|About this Abstract
||Materials Science & Technology 2019
||Late News Poster Session
||P2-98: Genetic Algorithm Optimization of Process Parameters in Resistance Spot Welding of Automotive Steel Sheets
||Feujofack Kemda Bleriot Vincent, Noureddine Barka, Mohammad Jahazi, Denis Osmani
|On-Site Speaker (Planned)
||Feujofack Kemda Bleriot Vincent
This paper presents an efficient method to minimize production energies in resistance spot welding of automotive thin steel sheets. Two grades of steel were used, A36 and A653 hot dipped galvanized steels. Welding was carried out in overlap configuration, following complete factorial plans. Micrographic analysis revealed welds microstructure, micro-indentation hardness tests enabled to establish hardness profiles along weld nuggets, and tensile-shear tests enabled to quantify mechanical strength of welds. The ratio of nugget hardness to nugget surface area was found to be correlated with mechanical strength of welded specimens. On that basis, a multi-objective optimization of the process parameters, through non-dominated sorting genetic algorithm was performed. This optimization results in a reduction of current, electrode pressing force and welding time of 10.54%, 13.95% and 31.88% respectively. Optimized parameters were then assessed trough tensile-shear testing of welded specimens, all specimens passed the validation tests by experiencing failure in the base metal.