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Development of Statistical Model and Neural Network Model for Tensile Strength Estimation in Laser Material Processing of Aluminum Alloy  

Park, Young-Whan (한양대학교 BK21 혁신 설계 기계인력 양성사업단)
Rhee, Se-Hun (한양대학교 기계공학부)
Publication Information
Journal of the Korean Society for Precision Engineering / v.24, no.4, 2007 , pp. 93-101 More about this Journal
Abstract
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.
Keywords
Laser Welding; Filler Wire; Tensile Strength; Regression Model; Neural Network Model; Average Error Rate;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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