Prediction of gas metal arc welding parameters based on artificial neural networks

Materials and Design

Volumn 28, Issue 7, 2007, Pages 2015-2023

  Ates, Hakan ^a  

^a GAZI UNIVERSITY   (Turkey)

Author keywords

Artificial neural networks; Gas metal arc welding; Mechanical properties

Indexed keywords

COMPUTER SIMULATION; ELONGATION; GAS MIXTURES; HARDNESS; IMPACT STRENGTH; LEARNING ALGORITHMS; MATHEMATICAL MODELS; NEURAL NETWORKS; TENSILE STRENGTH;

DELTA-BAR-DELTA LEARNING ALGORITHM;

GAS METAL ARC WELDING;

COMPUTER SIMULATION; ELONGATION; GAS METAL ARC WELDING; GAS MIXTURES; HARDNESS; IMPACT STRENGTH; LEARNING ALGORITHMS; MATHEMATICAL MODELS; NEURAL NETWORKS; TENSILE STRENGTH;

EID: 34249066848     PISSN: 02613069     EISSN: 18734197     Source Type: Journal    
DOI: 10.1016/j.matdes.2006.06.013     Document Type: Article

References (15)

1. Kim I.-S., Son J.-S., and Yarlagadda P.K.D.V. A study on the quality improvement of robotic GMA welding process. Robot Cim-Int Manuf 19 6 (2003) 567-572
2. Welding handbook fundamentals of welding, vol. 1, 7th ed. Miami, FL: American Welding Society; 1981. p. 7-9.
3. Welding handbook welding processes-arc and gas welding and cutting, brazing and soldering, vol. 2, 7th ed. Miami, FL: American Welding Society; 1978. p. 114-6.
4. Tusek J., and Suban M. Experimental research of the effect of hydrogen in argon as a shielding gas in arc welding of high alloy stainless steel. Int J Hyd Ener 25 4 (2000) 369-376
5. Jang K.C., Lee D.G., Kuk J.M., and Kim I.S. Welding and environmental test condition effect in weldability and strength of Al alloy. J Mater Process Technol 164-165 15 (2005) 1038-1045 May
6. Han J.M., and Han Y.S. Welding technology and strength of GT membrane type LNG ships. J Korean Weld Soc 11 4 (1993) 7-15
7. Lucas B. Shielding gases for arc welding: part I. Weld Met Fabr (1992) 218-225
8. Yildiz C., Gultekin S., Guney K., and Sagiroglu S. Neural models for the resonant frequency of electrically thin and thick circular microstrip antennas and the characteristic parameters of an asymmetric coplanar waveguides with a conductor backed. Int J Electron Commun (AEÜ) 56 6 (2002) 309-406
9. Sözen A., Arcaklioglu E., and Özalp M. Formulation based on artificial neural network of thermodynamic properties of ozone friendly refrigerant/absorbent couples. Appl Therm Eng 25 11-12 (2005) 1808-1820 August
10. Okuyucu H., Kurt A., and Arcaklioglu E. Artificial neural network application to the friction stir welding of aluminum plates. Materials & Design 28 1 (2007) 78-84
11. Kim I.S., Son J.S., Park C.E., Kim I.J., and Kim H.H. An investigation into an intelligent system for predicting bead geometry in GMA welding process. J Mater Process Technol 159 1 (2005) 113-118
12. Maren A., Harston C., and Pap R. Handbook of neural computing applications (1990), London Academic Press. 0-12-471260-6
13. Haykin S. Neural networks: a comprehensive foundation (1994), Macmillan College Publishing Company, New York. 0-02-352761-7
14. Minai AA, Williams RD. Acceleration of back propagation through learning rate and momentum adaptation. In: International joint conference on neural networks, vol. 1; 1990. p. 676-9.
15. Neural Computing, A technology handbook for professional II/PLUS and neural works explorer, Pittsburgh: Neural Ware Inc; Technical Publications Group, 1996.