• Journal of Power System Engineering
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• v.18 no.5
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• pp.94-99
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• 2014

In this study, AISI M2 powder was selected primarily through various literature in order to improve the hardness and wear resistance. Among the laser metal deposition parameters, laser power was studied to improve the deposition efficiency in the laser metal deposition using a diode pumped disk laser. SKD61 hot work steel plate and AISI M2 powder were used as a substrate and powder for laser metal deposition, respectively. Fixed parameters are CTWD, focal position, travel speed, powder feed rate, etc. Experiments for the laser metal deposition were carried out by changing laser power. Through optical micrographs analysis of cross-section in LMD track, effect of the major parameters were predicted by track pitch. As the track pitch increased, so the reheated zone width, the overlap width and the minimum thickness was decreased. The hardness was decreased in the HAZ area, the hardness in the reheated HAZ area was decreased significantly and regularly in particular.

• 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.24 no.1
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• pp.88-92
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• 2006

Laser-rotating arc hybrid welding was introduced by combining $CO_2$ laser and rotating gas metal arc welding. While the arc rotation enhances the weld pool motion, it reduces the undercut formation which is one of most critical weld defects in the conventional laser-arc hybrid welding. This research investigated the bead characteristics according to the welding parameters such as frequency of rotation, welding voltage, shielding gas composition and interspacing distance between laser and we. The welding parameters were selected to reduce spatter generation and ensure sound weld beads fur bead welding and butt welding with various joint gaps. Gap bridging ability was improved, such that the sound weld beads were achieved for butt joint with up to 2mm joint sap, with no adjustment of CTWD(Contact tip-to-workpiece distance) and electrode diameter.

• Journal of the Korean Society of Industry Convergence
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• v.5 no.2
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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.

• Journal of Welding and Joining
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• v.31 no.5
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• pp.35-40
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• 2013

In this paper, we propose the Tangible Virtual Reality Representation Method to using haptic device and feature to morphology of created bead from Flux Cored Arc Welding. The virtual reality was started to rising for reduce to consumable materials and welding training risk. And, we will expected maximize virtual reality from virtual welding training. In this paper proposed method is get the database to changing the input factor such as work angle, travelling angle, speed, CTWD. And, it is visualization to bead from extract to optimal morphological feature information to using the Neural Network algorithm. The database was building without error to extract data from automatic robot welder. Also, the Neural Network algorithm was set a dataset of the highest accuracy from verification process in many times. The bead was created in virtual reality from extract to morphological feature information. We were implementation to final shape of bead and overlapped in process by time to using bead generation algorithm and calibration algorithm for generate to same bead shape to real database in process of generating bead. The best advantage of virtual welding training, it can be get the many data to training evaluation. In this paper, we were representation bead to similar shape from generated bead to Flux Cored Arc Welding. Therefore, we were reduce the gap to virtual welding training and real welding training. In addition, we were confirmed be able to maximize the performance of education from more effective evaluation system.