Statistical modeling and optimization of resistance spot welding process parameters using neural networks and multi-objective genetic algorithm

Author:

Pashazadeh Hamed,Gheisari Yousof,Hamedi Mohsen

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Industrial and Manufacturing Engineering,Software

Reference30 articles.

1. Bishop, C. M. (1996). Neural network for pattern recognition. Oxford: Oxford University Press.

2. Canyurt, O. E. (2005). Estimation of welded joint strength using genetic algorithm approach. International Journal of Mechanical Sciences, 47, 1249–1261.

3. Darwish, S. M., & Al-Dekhial, S. D. (1999). Statistical models for spot welding of commercial aluminium sheets. International Journal of Machine Tools and Manufacture, 39, 1589–1610.

4. Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000). A fast elitist non-dominated sorting genetic algorithm for multi objective optimization: NSGA-II. In M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E. Lutton, J. J. Merelo, & H. P. Schwefel (Eds.), Parallel problem solving from nature (PPSN VI). Lecture notes in computer science (Vol. 1917). Berlin: Springer.

5. Eisazadeh, H., Hamedi, M., & Halvaee, A. (2010). New parametric study of nugget size in resistance spot welding process using finite element method. Materials and Design, 31, 149–157.

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