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Material Flow and Defect Formation D...

Ajri, Abhishek.

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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Material Flow and Defect Formation During Friction Stir Welding Processes via Predictive Numerical Modeling and Experiments./
Author:	Ajri, Abhishek.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	108 p.
Notes:	Source: Masters Abstracts International, Volume: 56-06.
Contained By:	Masters Abstracts International56-06(E).
Subject:	Mechanical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10272652
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. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 108 p.

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

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

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.

ISBN: 9780355150322Subjects--Topical Terms:

649730
Mechanical engineering.

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.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 108 p.
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.
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.
590 $a School code: 0183.
650 4 $a Mechanical engineering. $3 649730
650 4 $a Materials science. $3 543314
650 4 $a Low temperature physics. $3 3173917
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710 2 $a Purdue University. $b Mechanical Engineering. $3 1019124
773 0 $t Masters Abstracts International $g 56-06(E).
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792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10272652