Publication Type:

Journal Article

Source:

International Journal of Vehicle Structures and Systems, MechAero Found. for Techn. Res. and Educ. Excellence, Volume 10, Issue 2, p.98-102 (2018)

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85049059394&doi=10.4273%2fijvss.10.2.05&partnerID=40&md5=028167155b0c1e3619a52e54ae957fc5

Keywords:

Accurate prediction, Aluminum alloys, Artificial neural network modeling, Central composite designs, Experimental trials, Forecasting, Forward propagation, Friction, Friction stir welded joints, Friction stir welding, High strength alloys, Light weight structures, Network architecture, Neural networks, Process parameters, Research laboratories, Solid-state welding process, Tensile strength, Tribology

Abstract:

Aluminium alloy AA1100 finds application in light weight structures due to its high strength to weight ratio. Friction stir welding is a solid state welding process, in which the materials are joined in the plasticized state. The quality of the friction stir welded joints depends on the process parameters used and tool parameters. In this study, four process parameters were varied at five levels and experimental trials were performed as per face centered central composite design. Artificial neural network model was developed with cascade forward propagation network architecture and trained with LM algorithm and BFGS QN algorithm. The models were used to predict the tensile strength of the joints and the error in prediction was used to judge the accuracy of the developed models. It is observed that BFGS QN algorithm trains the ANN efficiently and results in accurate predictions. © 2018.

Notes:

cited By 0

Cite this Research Publication

Vaira Vignesh R. and Dr. Padmanaban R., “Comparison of ANN training algorithms for predicting the tensile strength of friction stir welded aluminium alloy AA1100”, International Journal of Vehicle Structures and Systems, vol. 10, no. 2, pp. 98-102, 2018.