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Journal of Applied Sciences
  Year: 2012 | Volume: 12 | Issue: 10 | Page No.: 902-910
DOI: 10.3923/jas.2012.902.910
 
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Computational Modeling of High Temperature Processing Maps for Microalloyed Al-Cu-Mg Alloys Using Artificial Neural Networks

Sanjib Banerjee, Nastagis Niyaz Ahmed, Pranjal Bhuyan and Subhashish Baruah

Abstract:
High temperature deformation behavior of Al-5.9, Cu-0.5%, Mg alloy and Al-5.9, Cu-0.5%, Mg alloy containing 0.06 wt.% Sn was studied by hot compression tests conducted at different temperatures and strain rates. Trace content of Sn resulted in a significant increase of flow stress for various processing conditions. Artificial Neural Network (ANN) modeling was applied providing excellent prediction of flow stress at different combinations of strain, strain rate and deformation temperature. While validation, it was possible to predict 100 and 89% of the flow stress values of the respective alloys within an error less than±10%. The flow stress data thus generated using the ANN architectures was used to develop power dissipation efficiency maps, instability maps and subsequently the processing maps which would delineate the process domains for safe metal working. Optimum hot processing window was suggested for the investigated alloys, providing intelligent processing and manufacturing system of these wrought microalloyed Al alloys, extensively used in aircraft and space applications. The power dissipation efficiency maps revealed a maximum efficiency of 60% for the alloy without Sn content, while a comparatively lower value of 40% for the microalloyed material. The instability maps generated for the alloy containing Sn, revealed only one instability regime. The safe regimes for hot working of the base alloy without Sn content were observed at (i) very low strain rate (<0.003 sec-1) with temperature <450°C and (ii) high temperature (>400°C) with strain rate >0.02 sec-1. The safe processing zone of the alloy with trace content of Sn, is at low strain rate (<0.01 sec-1) for the entire range of temperatures studied. Microstructural analysis confirmed that dynamic recovery (DRV) and dynamic recrystallization (DRX) characterized the safe processing regimes of both the alloys. Instability during hot deformation was observed to be driven mainly by shear band formation and/or intercrystalline cracking for the investigated Al alloys.
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How to cite this article:

Sanjib Banerjee, Nastagis Niyaz Ahmed, Pranjal Bhuyan and Subhashish Baruah, 2012. Computational Modeling of High Temperature Processing Maps for Microalloyed Al-Cu-Mg Alloys Using Artificial Neural Networks. Journal of Applied Sciences, 12: 902-910.

DOI: 10.3923/jas.2012.902.910

URL: https://scialert.net/abstract/?doi=jas.2012.902.910

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