• Journal of Welding and Joining
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• v.17 no.4
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• pp.39-45
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• 1999

The procedure of robotic Gas metal Arc (GMA) welding in order to achieve the optimized bead geometry needs the selection of suitable process parameters such as arc current, welding voltage, welding speed. It is required the relationships between process parameters and bead geometry. The objective of this paper is to develop the algorithm that enables the determination of process parameters from the optimized bead geometry for robotic GMA welding. It depends on the inversion of empirical equations derived from multiple regression analysis of the relationships between the process parameters and the bead dimensions using the least square method. The method not only directly determines those parameters which will give the desired set of bead geometry, but also avoids the need to iterate with a succession of guesses employed Finite Element Method(FEM). These results suggest that process parameter from experimental equation for robotic GMA welding may be employed to monitor and control the bead geometry in real time.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.2
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• pp.310-316
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• 2003

One of the significant subject in the automatic arc welding is to establish control system of the welding parameters for controlling bead geometry as a criterion to evaluate the quality of arc welding. This paper proposes an inference algorithm for bead geometry in CMA Welding using Neuro-Fuzzy algorithm. The characteristic welding parameters are measured by the circuit composed of hall sensor, voltage divider tachometer, etc. and then the bead geometry of each weld pool is calculated and detected by an image processing with CCD camera and a measuring with microscope. The relationships between the characteristic welding parameters and the bead geometry have been arranged empirically. From the result of experiments, membership functions and fuzzy rules are tuned and determined by the learning of neural network, and then the relationship between actual bead geometry and inferred bead geometry are concluded by fuzzy logic controller. In the applied inference system of bead geometry using Neuro-Fuzzy algorithm, the inference error percent is within -5%∼+4% in case of bead width, -10%∼+10% in bead height, -5%∼+6% in bead area, -10%∼+10% in penetration. Use of the Neuro-Fuzzy algorithm allows the CMA Welding system to evaluate the quality in bead geometry in real time as the welding parameters change.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.5
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• pp.30-35
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• 2008

As the quality of a weld feint is strongly influenced by process parameters during the welding process, an intelligent algorithms that can predict the bead geometry and shape to accomplish the desired mechanical properties of the weldment should be developed. This paper focuses on the development of mathematical models fur the selection of process parameters and the prediction of bead geometry(bead width, bead height and penetration) in robotic GMA(Gas Metal Arc) welding. Factorial design can be employed as a guide for optimization of process parameters. Three factors were incorporated into the factorial model: arc current, welding voltage and welding speed. 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. The results obtained show that developed mathematical models can be applied to estimate the effectiveness of process parameters for a given bead geometry, and a change of process parameters affects the bead width and bead height more strongly than penetration relatively.

• Proceedings of the KWS Conference
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• 2003.05a
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• pp.29-31
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• 2003

Generally, the Quality of a weld joint is strongly influenced by process parameters during the welding process. In order to achieve high quality welds, mathematical models that can predict the bead geometry to accomplish the desired mechanical properties of the weldment should be developed. To achieve this objectives, 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. The results obtained show that developed mathematical models can be applied to estimate the effectiveness of process parameters for a given bead geometry, and a change of process parameters affects the bead width and bead height more strongly than penetration relatively.

• The KSFM Journal of Fluid Machinery
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• v.3 no.4 s.9
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• pp.30-37
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• 2000

Several reverse design methods are developed and applied to the suction or pressure surface for finding design values of blade geometry for a given axial turbine blade. Re-designed blade profiles using shape parameters are compared with measured blade data. Essential shape parameters for blade design are induced by the procedure of reverse design for best fitting. Characteristics of shape parameters are evaluated through the system design method and restriction conditions of structural stability or aerodynamic flow loss. Some of shape parameters i.e blade radius or exit blade angle etc., are classified to weakly adjustable shape parameters, otherwise strongly adjustable shape parameters which would be applied for controlling blade shape. Average deviation values between the measured data and re-designed blade using shape parameters are calculated for each design method. Comparing with the average deviation for a given blade geometry, minimum shape parameters required to design a blade geometry are obtained.

