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contributor authorShen, Ninggang
contributor authorDing, Hongtao
contributor authorPu, Zhengwen
contributor authorJawahir, I. S.
contributor authorJia, Tao
date accessioned2017-11-25T07:17:45Z
date available2017-11-25T07:17:45Z
date copyright2017/31/1
date issued2017
identifier issn1087-1357
identifier othermanu_139_06_061012.pdf
identifier urihttp://138.201.223.254:8080/yetl1/handle/yetl/4234767
description abstractThe use of magnesium (Mg) alloy has been continuously on the rise with numerous expanded application in transportation/aerospace industries due to their lightweight and other areas, such as biodegradable medical implants. It was shown recently that machining can be used to improve the functional performance of Mg-based products/components, such as corrosion resistance, through engineered surface integrity. In this paper, the behavior of AZ31B Mg alloy in cryogenic machining was discussed firstly. The surface integrity can be significantly improved by introducing the ultrafine grained (UFG) layer due to the severe plastic deformation (SPD) effect during cryogenic machining. The mechanisms of microstructure evolution and plastic deformation were analyzed based on the experimental findings in literature. A physics-based constitutive model involving material plasticity and grain refinement is developed based on both slip and twinning mechanisms and successfully implemented in a finite-element (FE) analysis with multiple cutting passes to predict the microstructure evolution by nanocrystalline grain refinement and other improvement of the surface integrity in the cryogenic machining of AZ31B Mg alloy. With a more quantitative assessment, the FE model results are further discussed for grain refinement, changes in microhardness, residual stresses, and slip/twinning mechanism with the apparent SPD taking place due to rapid cryogenic cooling.
publisherThe American Society of Mechanical Engineers (ASME)
titleEnhanced Surface Integrity From Cryogenic Machining of AZ31B Mg Alloy: A Physics-Based Analysis With Microstructure Prediction
typeJournal Paper
journal volume139
journal issue6
journal titleJournal of Manufacturing Science and Engineering
identifier doi10.1115/1.4034279
journal fristpage61012
journal lastpage061012-13
treeJournal of Manufacturing Science and Engineering:;2017:;volume( 139 ):;issue: 006
contenttypeFulltext


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