• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.2
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• pp.165-173
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• 2004

Laser beam welding is increasingly being used in welding of structural steels. The laser welding process is one of the most advanced manufacturing technologies owing to its high speed and deep penetration. The thermal cycles associated with laser welding are generally much faster than those involved in conventional arc welding processes, leading to a rather small weld zone. Experiments are performed for 304 stainless steel plates changing several process parameters such as laser power, welding speed, shielding gas flow rate, presence of surface pollution, with fixed or variable gap and misalignment between the similar and dissimilar plates, etc. The following conclusions can be drawn that laser power and welding speed have a pronounced effect on size and shape of the fusion zone. Increase in welding speed resulted in an increase in weld depth/ aspect ratio and hence a decrease in the fusion zone size. The penetration depth increased with the increase in laser power.

• Transactions of the Korean Society of Automotive Engineers
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• v.14 no.3
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• pp.44-50
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• 2006

The mono steel forged piston was improved a mechanical strength of an aluminum piston and reduced the weight of a articulated piston. The mono steel forged piston was composed of forged crown part and forged skirt part and was completed by friction welding process of two forged parts. Forging process analysis and friction welding analysis was done by finite element simulation using numerical package DEFORM. The preform shape and the initial billet dimension were decided by maximum stress of the die, amount of the flash and filling of die. The upset length of friction welding variable was decided by the shape of the flash that was created by friction welding analysis. Through this research, we developed a forging process of the mono steel forged piston, and decided the design variables of friction welding.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1560-1565
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• 2005

Laser beam welding is increasingly being used in welding of structural steels. The laser welding process is one of the most advanced manufacturing technologies owing to its high speed and deep penetration. The thermal cycles associated with laser welding are generally much faster than those involved in conventional arc welding processes, leading to a rather small weld zone. Experiments are performed for 304 stainless steel plates changing several process parameters such as laser power, welding speed, shielding gas flow rate, presence of surface pollution, with fixed or variable gap and misalignment between the similar and dissimilar plates, etc. The following conclusions can be drawn that laser power and welding speed have a pronounced effect on size and shape of the fusion zone. Increase in welding speed resulted in an increase in weld depth/ aspect ratio and hence a decrease in the fusion zone size. The penetration depth increased with the increase in laser power.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.406-408
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• 1997

This paper presents the new method of a nondestructive spot welding state inspection system using neural networks. The learning process of neural networks makes the inspection system to adapt the variable welding parameters. The inspecting process is working with on-line real-time after off-line learning process. This neural network based inspection system shows reliable results through the field test for variations of applied voltages, currents, and contact area of the welding electrode.

• Journal of Welding and Joining
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• v.33 no.2
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• pp.14-17
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• 2015

Cleaning effect is well known mechanism of oxide layer removal in DCEP polarity. It is also known that DCEN has higher heat input efficiency than DCEP in GTAW process. Based on these two renowned arc theories, conventional variable polarity arc for aluminum welding was set up to have minimum DCEP and maximum DCEN duty ratio to achieve the highest heat input efficiency and weldability increase. However, recent several variable polarity GTA research papers reported unexpected result of proportional relationship between DCEP duty ratio and heat input. The authors also observed the same result then suggested combination of tunneling effect and random walk of cathode spot to fill up the gap between experiment and conventional arc theory. In this research, suggested combinational work of tunneling effect and rapid cathode spot changing is applied to another unexpected phenomena of variable polarity aluminum arc welding. From previous research, it is reported that wider oxide removal range, narrower bead width and shallower penetration depth are observed in thin oxide layered aluminum compared to the case of thick oxide. This result was reported for the first time and it was hard to explain the reason at that time therefore the inference by the authors was hardly acceptable. However, the suggested combinational theory successfully explains the result of the previous report in logical way.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.1154-1159
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• 2004

Laser beam welding is increasingly being used in welding of structural steels. The laser welding process is one of the most advanced manufacturing technologies owing to its high speed and deep penetration. The thermal cycles associated with laser welding are generally much faster than those involved in conventional arc welding processes, leading to a rather small weld zone. Experiments are performed for Inconel 600 plates changing several process parameter such as laser power, welding speed, shielding gas flow rate, presence of surface pollution, with fixed or variable gap and misalignment between plate and plate, etc. The follow conclusions can be drawn that laser power and welding speed have a pronounced effect on size and shape of the fusion zone. Increase in welding speed resulted in an increase in weld depth/ aspect ratio and hence a decrease in the fusion zone size. The penetration depth increased with the increase in laser power . Welding characteristics of austienite Inconel 600 using a continuous wave Nd:YAG laser are experimentally investigated. This paper describes the weld ability of inconel 600 for machine structural use by Nd:YAG laser.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.6
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• pp.8-14
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• 2022

In this study reports experiments were conducted to determine the quality of weld beads of different materials, Al and Cu. Among the lasers used to make battery cells for electric vehicles, non-destructive testing was performed using deep learning to determine the quality of beads welded with the ARM laser. Deep learning was performed using AlexNet algorithm with a convolutional neural network structure. The results of quality identification were divided into good and bad, and the result value was derived that all the results were in agreement with 94% or more. Overall, the best welding quality was obtained in the experiment for the fixed ring beam output/variable center beam output, in the case of the fixed beam (ring beam) 500W and variable beam (center beam) 1,050W; weld bead failure was seldom observed. The tensile force test to confirm the reliability of welding reported an average tensile force of 2.5kgf/mm or more in all sections.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.287-292
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• 2004

In this study, we made a comparative study of backpropagation neural network and probabilistic neural network and bayesian classifier and perceptron as shape recognition algorithm of welding flaws. For this purpose, variables are applied the same to four algorithms. Here, feature variable is composed of time domain signal itself and frequency domain signal itself, Through this process, we confirmed advantages/disadvantages of four algorithms and identified application methods of few algorithms.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.271-276
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• 2003

In this study, we compared backpropagation neural network(BPNN) with probabilistic neural network(PNN) as shape recognition algorithm of welding flaws. For this purpose, variables are applied the same to two algorithm. Here, feature variable is composed of time domain signal itself and frequency domain signal itself, Through this process, we comfirmed advantages/disadvantages of two algorithms and identified application methods of two algorithms.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.280-285
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• 2004

In this study, we made a comparative study of backpropagation neural network and probabilistic neural network and bayesian classifier and perceptron as shape recognition algorithm of welding flaws. For this purpose, variables are applied the same to four algorithms. Here, feature variable is composed of time domain signal itself and frequency domain signal itself. Through this process, we comfirmed advantages/disadvantages of four algorithms and identified application methods of four algorithms.