• 한국공작기계학회논문집
• /
• 제17권6호
• /
• pp.49-55
• /
• 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.

• 한국정밀공학회지
• /
• 제23권7호
• /
• pp.59-66
• /
• 2006

In laser welding, automatic seam tracking is important to adjust the laser head position in real time as it moves along the seam. Also if the joint gap is occurred, filling the missing material into the joint gap is necessary to prevent welding defects and bad welding quality. In general, the joint gap width is not constant along the seam due to a variety of reason. So it is essential to control the filler wire speed into the joint gap to acquire good welding quality. This paper describes an intelligent filler wire feeding device which can control 3-dimensional seam tracking and the filler wire speed by measuring the gap position and the joint gap width in laser welding. We call this device as Smart Micro Control system(SMC). To achieve this objective, we assessed weld quality in 2mm sheets of A16061 which had various gap width by using the developed device. From the experimental results, It was found the possibility that the developed device could be used in welding various 3-dimensional structures.

• Journal of Welding and Joining
• /
• 제25권6호
• /
• pp.64-70
• /
• 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.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
• /
• pp.1408-1411
• /
• 1996

In arc welding processes, because of the complexity and nonlinearity of heat transfer phenomena, it is often difficult to design an effective control system based upon an exact mathematic model. In this paper, we simulated the welding process for various welding conditions and positions to investigate the variations of temperature distribution for those cases. Those will be used as a preinformation for developing quality control system for arc welding process.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2005년도 추계학술대회 논문집
• /
• pp.754-758
• /
• 2005

Optimization of process variables such as arc current, welding voltage and welding speed in terms of the weld characteristics desired is the key step in achieving high quality and improving performance characteristics without increasing the cost. Consequently, incorrect settings of those process variables give rise to deviations in the welding characteristics from the desired bead geometry. Therefore, trainee welders are referred to the tabulated information relating different metal types and thickness as to recommend the desired values of process variables. Basically, the bead geometry plays an important role in determining the mechanical properties of the weld. So that it is very important to select the process variables for obtaining optimal bead geometry. However, it is difficult for the traditional identification methods to provide an accurate model because the optimized welding process is non-linear and time-dependent. In this paper, the possibilities of the Infra-red sensor in sensing and control of the bead geometry in the automated welding process are presented. Infra-red sensor is a well-known method to deal with the problems with a high degree of fuzziness so that the sensor is employed to build the relationship between process variables and the quality characteristic the proposed above respectively. Based on several neural networks, the mathematical models are derived from extensive experiments with different welding parameters and complex geometrical features. The developed system enables to select the optimal welding parameters and control the desired weld dimensions during arc welding process.

• International Journal of Korean Welding Society
• /
• 제1권1호
• /
• pp.31-38
• /
• 2001

For the horizontal fillet welding with one plate in a vertical position, there will be a higher tendency of weld metal falling down rather than for the butt-welding in flat position. Such phenomenon could bring about the overlap or deflection of weld pool, and consequently induce the poor mechanical strength of weldments. Therefore, a precise position control of welding torch in conjunction with the weld qualify plays an important role in welding robot applications. In the present study, an experimental method was proposed for deriving a mathematical model between the leg length and the welding conditions. Finally, an algorithm was proposed for weld seam tracking and improvement of the weld quality. The reliability of the proposed algorithm was evaluated through various experiments, which showed that the proposed algorithm can be very effective for tracking the weld line and simultaneously achieving the sound weld bead.

• Journal of Welding and Joining
• /
• 제21권5호
• /
• pp.491-499
• /
• 2003

• 한국지능시스템학회:학술대회논문집
• /
• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
• /
• pp.189-193
• /
• 1997

Generally, though we use the vision sensor or arc sensor in welding process, it is difficult to define the welding parameters which can be applied to the weld quality control. Especially, the important parameters is Arc Voltage, Welding Current, Welding Speed in arc welding process and they affect the decision of weld bead shape, the stability of welding process and the decision of weld quality. Therefore, it is difficult to determine the unique relationship between the weld bead geometry and the combination of various welding condition. Due to the various difficulties as mentioned, we intend to use Fuzzy Logic and Neural Network to solve these problems. Therefore, the combination of Fuzzy Logic and Neural network has an effect on removing the weld defects, improving the weld quality and turning the desired weld bead shape. Finally, this system can be used under what kind of welding process adequately and help us make an estimate of the weld bead shape and remove the weld defects.

• 한국항해학회지
• /
• 제23권4호
• /
• pp.105-114
• /
• 1999

Generally, though we use the vision sensor or arc sensor in welding process, it is difficult to define the welding parameters which can be applied to the weld quality control. Especially, the important Parameters is Arc Voltage, Welding Current, Welding Speed in arc welding process and they affect the decision of weld bead shape, the stability of welding process and the decision of weld quality. Therefore, it is difficult to determine the unique relationship between the weld bead geometry and the combination of various welding condition. Due to the various difficulties as mentioned, we intend to use Fuzzy Logic and Neural Network to solve these problems. Therefore, the combination of Fuzzy Logic and Neural network has an effect on removing the weld defects, improving the weld quality and turning the desired weld bead shape. Finally, this system can be used under what kind of welding recess adequately and help us make an estimate of the weld bead shape and remove the weld defects.

• Journal of Welding and Joining
• /
• 제33권6호
• /
• pp.13-20
• /
• 2015

Many automotive companies are endeavoring to improve the quality of resistance spot welding by updating body-in-white (BIW) production line with adaptive control spot welding system to compensate the process disturbances such as gap, electrode wear, oxidized surfaces, poor fit up and adhesive etc. Most of the commercial adaptive weld controllers require proper "Initial Welding Schedule" or "Reference weld" to achieve compensation in welding parameters during real time welding. In this study, the compensation of a commercial adaptive weld controller had been observed and analyzed thoroughly for various process disturbances to find optimal initial welding schedule. It was observed that 90 percent of the expulsion current in constant current control as reference welding schedule conferred the maximum button diameter in adaptive control welding. Finally, effects of each disturbance in combined field disturbances system with adaptive control had also been confirmed with the design of experiment (DOE) by minitab(R)16 for combined disturbances situation and suitability of optimum initial weld current had also established with real body part validation test.