• International Journal of Automotive Technology
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• v.6 no.5
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• pp.513-517
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• 2005

A full-bead of multi-layer-steel engine head gasket, taking charge of the dynamic sealing of combustion chamber, is susceptible to fatigue failure. The fatigue durability of full-bead was assessed with the finite element analysis results and the high-cycle multi-axial fatigue theory. The assessment aimed to reveal the effects of the forming parameters and dimensions of full-bead. The results show that the selection of embossing parameters producing less deformation of bead plate is beneficial for the improvement of durability while the flatting has marginal influence. The fatigue durability also improves with the increase in the width of full-bead and the radial length of bore-side flat region. However, the dimensional effects are limited due to the occurrence of snap-through.

• International Journal of Automotive Technology
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• v.6 no.2
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• pp.191-196
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• 2005

A full-bead of multi-layer-steel (MLS) engine head gasket is used to seal the combustion gas. Finite element analyses were conducted to assess the dependence of the sealing performance of full-bead on the forming process consisting of embossing and flatting operations. It is demonstrated that the sealing performance is enhanced with more severe deformation of the bead plate during the embossing, i.e., with the increase in the punching depth, the punch height, the punch width and the friction coefficient of the bead plate against the punch and die, and with the decrease in the width of die cavity. Meanwhile, the flatting process that is employed to adjust the height of the embossed full-bead has no influence on the sealing performance.

• International Journal of Korean Welding Society
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• v.3 no.2
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• pp.15-23
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• 2003

A genetic algorithm was applied to an arc welding process to determine near optimal settings of welding process parameters which produce good weld quality. This method searches for optimal settings of welding parameters through systematic experiments without a model between input and output variables. It has an advantage of being able to find optimal conditions with a fewer number of experiments than conventional full factorial design. A genetic algorithm was applied to optimization of weld bead geometry. In the optimization problem, the input variables were wire feed rate, welding voltage, and welding speed, root opening and the output variables were bead height, bead width, penetration and back bead width. The number of level for each input variable is 8, 16, 8 and 3, respectively. Therefore, according to the conventional full factorial design, in order to find the optimal welding conditions, 3,072 experiments must be performed. The genetic algorithm, however, found the near optimal welding conditions from less than 48 experiments.

• Journal of Welding and Joining
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• v.27 no.1
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• pp.95-101
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• 2009

In case of manufacturing the high quality welds or pipeline, the full penetration weld has to be made along the weld joint. Thus the root pass welding is very important and has to be selected carefully. In this study, an experimental method for the selection of optimal welding condition was proposed in the root pass welding which was done along the 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. In the experiments, the target values of the back bead width and the height are 6mm and zero respectively for the V-grooved butt weld joint of 8mm thickness mild steel. The optimal welding conditions could predict the back bead profile(bead width and height) as 6.003mm and -0.003mm. From a series of welding test, it was revealed that a uniform and full penetration 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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• v.18 no.5
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• pp.63-69
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• 2000

A genetic algorithm was applied to an arc welding process to determine near optimal settings of welding process parameters which produce good weld quality. This method searches for optimal settings of welding parameters through systematic experiments without a model between input and output variables. It has an advantage of being able to find optimal conditions with a fewer number of experiments than conventional full factorial design. A genetic algorithm was applied to optimization of weld bead geometry. In the optimization problem, the input variables was wire feed rate, welding voltage, and welding speed and the output variables were bead height, bead width, and penetration. The number of level for each input variable is 16, 16, and 8, respectively. Therefore, according to the conventional full factorial design, in order to find the optimal welding conditions, 2048 experiments must be performed. The genetic algorithm, however, found the near optimal welding conditions from less than 40 experiments.

• International Journal of Korean Welding Society
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• v.1 no.1
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• pp.44-50
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• 2001

A genetic algorithm was applied to the arc welding process as to determine the near-optimal settings of welding process parameters that produce the good weld quality. This method searches for optimal settings of welding parameters through the systematic experiments without the need for a model between the input and output variables. It has an advantage of being capable to find the optimal conditions with a fewer number of experiments rather than conventional full factorial designs. A genetic algorithm was applied to the optimization of the weld bead geometry. In the optimization problem, the input variables were wire feed rate, welding voltage, and welding speed. The output variables were the bead height bead width, and penetration. The number of levels for each input variable is 16, 16, and 8, respectively. Therefore, according to the conventional full factorial design, in order to find the optimal welding conditions,2048 experiments must be performed. The genetic algorithm, however, found the near optimal welding conditions in less than 40 experiments.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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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.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.4
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• pp.207-212
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• 2004

Full-beads of multi-layer steel engine head gaskets that are used to seal the combustion gas between the head and the block are subject to cyclic bending stresses due to the variation of the head/block gap during engine operation. The S-N curve for the fatigue durability assessment of the full-bead formed on NBR-coated SUS301 thin plate is deduced from the axial fatigue test results because of the difficulty in conducting the bending fatigue test of thin plate. The experimental verification of the deduced S-N curve is presented. It is shown that the NBR coating increases the endurance limit of the plate significantly. Mechanism of crack nucleation and propagation in the full-bead is discussed with photographs of the fatigue cracks.

• Journal of Welding and Joining
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• v.19 no.3
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• pp.317-324
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• 2001

This study deals with high power Nd:YAG laser welding of thin steels for small pressure vessels. Full penetration welding at the overlap joint was performed so as to assure sufficient weld strength. Results showed that mid-depth weld size reduced drastically with increasing the travel speed. Position of focus had little effect on the bead formation even though short focal system was used. However, the shape factor and the bead width had closely related with the position of focus. Based on the microstructural inspection, acceptable weld was obtained when the overlap clearance was controlled up to 20% of the base metal thickness. In the case that the joint contained more clearance than the critical value, both the tensile shear strength and the tear strength were reduced. Results also demonstrated that shielding gases were proved to play a key role as far as the bead formation characteristics was taken into consideration. Blowing dry air through 5mm in diameter nozzle produced narrower bead cross-section than that of argon or nitrogen shielding.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.596-599
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• 2004

The robotic $CO_2$ 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. To achieve this above objective, Taguchi method was employed using five different process parameters (tip gap, gas flow rate, welding speed, arc current, welding voltage) as a guide for optimization of process parameters.