• 한국강구조학회 논문집
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• 제10권4호통권37호
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• pp.709-720
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• 1998

용접잔류응력의 모재두께방향을 포함하는 3차원 분포를 파악하기 위해, 용접상세를 변화시킨 필렛용접이음을 대상으로 해서 실험 및 해석을 수행하였다. 특히, 지금까지 계측이 곤란했던 필렛용접이음의 용접루트부의 잔류응력을 실측하였다. 파라메타로써는 용접입열량과 용접층수를 취급하고, 모재두께방향을 포함하는 3차원 잔류응력의 분포를 조사하였다. 그 결과, 입열량이 증가하면, 용접토우와 루트부를 포함하는 용접부에서는 잔류응력의 크기에 변화가 거의 없지만, 인장잔류응력의 영역이 크게 나타났다. 또 단층과 다층용접의 비교에서는, 다층용접 쪽이 단층용접보다 잔류응력이 상당히 낮음을 알 수 있었다. 용접부 근방의 인장잔류응력의 영역도 다층용접 쪽이 단층용접보다 작게 나타난 것을 알 수 있었다.

• Journal of Welding and Joining
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• 제14권4호
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• pp.53-61
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• 1996

It was attempted to improve the wear resistance of Al alloy under the load condition by making a formation of the thicker surface hardening alloy layers. The thicker surface hardening alloy layers were formed on 6061 Al alloys overlayed by MIG and TIG welding process with Cu powders feeding. The characteristics of hardening and wear resistance have been investigated in relation to the microstructures of alloyed layers, with a selection of optimum alloying conditions for formation of overlaying layer. The results obtained were summarized as follows With increasing feeding rate of Cu powders by MIG welding, the hardness and specific wear of the overlay weld alloys were increased. It is considered that these high hardness and specific wear of overlay weld alloys were due to the formation of Θ($Al_2Cu$) phases. With increasing feeding rate of Cu powders by TIG welding, the hardness and specific wear of the overlay weld alloys were increased in feeding rates 12 and 18g/min. However, the hardness and specific wear were decreased in the powder feeding rate 38g/min. It is considered that considered that decrease of hardness and specific wear in the powder feeding rate 38g/min due to formation of ${\gamma}$($Al_4Cu_9$) phases.

• 대한용접접합학회:학술대회논문집
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• 대한용접접합학회 1998년도 특별강연 및 추계학술발표 개요집
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• pp.250-252
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• 1998

• Journal of Welding and Joining
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• 제34권3호
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• pp.69-76
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• 2016

Characteristics of dissimilar metal welds between ASTM Type 316L and carbon steel ASTM A516 Gr.70 made with FCAW were evaluated in terms of microstructure, ferrite content, EDS analysis, hardness, tensile strength, impact toughness and corrosion resistance. Three heat inputs of 10.4, 16.9, 23.4kJ/cm were employed to make joints of dissimilar metals with E309LMoT1-1 wire. Microstructure of dissimilar weld metals consisted of mostly vermicular type of ${\delta}$-ferrite and some lathy type of ${\delta}$-ferrite, and ${\delta}$-ferrite was transformed into globular type in reheated zone. In all conditions, weld metals were solidified on FA solidification mode. Based on the EDS analysis of weld metals, All Creq/Nieq values were in the range of FA solidification mode, and it was decreased with increasing heat inputs whereas it was increased with increasing layers. The amount of ${\delta}$-ferrite was decreased with increasing heat input due to the difference of cooling rate, and it was increased with increasing layers. Accordingly, hardness and tensile strength of dissimilar metals weld joints was decreased with increasing heat input while impact energy was increased with increasing heat input. Corrosion test of dissimilar metals weld joints showed that weight gain rate of heat input 10.4kJ/cm was the greatest, and that of three heat inputs became constant after certain time.

• 한국생산제조학회지
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• 제8권5호
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• pp.70-75
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• 1999

Adaptive control in the robotic GMA(Gas Metal Arc) welding is employed to monitor the information about weld characteristics and process paramters as well as modification of those parameters to hold weld quality within the acceptable limits. Typical characteristics are the bead geometry composition micrrostructure appearance and process parameters which govern the quality of the final weld. The main objectives of this paper are to realize the mapping characteristicso f penetration through the learning. After learning the neural network can predict the pene-traition desired from the learning mapping characteristic. The design parameters of the neural network estimator(the number of hidden layers and the number of nodes in a layer) were chosen from an error analysis. partial-penetration single-pass bead-on-plate welds were fabricated in 12mm mild steel plates in order to verify the performance of the neural network estimator. The experimental results show that the proposed neural network estimator can predict the penetration with reasonable accuracy and gurarantee the uniform weld quality.

