• 대한용접접합학회:학술대회논문집
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• 대한용접접합학회 2002년도 Proceedings of the International Welding/Joining Conference-Korea
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• pp.595-599
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• 2002

Edge welding of thin sheets is very difficult because of the fit-up problem and small weld area In laser welding, joint fit-up and penetration are critical for sound weld quality, which can be monitored by appropriate methods. Among the various monitoring systems, visual monitoring method is attractive because various kinds of weld pool information can be extracted directly. In this study, a vision sensor was adopted for the weld pool monitoring in pulsed Nd:YAG laser edge welding to monitor whether the penetration is enough and the joint fit-up is within the requirement. Pulsed Nd:YAG laser provides a series of periodic laser pulses, while the shape and brightness of the weld pool change temporally even in one pulse duration. The shutter-triggered and non-interlaced CCD camera was used to acquire a temporally changed weld pool image at the moment representing the weld status well. The information for quality monitoring can be extracted from the monitored weld pool image by an image processing algorithm. Weld pool image contains not only the information about the joint fit-up, but the penetration. The information about the joint fit-up can be extracted from the weld pool shape, and that about a penetration from the brightness. Weld pool parameters that represent the characteristics of the weld pool were selected based on the geometrical appearance and brightness profile. In order to achieve accurate prediction of the weld penetration, which is nonlinear model, neural network with the selected weld pool parameters was applied.

• Journal of Welding and Joining
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• 제16권5호
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• pp.76-85
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• 1998

The resistance spot welding process has been used for joining the sheet metal in automotive engineering. In the resistance spot welding, the weld quality is very important, because the quality of weld is one of the most important factors to the automobile quality. The size of he molten nugget has been utilized to estimate the weld quality. However, it is not easy to find the weld defects. For weldability estimation, we have to use the nondestructive method such as X-ray or ultrasonic inspection. But these kinds of approaches are not suitable for detecting the defects in real time. The purpose of this study is to develop the real time monitoring of the weld quality in the resistance spot welding. Obtained data were used to estimate weldability using fuzzy algorithm. It is sound that this monitoring and estimation system can be useful to improve the weld quality in the resistance spot welding process and it is possible to estimate the weldability in real time.

• 한국기계가공학회지
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• 제21권6호
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• pp.89-97
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• 2022

Ultrasonic metal welding has been widely used for joining lithium-ion battery tabs. Weld quality monitoring has been an important issue in lithium-ion battery manufacturing. This study focuses on the weld quality monitoring in ultrasonic metal welding with the longitudinal-torsional vibration mode horn developed newly. As the quality of ultrasonic welding depends on welding parameters like pressure, time, and amplitude, the suitable values of these parameters were selected for experimentation. The welds were tested via tensile testing machine and weld strengths were investigated. The dataset collected for performance test was used to train the multi-layer perceptron neural network. The three layer neural network was used for the study and the optimum number of neurons in the first and second hidden layers were selected based on performances of each models. The best models were selected for the horn and then tested to see their performances on an unseen dataset. The neural network models for the longitudinal-torsional mode horn attained test accuracy of 90%. This result implies that proposed models has potential for the weld quality monitoring.

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

• 한국해양공학회지
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• 제6권2호
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• pp.113-124
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• 1992

The offshore industry created a need for quality wet weld repairs. Wet welding is a fast method of repair providing sound, structural quality welds. It requires less support equipment than a similar underwater dry weld repair or the alternative mechanical connections. Compared to welds made in air, underwater wet welds are plagued by increased hardness due to rapid quenching by the surrounding water. In this paper is described the experimntal study of improving the cooling rates of wet welds of TMCP steel plate by shielding around weld arc surroundings. The principal results of this experimental investigation can be summarized as follows : By shielding around weld arc surrounding, the cooling rates resulting from wet welds on TMCP steel plate could be lower than that of nonshielded wet welds and the fesibility on high quality of mecanical properties of wet weld on TMCP steel plate was carried out with shielded weld arc surrounding.

• Journal of Welding and Joining
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• 제32권3호
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• pp.1-10
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• 2014

The efficient application of any welding process depends on the understanding of associated process parameters influence on the weld quality. The weld quality includes the weld bead dimensions, temperature distribution, metallurgical phases and the mechanical properties. A detailed review on the experimental and numerical approaches to understand the parametric influence of a single wire submerged arc welding (SAW) and multi-wire SAW processes on the final weld quality is reported in two parts. The first part deals with the experimental approaches which explain the parametric influence on the weld bead dimensions, metallurgical phases and the mechanical properties of the SAW weldment. Furthermore, the studies related to statistical modeling of the present welding process are also discussed. The second part deals with the numerical approaches which focus on the conduction based, and heat transfer and fluid flow analysis based studies in the present welding process. The present paper is the first part.

• 한국레이저가공학회지
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• 제3권2호
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• pp.53-61
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• 2000

On the laser weld production line, a slight alteration of the welding condition produces many defects. The defects are monitored in real time, in order to prevent continuous occurrence of defects, reduce the loss of material, and guarantee good quality. The measurement system is produced by using three photo-diodes for detection of the plasma and spatter signal in CO$_2$ laser welding. For high speed CO$_2$ laser welding, laser tailored welded blanks for example, on-line weld quality monitoring system was developed by using fuzzy multi-feature pattern recognition. Weld qualities were classified optimal heat input, a little low heat input, low heat input, and focus misalignment, and final weld quality were classified good and bad.

• International Journal of Korean Welding Society
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• 제1권1호
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• pp.7-12
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• 2001

Real time monitoring has become critical as the use of laser welding increases. Plasma and spatter are measured and used as the signal for estimating weld quality. The estimating algorithm was made using the fuzzy pattern recognition with the area of data that is beyond the tolerance boundary. Also, an algorithm that detects the spatter and the localized defect was created in order to kd the partially produced pit and the sudden loss of weld penetration. These algorithms were used in quality monitoring of the $CO_2$ laser tailored blank weld. Statistical program that can display the laser weld quality result and the signal transition was made for the first stage of the remote control system.

• Journal of Welding and Joining
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• 제1권2호
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• pp.69-75
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• 1983

Both in-process quality control and reliability of the weld is one of the major concerns is applying friction welding. No reliable nondestructive monitoring method is available at present to determine the weld quality particularly in process of production. So that, this paper presents an experimental examination and quantitative analysis for the effects of initial acoustic emission(AE) counts on the weld strength relating to the rotating speed as a new approach which attempts finally to develop an on-line quality monitoring system design for friction welds using AE techniques. As one of the important results, it was well confirmed that the initial AE counts occurring during plastic deformation period of welding were quantitatively correlated with reliability at 95% confidence level to the joint strength of welds, tube-to-tube (SM 20 C to STS 304) and then an AE technique using the initial AE counts can be reliably applied to in-process strength monitoring of the weld.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.1404-1407
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• 1996

A quality monitoring system in butt welding process is proposed to estimate weld pool sizes. The geometrical parameters of the weld pool such as the top bead width and the penetration depth plus half back width are utilized to prove the integrity of the weld quality. The monitoring variables used are the surface temperatures measured at three points on the top surface of the weldment. The temperature profile is assumed that it has a gaussian distribution in vertical direction of torch movement and verify this assumption through temperature analysis. A neural network estimator is designed to estimate weld pool size from temperature informations. The experimental results show that the proposed neural network estimator which used gaussian distribution as temperature information can estimate the weld pool sizes accurately than used three point temperatures as temperature information. Considering the change of gap size in butt welding, the experiment were performed on various gap size.