various parameters on the weld bead geometry in Gas Metal Arc(GMA) welding process are required. The results of bead on plate welds deposited using the GMA welding process has enabled mathematical relationships to be developed that model the weld bead geometry. Experimental results were compared to outputs obtained using existing formulae that correlate process input variables to output parameters and subsequent modelling was performed in order to better predict the output of the GMA welding process. The aim of this work was to explain the relationships between GMA welding variables and weld bead geometry and thus, be able to predict input weld bead size. The relationships can be usefully employed for open loop process control and also for adaptive control provided that dynamic sensing of process output is performed.

• Journal of Welding and Joining
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• v.25 no.6
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• pp.64-70
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• 2007

Recently, several models to control weld quality, productivity and weld properties in arc welding process have been developed and applied. Also, the applied model to make effective use of the robotic GMA(Gas Metal Arc) welding process should be given a high degree of confidence in predicting the bead dimensions to accomplish the desired mechanical properties of the weldment. In this study, a development of the on-line learning neural network models that investigate interrelationships between welding parameters and bead width as well as apply for the on-line quality control system for the robotic GMA welding process has been carried out. The developed models showed an excellent predicted results comparing with the predicted ability using off-line learning neural network. Also, the system will extend to other welding process and the rule-based expert system which can be incorporated with integration of an optimized system for the robotic welding system.

• Journal of Welding and Joining
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• v.29 no.3
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• pp.64-69
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• 2011

Since welding process for most pipelines with large diameter has been carried out by the manual process, automation of the welding process is necessary for the sake of consistent weld quality and improvement in productivity. Therefore the development of the optimized algorithm to decide the welding condition is an effective technique to prove the feasibility of interface standards and intelligent control technology to increase productivity and reduce the cost of system integration. In this study, the pipe welding experiment has been carried out using plused GMA welding process to select optimal welding condition. And necessary information in root-pass welding has been obtained by applying in the pipeline using the selected welding conditions through the welding experiment.

• Journal of Welding and Joining
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• v.35 no.2
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• pp.35-42
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• 2017

The GMA welding process involves large number of interdependent variables which may affect product quality, productivity and cost effectiveness. The relationships between process parameters for a vertical weldment and mechanical properties are complex because a number of process parameters are involved. To make the vertical-position welding, a method that predicts bead geometry and accomplishes the desired mechanical properties of the weldment should be developed. In addition, a reliable welding process and conditions must be implemented to reduce weld structure failure. In this study, the welding process analysis of investigates the interaction between the heat input and welding parameter(Welding current, Arc voltage, Welding speed) for predicting the weldment hardness.

• Journal of Welding and Joining
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• v.30 no.6
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• pp.126-132
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• 2012

The GMA welding process involves large number of interdependent variables which may affect product quality, productivity and cost effectiveness. The relationships between process parameters for a fillet joint and bead geometry are complex because a number of process parameters are involved. To make the automated GMA welding, a method that predicts bead geometry and accomplishes the desired mechanical properties of the weldment should be developed. The developed method should also cover a wide range of material thicknesses and be applicable for all welding position. For the automatic welding system, the data must be available in the form of mathematical equations. In this study a new intelligent model with genetic algorithm has been proposed to investigate interrelationships between welding parameters and bead geometry for the automated GMA welding process. Through the developed model, the correlation between process parameters and bead geometry obtained from the actual experimental results, predicts that data did not show much of a difference, which means that it is quite suitable for the developed genetic algorithm. Progress to be able to control the process parameters in order to obtain the desired bead shape, as well as the systematic study of the genetic algorithm was developed on the basis of the data obtained through the experiments in this study can be applied. In addition, the developed genetic algorithm has the ability to predict the bead shape of the experimental results with satisfactory accuracy.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.4
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• pp.97-103
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• 1997

Designing a feedback controller requires a dynamic model of the process to be controlled. But the GMA welding process models have not been fully developed for many reasons such as complexity of the welding process and lack of reliable sensors. Because of the complexity of the welding system, we obtained a dynamic model for control using system identification routines, rather than derived a model from fundamental physical laws. The controller was designed based on the experimentally derived linear dynamic model of the welding process. In order to demonstrate application of the designed controller, the simulation was carried out.

• International Journal of Korean Welding Society
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• v.3 no.1
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• pp.8-16
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• 2003

A major concern in Gas Metal Arc (GMA) welding process is the determination of welding process variables such as wire diameter, gas flow rate, welding speed, arc current and welding voltage and their effects on the desired weld bead dimensions and shape. To successfully accomplish this objective, 81 welded samples from mild steel AS 1204 flats adopting the bead-on-plate technique were employed in the experiment. The experimental results were used to develop a mathematical model to predict the magnitude of bead geometry as follows; weld bead width, weld bead height, weld bead penetration depth, weld penetration shape factor, weld reinforcement shape factor, weld bead total area, weld bead penetration area, weld bead reinforcement area, weld bead dilution, length of weld bead penetration boundary and length of weld bead reinforcement boundary, and to establish the relationships between weld process parameters and bead geomery. Multiple regression analysis was employed for investigating and modeling the GMA process and significance test techniques were applied for the interpretation of the experimental data.