• Laser Solutions
• /
• v.13 no.3
• /
• pp.7-12
• /
• 2010

In this study, laser welding of aluminum AA5182 with AA5356 filler wire was carried out and the formability of the weld joint was evaluated through Erichsen test according to laser power, welding speed and wire feed rate. Fracture was occurred in both directions, perpendicular and parallel to the weld line at 0.75 of Erichsen ratio. Second order Regression model to estimate Erichsen ratio with experimental variables was proposed and the performance of model was evaluated with F-test and average error rate.

• Proceedings of the KWS Conference
• /
• 2009.11a
• /
• pp.111-111
• /
• 2009

전 세계적으로 환경 보호의 차원에서 자동차 업체는 자동차의 연비 향상을 위한 차체의 경량화가 큰 이슈로 대두되고 있다. 이를 위해 알루미늄과 같은 경량화 소재를 이용하여 차체 조립에 투입하고자 연구 중에 있다. 이와 같은 레이저 용접 공정이 현장에 적용되기 위해서는 용접부의 품질을 실시간으로 모니터링하고 품질을 판단하여야 생산성을 극대화 할 수 있다. 그러므로 본 연구에서는 알루미늄 AA5182 알루미늄 판재의 용가 와이어를 이용한 레이저 용접에서 용접부를 모니터링 할 수 있는 시스템을 구축하였다. 이를 위하여 레이저는 4kW급 Nd:YAG 레이저를 사용하였고, 차체용 알루미늄 판재 AA5182 1.4t를 AA5356 와이어를 이용하여 용접을 수행하였다. 모니터링 센서로는 반응 범위가 190 mn~680 nm인 센서를 이용하였고, 용접 중 센서로부터 발생된 출력전류를, 신호 증폭기와 DAQ 보드를 통해 초당 10,000 samples/sec로 계측하였다. 다양한 용접조건을 이용하여 실험을 수행하였고 이를 정량적으로 분석하였다. 계측된 신호와 용접 품질은 비선형적 관계를 가지고 있으므로 본 연구에서는 용접 품질을 예측하는 방법으로 퍼지 패턴인식 알고리즘을 이용하는 방법과 계측 신호를 이용한 인장강도 예측모델을 이용하여 병렬로 품질평가를 할 수 있는 알고리즘을 구현하였다. 이를 위하여 계측된 신호와 용접 품질과의 관계를 이용하여 퍼지 규칙 베이스 정의하였고, 신경회로망 모델을 이용하여 인장강도 예측모델을 제시하였다. 또한 품질 평가 알고리즘을 기반으로 레이저 용접부의 품질평가가 가능한 GUI 프로그램을 구현하였다.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.14 no.2
• /
• pp.57-66
• /
• 2006

AA5182-polypropylene sandwich sheet was manufactured, and the mechanical properties evaluation was executed in order to identify $L{\ddot{u}}ders$ band that causes fabrication process problem and especially surface roughness. To identify formability, deformation behavior, plastic strain ratio (R-value) and pole figure were measured, and texture analysis was performed. In the case of sandwich sheet, the unstable deformation behavior has decreased. As well, for sandwich sheet, A1 skin could manage the most of load, and the elongation has improved about 45% more than that of A1 skin. The plastic strain ratio of A1 skin and sandwich panel, which indicates serration behavior, was obtained from instantaneous plastic strain ratio evaluation. Also, the planar anisotropy of sandwich sheet has decreased more than that of A1 skin. According to these results, the sandwich sheet produced lightening effect and could control unstable deformation characteristic, that is, surface roughness caused by $L{\ddot{u}}ders$ band. Furthermore, it was proved that the texture control of the rolling attachment of A1 skin is necessary to improve the formability of the sandwich panel.

• Journal of Welding and Joining
• /
• v.24 no.5
• /
• pp.67-73
• /
• 2006

Laser welding is suitable for welding to the aluminum alloy sheet. In order to apply the aluminum laser welding to production line, parameters should be optimized. In this study, the optimal welding condition was searched through the genetic algorithm in laser welding of AA5182 sheet with AA5356 filler wire. Second-order polynomial regression model to estimate the tensile strength model was developed using the laser power, welding speed and wire feed rate. Fitness function for showing the performance index was defined using the tensile strength, wire feed rate and welding speed which represent the weldability, product cost and productivity, respectively. The genetic algorithm searched the optimal welding condition that the wire feed rate was 2.7 m/min, the laser power was 4 kW and the welding speed was 7.95 m/min. At this welding condition, fitness function value was 137.1 and the estimated tensile strength was 282.2 $N/mm^2$.

• Laser Solutions
• /
• v.8 no.3
• /
• pp.11-19
• /
• 2005

In automotive industry, light weight vehicle is one of issues because of the air pollution and the protection of environment. Therefore, automotive manufacturers have tried to adopt light materials such as aluminum alloy to production line. Aluminum welding using laser has some advantages high energy density and high productivity. It is very important to understand weld characteristic according to welding condition in order to determine the possibility of application to car body. In this study, Nd:YAG laser welding of 5182 aluminum alloy with filler wire AA5356 was carried out through experimental design according to wire feed rate, laser power and welding speed. Weld bead shape in terms of cross section photo, bead with, height of reinforcement and penetration depth and mechanical property in terms of tensile strength and formability was investigated. Analysis of variation (ANOVA) was performed to know the effect of weld parameter for weldability, laser power was statistically most significance factor of three variables.

• Journal of the Korean Society for Precision Engineering
• /
• v.24 no.4 s.193
• /
• pp.93-101
• /
• 2007

Aluminum alloy which is one of the light materials has been tried to apply to light weight vehicle body. In order to do that, welding technology is very important. In case of the aluminum laser welding, the strength of welded part is reduced due to porosity, underfill, and magnesium loss. To overcome these problems, laser welding of aluminum with filler wire was suggested. In this study, experiment about laser welding of AA5182 aluminum alloy with AA5356 filler wire was performed according to process parameters such as laser power, welding speed and wire feed rate. The tensile strength was measured to find the weldability of laser welding with filler wire. The models to estimate tensile strength were suggested using three regression models and one neural network model. For regression models, one was the multiple linear regression model, another was the second order polynomial regression model, and the other was the multiple nonlinear regression model. Neural network model with 2 hidden layers which had 5 and 3 nodes respectively was investigated to find the most suitable model for the system. Estimation performance was evaluated for each model using the average error rate. Among the three regression models, the second order polynomial regression model had the best estimation performance. For all models, neural network model has the best estimation performance.