• 한국생산제조학회지
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• 제7권6호
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• pp.57-63
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• 1998

An adaptive control in the robotic GMA welding is employed to monitor information about weld characteristics and process parameters as well as to modify those parameters to hold weld quality within acceptable limits. Typical characteristics are the bead geometry, composition, microstructure, appearance, and process parameters which govern the quality of the final weld. The main objectives of this thesis are to realize the mapping characteristics of bead width through learning. After learning, the neural estimation can estimate the bead width desired form the learning mapping characteristic. The design parameters of the neural network estimator(the number of hidden layers and the number of nodes in a layer) are chosen from an estimation error analysis. A series of bead of bead-on-plate GMA welding experiments was carried out in order to verify the performance of the neural network estimator. The experimental results show that the proposed neural network estimator can predict the bead width with reasonable accuracy and guarantee the uniform weld quality.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 춘계학술대회 논문집
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• pp.430-433
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• 2005

Laser bonding between similar and dissimilar thermoplastics has been investigated by making use of laser transmission weld technique. Spot welding of two layers of plastic materials has been demonstrated by using of a high-quality diode-laser with 808nm wavelength. Weld areas increases according to power density, exposure time. The results of peel out test show that peel strengths increase with the area of molten plastics. Layers, which have the same chemical properties, have good bonding qualities. A bonding method which dye film is coated on the interface is used for laser bonding between plastics with high transmission for laser wavelength. Laser transmission bonding is worthy of attention because it is not in contact, requires a few tooling devices, allows a flexible energy delivery and produces nearly invisible welds

• International Journal of Korean Welding Society
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• 제5권1호
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• pp.10-14
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• 2005

An element of Pure Weld Metal(PWM) is important factor to understand the Flux's conduct in Submerged Arc Welding(SAW). To get the element of PWM, pile-up welding over than 10 layers have been used in the past. But, it took a long time to analyze the elements of PWM in this method. Therefore, in this study, instead of pile-up welding over than 10 layers, one pass bead welding is used to predict an element of PWM using mathematical formula which got to be derived. As a results that applied the formula, there was no differences between theoretical and experimental value except the element Mn and Si.

• Journal of Welding and Joining
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• 제34권4호
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• pp.55-61
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• 2016

During laser overlap welding of galvanized steel sheets, explosion of weld pool by the high pressure zinc vapor induces weld defects like porosity and blowhole. In this study, laser 2-pass welding was implemented to prevent the weld defects. Through the 1st pass welding, zinc layers on the faying surfaces were removed when proper heat input was applied. Excessive heat input could result in explosion even during the 1st pass welding and insufficient heat input could not remove enough region of zinc layer for the 2nd pass welding. Coating weights of $45g/m^2$ and $60g/m^2$ were considered and for both cases sound welds without weld defects could be achieved. In spite of 2-pass welding, softening of weld and heat affected zone was not observed and Zn coating was not diluted into the weld metal.

• 한국기계가공학회지
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• 제21권6호
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• pp.89-97
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• 2022

Ultrasonic metal welding has been widely used for joining lithium-ion battery tabs. Weld quality monitoring has been an important issue in lithium-ion battery manufacturing. This study focuses on the weld quality monitoring in ultrasonic metal welding with the longitudinal-torsional vibration mode horn developed newly. As the quality of ultrasonic welding depends on welding parameters like pressure, time, and amplitude, the suitable values of these parameters were selected for experimentation. The welds were tested via tensile testing machine and weld strengths were investigated. The dataset collected for performance test was used to train the multi-layer perceptron neural network. The three layer neural network was used for the study and the optimum number of neurons in the first and second hidden layers were selected based on performances of each models. The best models were selected for the horn and then tested to see their performances on an unseen dataset. The neural network models for the longitudinal-torsional mode horn attained test accuracy of 90%. This result implies that proposed models has potential for the weld quality monitoring.