• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.322-326
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• 1989

This paper describes design procedure of suboptimal control to minimize a performance index which is represented as sum of square output error and the heat input power in arc welding process. Heat input and temperature of a fixed point on the surface of the material are concerned as input and output of the process, repectively. The suboptimal control law considered here in is a proportional plus integral type and is implemented by using only the output variables available from sensor which is also optimally located in a fixed point w.r.t. a moving weld touch.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.289-294
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• 1988

This paper describes tbe design and implementation of the adaptive controller to maintain the glood weld quality in gas metal arc welding process. The weld torch travel speed and the surface temperature are taken, respectively, as an input and an output of the welding control system. Because of the very complex phenomena of the process, the input-output dynamic model was experimentally identified by AIC (Akiake Information Criterion). Based on the model structure, the explicit model reference adaptive controller is simulated in order to regulate the output tempernture to the desired level.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.1028-1033
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• 1991

This paper describes the design of a neural network estimator to estimate weld pool sizes for on-line use of quality monitoring and control in GMA welding processes. The estimator utilizes surface temperatures measured at various points on the top surface of the weldment as its input. The main task of the neural net is to realize the mapping characteristics from the point temperatures to the weld pool sizes through training, A series of bead-on plate welding experiments were performed to assess the performance of the neural estimator.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.531-536
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• 1992

In GMA welding processes, monitoring and control of weld quality are extremely difficult problems. This paper describes a neural network-based method for monitoring and control of weld pool sizes. First, weld pool sizes are estimated via a neural estimator using multi-point surface temperatures, which are strongly related to the formation of weld pool, and then controlled using the estimated pool sizes. Two types of controllers using the pool size estimator are designed and tested. To evaluate the performance of the designed controllers, a series of simulation studies was performed.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.797-800
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• 1992

Much experience and knowledge is needed in welding because there are many working parameters and quantitative description is difficult. Therefore, introduction of expert system based on such data base has been required. In this study, welding heat and shape of bead was controlled by fuzzy inference with the welding condition, position error and voltage and current error of robot. For this, torch trajectory of robot was generated by modeling the working data with CAD and then welding was carried out through down loading to robot. And working error was controlled by on-line communication.

• Proceedings of the KWS Conference
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• 2006.10a
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• pp.101-103
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• 2006

DSME has developed Sub-assembly Welding Robot System(SWRS) in order to increase the productivity of arc welding and to improve hazard and unclean environments in shipbuilding. DSME's SWRS includes a number of equipments such as four overhanging 6-axis articulated robot manipulators(10kg pay-load), gantry system, vision system detecting the workpiece automatically, and OLP system using the CAD data and a central control system integrating an anti-collision module. The SWRS was installed in CAS(Component Assembly Shop) of DSME's OKPO shipyard in August 2006, and now SWRS is running well in site.

• Proceedings of the KWS Conference
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• 1997.10a
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• pp.205-209
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• 1997

One of the major important tasks in the robotic $CO_2$ arc welding process is to understand how process variables affected bead geometry and to subsequently develop the mathematical models to predict the desired bead dimensions. Experiment results are compared to outputs obtained using a set of published formulae relating input variables to output parameters and also investigated process variables on bead geometry for robotic $CO_2$ arc welding process The university of results obtained using empirical equations taken from existing models provided to be limited in predicting experimental bead shapes.

• Proceedings of the KWS Conference
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• 1997.10a
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• pp.229-233
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• 1997

The demand to increase productivity and quality, the shortage of skilled labour and the strict health and safety requirements have led to the development of the automated welding process to deal with many of the present problems of welded fabrication. To make effective use of the automated arc welding process, it is imperative that a mathematical model, which can be programmed easily and fed to the robot, should be developed. The objectives of the paper are to develop the mathematical equations (linear and curvilinear) for study of the relationship between process variables and bead geometry by employing a standard statistical package program, SAS and to choose the best model for automation of the $CO_2$ gas arc welding process. Mathematical models developed from experimental results can be employed to control the process variables in order to achieve the desired bead geometry based on weld quality criteria. Also these equations may prove useful and applicable for automatic control system and expert systems.

• Journal of Welding and Joining
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• v.19 no.2
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• pp.208-214
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• 2001

In this study, we constructed a visual sensor system for weld seam tracking in real time in GMA welding. A sensor part consists of a CCD camera, a band-pass filter, a diode laser system with a cylindrical lens, and a vision board for inter frame process. We used a commercialized robot system which includes a GMA welding machine. To extract the weld seam we used a inter frame process in vision board from that we could remove the noise due to the spatters and fume in the image. Since the image was very reasonable by using the inter frame p개cess, we could use the simplest way to extract the weld seam from the image, such as first differential and central difference method. Also we used a moving average method to the successive position data or weld seam for reducing the data fluctuation. In experiment the developed robot system with visual sensor could be able to track a most popular weld seam. such as a fillet-joint, a V-groove, and a lap-joint of which weld seam include planar and height directional variation.

• Proceedings of the KWS Conference
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• 2007.11a
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• pp.67-69
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• 2007

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 dimensional laser welding for non-linear pipe welding line is performed.