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Material Flow and Defect Formation During Friction Stir Welding Processes via Predictive Numerical Modeling and Experiments.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Material Flow and Defect Formation During Friction Stir Welding Processes via Predictive Numerical Modeling and Experiments./
Author:	Ajri, Abhishek.
Description:	1 online resource (108 pages)
Notes:	Source: Masters Abstracts International, Volume: 56-06.
Contained By:	Masters Abstracts International56-06(E).
Subject:	Mechanical engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9780355150322

Material Flow and Defect Formation During Friction Stir Welding Processes via Predictive Numerical Modeling and Experiments.
Ajri, Abhishek.

Material Flow and Defect Formation During Friction Stir Welding Processes via Predictive Numerical Modeling and Experiments. - 1 online resource (108 pages)

Source: Masters Abstracts International, Volume: 56-06.

Thesis (M.S.M.E.)--Purdue University, 2017.

Includes bibliographical references

Friction stir welding (FSW) has been at the forefront for welding aluminum alloys for both aerospace and automotive applications. Achieving good weld strength devoid of defects is very important for industrial applications of this process. The process parameters play a pivotal role in achieving a good weld joint. Understanding the differences in welding defects and preventing them cannot be explained only by experimentation. Thus, there is a need for developing a numerical model to explain the physics of the FSW process and explain the effect of process parameters on the defect formation mechanism.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355150322Subjects--Topical Terms:

557493
Mechanical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Material Flow and Defect Formation During Friction Stir Welding Processes via Predictive Numerical Modeling and Experiments.
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245 1 0 $a Material Flow and Defect Formation During Friction Stir Welding Processes via Predictive Numerical Modeling and Experiments.
264 0 $c 2017
300 $a 1 online resource (108 pages)
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500 $a Source: Masters Abstracts International, Volume: 56-06.
500 $a Adviser: Yung C. Shin.
502 $a Thesis (M.S.M.E.)--Purdue University, 2017.
504 $a Includes bibliographical references
520 $a Friction stir welding (FSW) has been at the forefront for welding aluminum alloys for both aerospace and automotive applications. Achieving good weld strength devoid of defects is very important for industrial applications of this process. The process parameters play a pivotal role in achieving a good weld joint. Understanding the differences in welding defects and preventing them cannot be explained only by experimentation. Thus, there is a need for developing a numerical model to explain the physics of the FSW process and explain the effect of process parameters on the defect formation mechanism.
520 $a The finite element (FE) model developed in this thesis helps not only in understanding the dynamics of the friction stir welding process; but also gives a bird's eye view during the formation of the weld under different processing conditions. Based on the observations made using the FE model, a methodology has been proposed to counter the defects formed during FSW; by accurately adjusting the process parameters.
520 $a The finite element model is first validated with the experimental data and then is used to understand the formation of defects like cavities, groove-like defects, tunnel defects and excess flash formation during the FSW process. It provides a relationship between temperature distribution, stir zone, material flow and velocity for different process parameters.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Mechanical engineering. $3 557493
650 4 $a Materials science. $3 557839
650 4 $a Low temperature physics. $3 1193206
655 7 $a Electronic books. $2 local $3 554714
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710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Purdue University. $b Mechanical Engineering. $3 845672
773 0 $t Masters Abstracts International $g 56-06(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10272652 $z click for full text (PQDT)