• Coupled systems mechanics
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• v.11 no.2
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• pp.133-149
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• 2022

The accurate prediction of elastoplasticity under prescribed workloads is essential in the optimization of engineering structures. Mechanical experiments are carried out with the goal of obtaining reliable sets of material parameters for a chosen constitutive law via inverse identification. In this work, two sample geometries made of high strength steel plates were evaluated to determine the optimal configuration for the identification of Ludwik's nonlinear isotropic hardening law. Finite element model updating(FEMU) was used to calibrate the material parameters. FEMU computes the parameter changes based on the Hessian matrix, and the sensitivity fields that report changes of computed fields with respect to material parameter changes. A sensitivity analysis was performed to determine the influence of the sample geometry on parameter identifiability. It was concluded that the sample with thinned gauge region with a large curvature radius provided more reliable material parameters.

• Proceedings of the KWS Conference
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• 2003.11a
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• pp.169-171
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• 2003

In this paper, the prediction for the optimized bead geometry such as bead width, height, penetration and bead area in the Gas Metal Arc (GMA) welding with Taguchi method is presented. An orthogonal array, and the Signal-to-Noise (S/N) ratio employed to investigate the welding quality characteristics together in the selection of process parameters in the GMA welding process, to analyze the effect of each process parameter on the bead geometry and to finally determine the process parameters with the optimal bead geometry. Experimental results fi-om this research show that the Taguchi method provides an effective tool to enhance the accuracy of the optimized bead geometry.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.4
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• pp.132-139
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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 FL(fuzzy logic). The parameters of input membership functions and those of consequence functions in FL were tuned through the method of learning by backpropagation algorithm. Bead geometry could be reasoned from welding current, arc voltage, travel speed on FL using the results learned by neural networks. On the developed inference system of bead geometry using neuro-furzy 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 FL. Therefore the inference system of welding quality expects to be developed through proposed algorithm.

• Journal of Welding and Joining
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• v.30 no.6
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• pp.126-132
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• 2012

The GMA welding process involves large number of interdependent variables which may affect product quality, productivity and cost effectiveness. The relationships between process parameters for a fillet joint and bead geometry are complex because a number of process parameters are involved. To make the automated GMA welding, a method that predicts bead geometry and accomplishes the desired mechanical properties of the weldment should be developed. The developed method should also cover a wide range of material thicknesses and be applicable for all welding position. For the automatic welding system, the data must be available in the form of mathematical equations. In this study a new intelligent model with genetic algorithm has been proposed to investigate interrelationships between welding parameters and bead geometry for the automated GMA welding process. Through the developed model, the correlation between process parameters and bead geometry obtained from the actual experimental results, predicts that data did not show much of a difference, which means that it is quite suitable for the developed genetic algorithm. Progress to be able to control the process parameters in order to obtain the desired bead shape, as well as the systematic study of the genetic algorithm was developed on the basis of the data obtained through the experiments in this study can be applied. In addition, the developed genetic algorithm has the ability to predict the bead shape of the experimental results with satisfactory accuracy.

• Structural Engineering and Mechanics
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• v.82 no.1
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• pp.1-16
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• 2022

This work elaborately investigates the influences of the guideway geometry parameters and track irregularity on the dynamic performances of the suspended monorail vehicle-guideway system (SMVGS). Firstly, a spatial dynamic analysis model of the SMVGS is established by adopting ANSYS parameter design language. Then, the dynamic interaction between a vehicle with maximum design load and guideway is investigated by numerical simulation and field tests, revealing the vehicle-guideway dynamic features. Subsequently, the influences of the guideway geometry parameters and track irregularity on the dynamic performances of the SMVGS are analyzed and discussed in detail, and the reasonable ranges of several key geometry parameters of the guideway are also obtained. Results show that the vehicle-guideway dynamic responses change nonlinearly with an increase of the guideway span, and especially the guideway dynamic performances can be effectively improved by reducing the guideway span; based on a comprehensive consideration of all performance indices of the SMVGS, the deflection-span ratio of the suspended monorail guideway is finally recommended to be 1/1054~1/868. The train load could cause a large bending deformation of the pier, which would intensify the car-body lateral displacement and decrease the vehicle riding comfort; to well limit the bending deformation of the pier, its cross-section dimension is suggested to be more than 0.8 m×0.8 m. The addition of the track irregularity amplitude has small influences on the displacements and stress of the guideway; however, it would significantly increase the vehicle-guideway vibrations and rate of load reduction of the driving tyre.