Publication Type:

Conference Paper

Source:

Proceedings of the 2nd International Conference on Communication and Electronics Systems, ICCES 2017, IEEE Xplore, Volume 2018-January, p.716-720 (2017)

ISBN:

9781509050130

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85047124799&doi=10.1109%2fCESYS.2017.8321174&partnerID=40&md5=ea43503d09aac3b39d649521d565c8bf

Keywords:

Aluminum alloys, Aluminum corrosion, Artificial neural network modeling, Corrosion resistance, Corrosion resistant alloys, Experimental trials, Forecasting, Friction, Friction stir process, Friction stir processing, Friction stir welding, Intergranular corrosion, Intergranular corrosion susceptibilities, L18 orthogonal array, Levenberg-Marquardt algorithm, Mean square error, Network architecture, Neural networks, Root mean squared errors, Taguchi methods, Tribology

Abstract:

Aluminium alloy AA5083 was subjected to friction stir processing with an objective to increase the intergranular corrosion resistance of the alloy. Experimental trials were performed by varying the friction stir process parameters namely Tool Rotation Speed, Tool Traverse Speed and Shoulder Diameters as per Taguchi's L18 orthogonal array. The base specimen and friction stir processed specimens were subjected to intergranular corrosion susceptibility test according to the standard ASTM G67-04. Artificial Neural Network model was developed with cascade forward propagation network architecture to predict the intergranular corrosion susceptibility of the friction stir processed specimens. The network was trained with 80% experimental data using Levenberg Marquardt algorithm and the remaining data was used for testing and validation. Least root mean squared error value and prediction error indicated high accuracy of the developed model. © 2017 IEEE.

Notes:

cited By 0; Conference of 2nd International Conference on Communication and Electronics Systems, ICCES 2017 ; Conference Date: 19 October 2017 Through 20 October 2017; Conference Code:135455

Cite this Research Publication

R. Harikeshava, Srinivasan, M. S., Vaira Vignesh R., and Dr. Padmanaban R., “ANN model for predicting the intergranular corrosion susceptibility of friction stir processed aluminium alloy AA5083”, in Proceedings of the 2nd International Conference on Communication and Electronics Systems, ICCES 2017, 2017, vol. 2018-January, pp. 716-720.