Development of Intelligent Model for Strength Estimation in Aluminum Remote Laser Welding
Many automotive companies have tried to reduce the car weight to improve the power efficiency. Light metals such as aluminum alloy or magnesium alloy were suitable materials for weight saving. However, it was not easy to join these metals due to their high conductivity. Thus, the laser has used to weld aluminum alloy because it has highly concentration energy. In addition, using the remote welding method could dramatically increase productivity, which is one of the advantages of laser welding. In this study, disk laser welding of AA 5J32 using remote welding with galvano scanner was carried out to enhance the welding speed and productivity comparing with conventional spot welding. In order to investigate the feasibility of aluminum remote welding applying to car bodies, bead shape and tensile shear strength were investigated according to laser power, welding speed and laser incidence angle. In addition a model to estimate tensile strength by the artificial neural network was proposed. The performance of the neural network model was verified with average error rate and coefficient of determinant, and it had a good estimation performance.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
Document Type: Research Article
Publication date: 01 July 2012
More about this publication?
- ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.
- Editorial Board
- Information for Authors
- Subscribe to this Title
- Ingenta Connect is not responsible for the content or availability of external websites