• 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.

• Proceedings of the KWS Conference
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• 2001.05a
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• pp.65-68
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• 2001

The application of a feedback control system in robotic arc welding is becoming more and more demanding than ever before. This requirement arises from the fact that robotic arc welding process needs no manual operator to monitor and manipulate the process parameters and hence a means of controlling the quality of the robotic arc welding process becomes apparent. Arc force sensor employed in this research to monitor the bead geometry of the arc welding process, A relationship between the bead dimension and the arc force distributions was established. Experimental configuration for measurement of arc force was used to quantify the changes in the arc force distributions of the plate being welded. Arc force sensor mounted at the end of the robot wrist was employed to measure the arc force applied to the weld. The sensor information was the used to establish a relationship between welding current and arc force. Arc force sensor have shown to be on of the most sophisticated technique to monitor perturbations that occurred during arc welding process.

• Journal of Welding and Joining
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• v.21 no.6
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• pp.26-32
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• 2003

Arc welding process has indicated that it suffers from many flaws. It's because requirement of products is diverse and factors which affects the quality is also various. Therefore, in order to stabilize the welding process, it is important to choose a proper welding machine for the each process, and to evaluate the welding process capability of each machine. In this study, rational and simple index to evaluate the welding machine was set the coefficient of resistance variation through the arc stability examination such as spatter generation weight and bead configuration uniformity etc. And the method to evaluate the process capability index was developed by application of 6$\sigma$.

• Journal of Welding and Joining
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• v.21 no.1
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• pp.27-34
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• 2003

• Journal of Welding and Joining
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• v.18 no.4
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• pp.11-19
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• 2000

• Proceedings of the KWS Conference
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• 2003.11a
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• pp.147-149
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• 2003

Many factors, such as welding current, welding time, force, electrode shape, the output stability of a welding machine, are closely related with micro spot series welding. Of those factors, the output stability evaluation of the welding machine would be checked first. in this study, we applied 6$\sigma$ to evaluation for the output stability of the welding machine. We estimated output stability and control stability of the welding machine by using Cp(process capability), Ppk and 4-block diagram. Therefore the problem as solved in micro spot series welding process and the problem of output control in welding machine by 6$\sigma$ tool.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.6
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• pp.49-55
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• 2008

Arc welding process is one of the most important technologies to join metal plates. Robotic welding offers the reduced manufacturing cost sought, but its widespread use demands a means of sensing and correcting for inaccuracies in the part, the fixturing and the robot. A number of problems that need to be addressed in robotic arc welding processes include sensing, joint tracking, and lack of adequate models for process parameter prediction and quality control. Problems with parameter settings and quality control occur frequently in the GMA(Gas Metal Arc) welding process due to the large number of interactive process parameters that must be set and accurately controlled. The objectives of this paper are to realize the mapping characteristics of bead width using a sensitivity analysis and develop the neural network and multiple regression method, and finally select the most accurate model in order to control the weld quality(bead width) for fillet welding. The experimental results show that the proposed neural network estimator can predict bead width with reasonable accuracy, and guarantee the uniform weld quality.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.9 no.4
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• pp.103-110
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• 1995

Fuzzy theory is recently finding wide popularity in various applications that include management, economics, medicine and process control system. This paper describes application of fuzzy logic in a current control system that use a inverter welding machine. The Fuzzy control is then extended to the current loop control, replacing the conventional proportional-integral(PI) control methods. The fuzzy control algorithms have been developed in detail and verified by experiments of a inverter welding system. The experimentation study indicates the superiority of fuzzy control over the I control methods. Fuzzy control seems to have a lot of promise in the applications of welding process control system.

• Journal of Welding and Joining
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• v.14 no.4
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• pp.33-42
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• 1996

In recent years there has been a significant growth in the use of the automated and/or robotic welding system, carried out as a means of improving productivity and quality, reducing product costs and removing the operator from tedious and potentially hazardous environments. One of the major difficulties with the automated and/or robotic welding process is the inherent lack of mathematical models for determination of suitable welding process parameters. Partial-penetration, single-pass bead-on-plate welds were fabricated in 12mm AS 1204 mild steel flats employing five different welding process parameters. The experimental results were used to develop three empirical equations: curvilinear; polynomial; and linear equations. The results were also employed to find the best mathematical equation under weld bend width to assist in the process control algorithms for the Gas Metal Arc Welding(GMAW) process and to correlate welding process parameters with weld bead width of bead-on-plates deposited. With the help of a standard statistical package program. SAS, multipe regression analysis was undertaken for investigating and modeling the GMAW process, and significance test techniques were applied for the interpretation of the experimental data.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1137-1140
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• 2005

Automatic welding has been used frequently on pipeline projects. The productivity and reliability are most essential features of the automatic welding system. The mechanized GMAW process is the most widely used welding process and the carriage and band system is most effective welding system for pipeline laying. This application-oriented paper introduces new automatic welding equipment for pipeline construction. It is based on cutting-edge design and practical welding physics to minimize downtime. This paper also describes the control system which was designed and implemented for new automatic welding equipment. The system has the self diagnostic function which facilitates maintenance and repairs, and also has the network function via which the welding task data can be transmitted and the welding process data can be monitored. The laser vision sensor was designed for narrow welding groove in order to implement higher accuracy of seam tracking and fully automatic operation.