• International Journal of Korean Welding Society
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• v.1 no.2
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• pp.36-44
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• 2001

To get the appropriate welding process variables, mathematical modeling in conjunction with many experiments is necessary to predict the magnitude of weld bead shape. Even though the experimental results are reliable, it has a difficulty in accurately predicting welding process variables for the desired weld bead shape because of nonlinear and complex characteristics of welding processes. The welding condition determined for the desired weld bead shape may cause the weld defect if the welding current/voltage/speed combination is improperly selected. In this study, the $2^{n-1}$ fractional factorial design method and correlation parameter were used to investigate the effect of the welding process variables on the fillet joint shape, and the multiple non-linear regression analysis was used for modeling the gas metal arc welding(GMAW）parameters of the fillet joint. Finally, a fuzzy rule-based method and a neural network method were proposed so that the complexity and non-linearity of arc welding phenomena could be effectively overcome. The performance of the proposed neuro-fuzzy system was evaluated through various experiments. The experimental results showed that the proposed neuro-fuzzy system could effectively check the welding conditions as to whether or not weld defects would occur, and also adjust the welding conditions to avoid these weld defects.

• Journal of Welding and Joining
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• v.32 no.5
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• pp.50-57
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• 2014

Friction Stir Welding (FSW) is a complex process with several mutually interdependent parameters. A slight difference from known settings may lead to imperfections in the stirred zone. These inhomogeneities affect on the mechanical properties of the FSWed joints. In order to prevent the failure of the welded joint it is necessary to detect the most critical defects non-destructive. Especially critical defects are wormhole and lack of penetration (LOP), because of the difficulty of detection. Online thermography is used process-accompanying for defect detecting. A thermographic camera with a fixed position relating to the welding tool measures the heating-up and the cool down of the welding process. Lap joints with sound weld seam surfaces are manufactured and monitored. Different methods of evaluation of heat distribution and intensity profiles are introduced. It can be demonstrated, that it is possible to detect wormhole and lack of penetration as well as surface defects by analyzing the welding and the cooling process of friction stir welding by passive online thermography measurement. Effects of these defects on mechanical properties are shown by tensile testing.

• Journal of Welding and Joining
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• v.20 no.6
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• pp.8-8
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• 2002

• Proceedings of the KWS Conference
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• 2005.06a
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• pp.103-105
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• 2005

Laser welding technology for automobile body is studied. laser system, robot and seam tracking system are used for 3D laser welding system The laser system is used 4kW Nd:YAG laser(HL4006D) of Trumpf and the robot system is used IRB6400R of ABB. The seam tracking system is SMRT-20LS of ServoRobot. The welding joints of steel plate are butt and lap joint. The 3-D welding for non-linear tailored blank is performed after experiments of bead on plate. Finally, the welding process for non-linear tailored blank is developed.

• Tribology and Lubricants
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• v.39 no.3
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• pp.118-122
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• 2023

In our study, we develop a finite element model based on Archard's wear law to predict the cumulative wear and the evolution of the tool profile in friction stir welding (FSW) applications. Our model considers the rotational and translational behaviors of the tool, providing a comprehensive description of the wear process. We validate the accuracy of our model by comparing it against experimental results, examining both the predicted cumulative wear and the resulting changes to the tool profile caused by wear. We perform a detailed comparison between the predictions of the model and experimental data by manipulating non-dimensional coefficients comprising model parameters, such as element sizes and time increments. This comparison facilitates the identification of a specific non-dimensional coefficient condition that best replicates the experimentally observed cumulative wear. We also directly compare the worn tool profiles predicted by the model using this specific non-dimensional coefficient condition with the profiles obtained from wear experiments. Through this process, we identify the model settings that yield a tool wear profile closely aligning with the experimental results. Our research demonstrates that carefully selecting non-dimensional coefficients can significantly enhance the predictive accuracy of finite element models for tool wear in FSW processes. The results from our study hold potential implications for enhancing tool longevity and welding quality in industrial applications.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.5
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• pp.110-117
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• 2004

In automatic welding process using a robot, seam tracking is one of the important parts. Sensor for seam tracking is divided broadly into two categories as non contact sensor and contact sensor. The arc sensor is one of the non contact sensors, and it can be applied in weaving arc and rotating arc welding process. In such the arc sensors, rotating arc sensor can be applied to high speed welding over tens of Hz. The decrease of self regulation by high rotating speed causes to improve accuracy and response of sensor. In this study, fuzzy controller was used to track the seam for the $CO_2$ arc welding which had unstable arc. It could be shown that the rotating arc sensor was better than the weaving arc sensor.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.05a
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• pp.369-373
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• 2001

In this paper, we proposes the steps adopted to construct the neural network model for GMAW welds. Conventional, automated process generally involves sophisticated sensing and control techniques applied to various processing parameters. Welding parameters are influenced by numerous factors, such as welding current, arc voltage, torch travel speed, electrode condition and shielding gas type and flow rate etc. In traditional work, the structural mathematical models have been used to represent this relationship. Contrary to the traditional model method, neural network models are based on non-parametric modeling techniques. For the welding process modeling, the non-linearity at well as the coupled input characteristics makes it apparent that the neural network is probably the most suitable candidate for this task. Finally, a suitable proposal to improve the construction of the model has also been presented in the paper.

• Journal of Welding and Joining
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• v.11 no.1
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• pp.2-8
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• 1993

A literature review was conducted to gather informations available on the welding metallurgy of aluminum alloys, emphasized on characteristics in the heat affected zone(HAZ). Nominal metallurgical reactions that occur in aluminum alloys provide a basis for understanding aluminum welding metallurgy. However, welding reactions differ to some extent because of the relatively short times involved, and the non-isothermal heating excursed. For non-heat treatable alloys, welding primarily affects these alloys by annealing (recrystallization and growth) and to a less extent, changes in low temperature precipitates. In the case of heat treatable alloys, the resulting HAZ properties depend upon alloy composition, starting temper, heat input and post weld heat treatments.

• International Journal of Korean Welding Society
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• v.2 no.1
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• pp.29-32
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• 2002

In previous study, the decrease and recovery of total stiffness in welded structure was discussed on the basis of experimental examination through tensile loading and unloading test of welded specimen. The recovery of structure stiffness was caused by the release of welding residual stress through mechanical loading. In this study, analysis model that is indispensable for the effective application of MSR method was established on the basis of test and measurement result. Thermal elasto-plastic analysis for welding process was performed by non- coupled analysis. Analysis results of welding process were transfer to elasto-plastic model for tensile loading & unloading by restart technique. In elasto-plastic analysis model for mechanical loading & unloading, hardening appearance of weld metal was considered by rezoning technique and tying technique was used for JIG condition of test machine.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.25 no.1
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• pp.48-53
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• 2016

An EMAT can be used to reliably detect defects as it serves as a non-contact transducer with the ability to transmit ultrasonic waves into specimens without couplant. Moreover, an EMAT can easily generate desired waves by altering the design of the coil and magnet. This study proposes an SH-EMAT to evaluate the integrity of the TIG welding part. A stainless steel was welded using the TIG welding method. The welding current was varied to create artificial defects. Both the PA-UT and the RT were applied to verify the defect size. The experimental results generated by using the EMAT were compared with those methods. The amplitude was observed to decrease with an increase in the defect size. These results confirmed that the presence of defects can be reliably detected by attenuation of signal amplitude. The results demonstrated that the proposed method is suitable for evaluating the integrity of TIG welding.