ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Artificial neural network model for predicting stable and unstable regions in Cu-Zn alloys

Ravi, R and Prasad, YVRK and Sarma, VVS and Raidu, RS (2006) Artificial neural network model for predicting stable and unstable regions in Cu-Zn alloys. In: Materials and Manufacturing Processes, 21 (8). pp. 756-760.

Full text not available from this repository. (Request a copy)


Processing maps are developed using the Dynamic Materials Model (DMM) and instability criterion, which help in choosing optimum process parameters for hot-working of materials. Certain high-level expertise is required to interpret and extract the information on instability regimes to be avoided during processing. In recent years, Artificial Neural Network (ANN) models have been developed to predict flow stress by using the input vector; namely, temperature, strain rate and strain. In this study, using the available Cu-Zn alloy data, ANN model has been developed to classify the hot-working process parameters, such as temperature, strain rate and flow stress for instability regime, directly from the corrected flow stress data without applying the DMM. This model uses 10 compositions of Cu-Zn system, ranging from 3% Zn to 51% Zn. The developed ANN model has been able to leam the nonlinear classifier, which separates unstable region from the stable region in the Cu-Zn alloy system with zinc content less than 40%.

Item Type: Journal Article
Publication: Materials and Manufacturing Processes
Publisher: Taylor & Francis, Colchester
Additional Information: Copyright of this article belongs to Taylor & Francis.
Keywords: ANN;Data Mining;Dynamic Materials Model;Instability region;Processing Maps
Department/Centre: Division of Mechanical Sciences > Materials Engineering (formerly Metallurgy)
Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 16 May 2008
Last Modified: 27 Aug 2008 13:23
URI: http://eprints.iisc.ac.in/id/eprint/13991

Actions (login required)

View Item View Item