• Journal of Welding and Joining
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• 제14권4호
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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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.1845-1848
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• 2005

The automatic $CO_2$ welding is a manufacturing process to produce high quality joints for metal and it could provide a capability of full automation to enhance productivity. Despite the widespread use in the various manufacturing industries, the full automation of the robotic $CO_2$ welding has not yet been achieved partly because the mathematical model for the process parameters of a given welding task is not fully understood and quantified. Several mathematical models to control welding quality, productivity, microstructure and weld properties in arc welding processes have been studied. However, it is not an easy task to apply them to the various practical situations because the relationship between the process parameters and the bead geometry is non-linear and also they are usually dependent on the specific experimental results. Practically, it is difficult, but important to know how to establish a mathematical model that can predict the result of the actual welding process and how to select the optimum welding condition under a certain constraint. In this research, an attempt has been made to develop an intelligent algorithm to predict the weld geometry (top-bead width, top-bead height, back-bead width and back-bead height) as a function of key process parameters in the robotic $CO_2$welding. A sensitivity analysis has been conducted and compared the relative impact of three process parameters on bead geometry in order to verify the measurement errors on the values of the uncertainty in estimated parameters.

• International Journal of Korean Welding Society
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• 제3권1호
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• pp.8-16
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• 2003

A major concern in Gas Metal Arc (GMA) welding process is the determination of welding process variables such as wire diameter, gas flow rate, welding speed, arc current and welding voltage and their effects on the desired weld bead dimensions and shape. To successfully accomplish this objective, 81 welded samples from mild steel AS 1204 flats adopting the bead-on-plate technique were employed in the experiment. The experimental results were used to develop a mathematical model to predict the magnitude of bead geometry as follows; weld bead width, weld bead height, weld bead penetration depth, weld penetration shape factor, weld reinforcement shape factor, weld bead total area, weld bead penetration area, weld bead reinforcement area, weld bead dilution, length of weld bead penetration boundary and length of weld bead reinforcement boundary, and to establish the relationships between weld process parameters and bead geomery. Multiple regression analysis was employed for investigating and modeling the GMA process and significance test techniques were applied for the interpretation of the experimental data.

• 대한기계학회논문집A
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• 제41권4호
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• pp.321-327
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• 2017

본 연구에서는 수평 자세에서의 TIG 용접 시 반응표면법(RSM)을 이용하여, 최적의 이면비드 형상을 구하는 연구를 진행하였다. 입력변수로는 베이스 전류, 피크 전류, 용접속도를 선정하였고, 이면비드 폭을 용접품질에 관한 출력변수로 하여 목표값을 5.4 mm로 설정하였다. Box-Behnken 실험계획법에 따라 실험을 진행하였고, 비드 폭에 관한 2차 회귀모형을 구한 후, F-테스트를 이용하여 회귀모델을 검정하였다. 그리고 망목특성의 호감도 함수를 사용하여 이면비드 형상을 평가함으로써 최적화를 수행하였다. 이렇게 도출된 최적의 용접조건은 코드화된 값으로 베이스 전류; 0.9204, 피크 전류; 0.8676, 용접속도; 0.3776이었다. 검증실험 결과 목표값에 근접한 5.38 mm의 이면비드 폭을 얻을 수 있었다.

• Journal of Welding and Joining
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• 제28권5호
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• pp.86-92
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• 2010

In GMAW processes, bead geometry is a criterion to estimate welding quality. Bead geometry is affected by welding current, arc voltage, welding speed, shielding gas and so on. Thus the welding condition has to be selected carefully. In this paper, an experimental method for the selection of optimal welding condition was proposed in the root pass welding which was done along the GMA V-grooved butt weld joint. This method uses the response surface analysis in which the width and height of back bead were chosen as the quality variables of the weld. The overall desirability function, which is the combined desirability function for the two quality variables, was used as the objective function for getting the optimal welding condition. Through the experiments, the target values of the back bead width and the height were chosen as 4mm and 1mm respectively for the V-grooved butt weld joint. From a series of welding test, it was revealed that a uniform weld bead can be obtained by adopting the optimal welding condition which was determined according to the method proposed.

• Journal of Welding and Joining
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• 제17권3호
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• pp.26-35
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• 1999

On the laser weld production line, a slight alteration of the welding condition changes the bead size and the strength of the weldment. The measurement system is produced by using three photo-diodes for detection of the plasma and spatter signal in $CO_2$ laser welding. The relationship between the sensor signals of plasma or spatter and the bead shape, and the mechanism of the plasma and spatter were analyzed for the bead size estimation. The penetration depth and the bead width were estimated using the multiple regression analysis.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1987년도 한국자동제어학술회의논문집; 한국과학기술대학, 충남; 16-17 Oct. 1987
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• pp.329-334
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• 1987

The purpose of this study is to monitor and control the back bead width in arc welding with consumable electrode for reduction of the occurrence of weld defect. The temperature of a point on the weldment surface is selected, as a monitoring parameter, and measured by an optical infra-red sensor. The correlation between the back bead width and the surface temperature is experimentally obtained for various thicknesses of the weldment. The welding travel speed and the surface temperature are taken, respectively, as an input and an output of the welding process under the stable condition of arc. A PI control scheme to maintain the surface temperature at the desired level is proposed by the experimental study.

• 한국산업융합학회 논문집
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• 제5권2호
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• pp.111-118
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• 2002

In GMAW(Gas Metal Arc Welding) processes, bead geometry (penetration, bead width and height) is a criterion to estimate welding quality, Bead geometry is affected by welding current, arc voltage and travel speed, shielding gas, CTWD (contact-tip to workpiece distance) and so on. In this paper, welding process variables were selected as welding current, arc voltage and travel speed. And bead geometry was reasoned from the chosen welding process variables using neuro-fuzzy algorithm. Neural networks was applied to design FLC(fuzzy logic control), The parameters of input membership functions and those of consequence functions in FLC were tuned through the method of learning by backpropagation algorithm, Bead geometry could he reasoned from welding current, arc voltage, travel speed on FLC using the results learned by neural networks. On the developed inference system of bead geometry using neuo-fuzzy algorithm, the inference error percent of bead width was within ${\pm}4%$, that of bead height was within ${\pm}3%$, and that of penetration was within ${\pm}8%$, Neural networks came into effect to find the parameters of input membership functions and those of consequence in FLC. Therefore the inference system of welding quality expects to be developed through proposed algorithm.

• 한국공작기계학회논문집
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• 제17권6호
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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.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2004년도 추계학술대회 논문집
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• pp.170-174
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• 2004

Despite the widespread use in the various manufacturing industries, the full automation of the robotic CO$_2$ welding has not yet been achieved partly because the mathematical model for the process parameters of a given welding task is not fully understood and quantified. Several mathematical models to control welding quality, productivity, microstructure and weld properties in arc welding processes have been studied. However, it is not an easy task to apply them to the various practical situations because the relationship between the process parameters and the bead geometry is non-linear and also they are usually dependent on the specific experimental results. Practically, it is difficult, but important to know how to establish a mathematical model that can predict the result of the actual welding process and how to select the optimum welding condition under a certain constraint. In this paper, an attempt has been made to develop an Radial basis function network model to predict the weld top-bead width as a function of key process parameters in the robotic CO$_2$ welding. and to compare the developed model and a simple neural network model using two different training algorithms in order to verify performance. of the developed model.