Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/24526
Title: Study on friction welding wear and hot deformation behavior of AZ31B magnesium alloy and nano composite
Researcher: Srinivasan, M
Guide(s): Loganathan, C
Keywords: AZ31B magnesium alloy
Friction welding
Magnesium alloy and nano composite
Material behavior
Mechanical engineering
Microstructural behavior
Upload Date: 3-Sep-2014
University: Anna University
Completed Date: 01/09/2013
Abstract: Reducing the weight is one of the key factors in the manufacturing of structural application materials to reduce the green house gas emissions Recently magnesium has its application in consumer products automotive and aerospace industries The density of magnesium alloys ranges from 1 74 to 1 83 g cc A wide variety of alloy elements are used to suit different range of applications The most important alloying ingredients used in magnesium alloys are aluminum zinc manganese silicon zirconium rare earth metals and strontium Researchers have also experimented by adding calcium and copper to enhance the oxidation resistance and strength Taking the above into consideration new AZ31B magnesium alloys were developed by reinforcing calcium and nano alumina This research work was carried out to elucidate the material behavior during secondary processing friction welding and hot deformation behavior and to study the wear behavior for the magnesium alloy and nano composite Friction welding was carried out first to predict the tensile behavior and the microstructural evolution of the magnesium nano composite of weldments The preliminary experiments were conducted on the composite and the tensile strength of the welded samples yielded only around 75 Inorder to get the optimal strength the experimental design was conducted using central composite design using design expert software For the obtained parameters the welds were performed on the pure AZ31B magnesium alloy Using response surface methodology the optimal tensile strength was evolved and the same has been validated with artificial neural network
Pagination: xxi, 185p.
URI: http://hdl.handle.net/10603/24526
Appears in Departments:Faculty of Mechanical Engineering

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03_abstract.pdf8.87 kBAdobe PDFView/Open
04_acknowledgement.pdf6.13 kBAdobe PDFView/Open
05_contents.pdf30.88 kBAdobe PDFView/Open
06_chapter1.pdf298.33 kBAdobe PDFView/Open
07_chapter2.pdf355.01 kBAdobe PDFView/Open
08_chapter3.pdf1.64 MBAdobe PDFView/Open
09_chapter4.pdf145.17 kBAdobe PDFView/Open
10_chapter5.pdf10.41 MBAdobe PDFView/Open
11_chapter6.pdf11.62 kBAdobe PDFView/Open
12_references.pdf39.9 kBAdobe PDFView/Open
13_publications.pdf5.86 kBAdobe PDFView/Open
14_vitae.pdf5.78 kBAdobe PDFView/Open
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