Modeling and control of friction stir welding
type: CreativeWork
description: "Friction stir welding (FSW) is receiving increased attention as an efficient solid state joining process for [a] number of reasons, including its applicability to different materials and high joint efficiencies. This apparently simple technique is affected by a number of different factors, such as process parameters, tool design, material properties and boundary conditions. Therefore, a good understanding of issues related to modeling, control, and metallurgy will be necessary to exploit its maximum potential. The objectives of this study were twofold. First, a discrete neural network (NN) based adaptive controller was developed and implemented on a six-axis robotic FSW machine...Second, the effect of process parameters and tool design was studied utilizing various sensor measurements"--Abstract, page iv.
abstract: "Friction stir welding (FSW) is receiving increased attention as an efficient solid state joining process for [a] number of reasons, including its applicability to different materials and high joint efficiencies. This apparently simple technique is affected by a number of different factors, such as process parameters, tool design, material properties and boundary conditions. Therefore, a good understanding of issues related to modeling, control, and metallurgy will be necessary to exploit its maximum potential. The objectives of this study were twofold. First, a discrete neural network (NN) based adaptive controller was developed and implemented on a six-axis robotic FSW machine...Second, the effect of process parameters and tool design was studied utilizing various sensor measurements"--Abstract, page iv.
author: Kalya, Prabhanjana
keywords: Mechanical Engineering
date published: 2016-02-10T08:00:00Z
date modified: 2026-07-24T03:20:02Z
provider: Missouri University of Science and Technology
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Missouri--Rolla