• Transactions of Materials Processing
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• v.8 no.4
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• pp.340-346
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• 1999

Circular drawbead forming and drawing characteristics of CO2 laser welded SPC1 blanks are investigated by experiments and numerical analysis. During the drawbead forming process, the distributions of major strain on upper and lower skins of the specimens are measured. During the drawing process, the drawing forces and the strain distributions are investigated. For the numerical analysis. DYNA3D and SGTAS, a developed rigid-plastic finite-element computer program are used. Numerical results predicted the deformation characteristics well in comparison with experiments. It is concluded that the strains and restraining forces during the forming and the drawing processes show different patterns according to the combination of welded blanks.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.637-642
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• 2002

Generally, the use of robots in manufacturing industry has been increased during the past decade. GMA(Gas Metal Are) welding is an actively growing area and many new procedures have been developed for use with high strength alloys. One of the basic requirement for welding applications is to study 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 enables the prediction of bead geometry using Rapid Prototyping(RP) in order to employ the robotic GMA welding processes. This system developed using MATLAB/SIMULINK, could be effectively implemented not only for estimating bead geometry, but also employed to monitor and control the bead geometry in real time.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.10a
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• pp.298-303
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• 1997

Over the last few year, there has been a growing interest in quantitative representation of heat transfer phenomena in weld pools in order to relate the processing conditions to the quality of the weldment produced and to use this information for the optimisation and robotization of the welding process. Normally, a theoretical model offers a powerful alternative to check out the physical concepts of the welding process and to calculate the effects of varying any of parameters. To solve this problem, a transient 2D(two-dimensional) heat conduction were developed for determining bead geometry and temperature distribution for the GMA welding process. The equation was solved using a general thermofluid-mechanics computer program, PHOENICS code, which is based on the SIMPLE algorithm. The simulation results showed that the calculated bead geometry from the developed models reasonablely agree with the experiment results.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.6
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• pp.57-63
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• 1998

An adaptive control in the robotic GMA welding is employed to monitor information about weld characteristics and process parameters as well as to modify those parameters to hold weld quality within acceptable limits. Typical characteristics are the bead geometry, composition, microstructure, appearance, and process parameters which govern the quality of the final weld. The main objectives of this thesis are to realize the mapping characteristics of bead width through learning. After learning, the neural estimation can estimate the bead width desired form the learning mapping characteristic. The design parameters of the neural network estimator(the number of hidden layers and the number of nodes in a layer) are chosen from an estimation error analysis. A series of bead of bead-on-plate GMA welding experiments was carried out in order to verify the performance of the neural network estimator. The experimental results show that the proposed neural network estimator can predict the bead width with reasonable accuracy and guarantee the uniform weld quality.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.11 no.4
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• pp.89-96
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• 2002

This paper aims to develop an intelligent model for predicting top-bead width for the robotic GMA(Gas Metal Arc) welding process using BP(Back-propagation) neural network and multiple regression analysis. Firstly, based on experimental data, the basic factors affecting top-bead width are identified. Then BP neural network model and multiple regression models of top-bead width are established. The modeling methods and procedure are explained. The developed models are then verified by data obtained from the additional experiment and the predictive behaviors of the two kind of models are compared and analysed. Finally the modeling methods, predictive behaviors md the advantages of each models are discussed.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.672-678
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• 1993

The bead shape in high frequency electric resistance (HER) pipe welding gives important information ons judging current welding state. In most manufacturing process, the heat input is controlled by skilled operators observing color and bead shape. We proposed the bead shape monitoring system in HERW pipe process by using structured light beam. We reconstructs 3-D shape of bead from the measured data, and compare this shape with real 3-D shape obtained by coordinate-measuring machine. This experiment results show that the proposed system can monitor the bead shape with good accuracy.

• Journal of Welding and Joining
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• v.18 no.4
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• pp.48-54
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• 2000

Adaptive control in the robotic GMA(Gas Metal Arc) welding is employed to monitor information about weld characteristics and process parameters as well; as t modify those parameters to hold weld. The objectives of this paper are to realize the mapping characteristics of bead width through the neural network and multiple regression method as well as to select the most accurate model in order to control the weld quality(bead width0. The experimental results show that the proposed neural network estimator can predict bead width with reasonable accuracy, and guarantee the uniform weld quality.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.10a
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• pp.57-62
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• 2000

Diagnosis or measurements using Infrared thermoimage hasn't been available. A quick diagnosis and thermal analysis can be possible when that kind of system is introduced to the investigation of each part. In this study, Infrared Camera, Thermovision 900 was used in order to investigate. Infrared Camera usually detects only Infrared wave from the light in order to illustrate the temperature distribution. Infrared diagnosis system can be applied to various field. Also, it is more effective to analyze temperature distribution on the welding parts during welding process. Especially, diagnosis using Infrared camera plays an important role on thermal analysis of Axle Casing Nut for Commercial Vehicles.

• Journal of Welding and Joining
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• v.18 no.6
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• pp.68-73
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• 2000

In case of overalyered wrapping roll, beadmark shape appear at wrapping roll surface due to irregular wear between weld bead. Irregular wear of this is caused by difference of hardness between weld bead. This study aims at investigating which matrix is good for removing of beadmark at wrapping roll surface. So, we make specimen with martensitic matrix and austenitic matrix. The overlayered alloys were deposited 4 times on a SS41 steel plate using self-shielding flux cored arc welding method. Difference of hardness between weld bead of specimen with matrix of martensite was higher than specimen with matrix of austenite both as-welded and after heat treatment. Therefore, austenitic matrix is between than martensitic matrix for removing of beadmark of wrapping roll surface.