Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/16932
Title: Studies on hot deformation of as cast aluminum alloys
Researcher: Deivasigamani R
Guide(s): Arunachalam V P
Keywords: Aluminum alloys
Artificial neural network
Mechanical Engineering
Upload Date: 6-Mar-2014
University: Anna University
Completed Date: 01/10/2013
Abstract: Aluminum alloys are widely found in various products regularly used in our daily lives, from aluminum foil for food packaging and easy open aluminum cans for beverages to the structural members of the aircraft. The wide use of aluminum alloys is possible because of their desirable combination of high specific strength, low density, good corrosion resistance, machinability and cost and also the ease with which they may be produced in a great variety of forms and shapes with wide variety of surface finishes The challenge posed to process designer in metal forming is to produce net-shape or near- net shapes economically with desired mechanical properties and quality. This necessitates the designer to have an understanding the behavior of metals and alloys at hot deformation conditions. The finite element methods and deformation processing maps are the important techniques available to characterize the material behavior under different processing conditions in metal various forming operations. To use these techniques effectively an accurate constitutive model is needed for better process control and parameter optimization in the hot working operation. Since the constitutive relation among flow stress, strain, strain rate and temperature is complex and nonlinear the results obtained from the parametric approach to constitutive modeling are not accurate. Because of inherent properties of artificial neural network (ANN) an alternative to the parametric modeling is found to be ANN. Flow stress data for hot working of as cast aluminum alloys are very much limited. Hence flow stress data of cast 4043 (Al-5Si), 5182 (Al-4.5 Mg) and RR58 aluminum alloys were generated by conducting compression tests in the range of temperatures from 500 K to 800 K and strain rates from 0.02 s-1 to 8 s-1. The present study has tried to investigate the feasibility of utilizing the neural network to extract the complex relationship involved in hot deformation process modeling.
Pagination: xx,182p.
URI: http://hdl.handle.net/10603/16932
Appears in Departments:Faculty of Mechanical Engineering

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01_title.pdfAttached File26.17 kBAdobe PDFView/Open
02_certificates.pdf926.14 kBAdobe PDFView/Open
03_abstracts.pdf12.85 kBAdobe PDFView/Open
04_acknowledgement.pdf6.17 kBAdobe PDFView/Open
05_contents.pdf33.42 kBAdobe PDFView/Open
06_chapter 1.pdf16.24 kBAdobe PDFView/Open
07_chapter 2.pdf310.07 kBAdobe PDFView/Open
08_chapter 3.pdf786.77 kBAdobe PDFView/Open
09_chapter 4.pdf861.76 kBAdobe PDFView/Open
10_chapter 5.pdf3.2 MBAdobe PDFView/Open
11_chapter 6.pdf8.46 kBAdobe PDFView/Open
12_appendix.pdf8.87 kBAdobe PDFView/Open
13_references.pdf28.1 kBAdobe PDFView/Open
14_publications.pdf5.4 kBAdobe PDFView/Open
15_vitae.pdf6.15 kBAdobe PDFView/Open


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