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Neural network models of peak temperature, torque, traverse force, bending stress and maximum shear stress during friction stir welding

DSpace at IIT Bombay

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Title Neural network models of peak temperature, torque, traverse force, bending stress and maximum shear stress during friction stir welding
 
Creator MANVATKAR, VD
ARORA, A
DE, A
DEBROY, T
 
Subject Friction stir welding
Neural network model
Peak temperature
Traverse force
Torque
Stresses
MATERIALS SCIENCE
FLOW CALCULATIONS
HEAT-TRANSFER
PLASTIC-FLOW
ALLOYS
STRENGTH
GEOMETRY
JOINTS
 
Description Tool and workpiece temperatures, torque, traverse force and stresses on the tools are affected by friction stir welding (FSW) variables such as plate thickness, welding speed, tool rotational speed, shoulder and pin diameters, pin length and tool material. Because of the large number of these welding variables, their effects cannot be realistically mapped by experiments. Here, we develop, test and make available a set of five neural networks to calculate the peak temperature, torque, traverse force and bending and equivalent stresses on the tool pin for the FSW of an aluminium alloy. The neural networks are trained and tested with the results from a well tested, comprehensive, three-dimensional heat and material flow model. The predictions of peak temperature and torque are also compared with appropriate experimental data for various values of shoulder radius and tool revolutions per minute. The models can be used even beyond the range of training with predictable levels of uncertainty.
 
Publisher MANEY PUBLISHING
 
Date 2014-10-15T16:38:11Z
2014-10-15T16:38:11Z
2012
 
Type Article
 
Identifier SCIENCE AND TECHNOLOGY OF WELDING AND JOINING, 17(6)460-466
http://dx.doi.org/10.1179/1362171812Y.0000000035
http://dspace.library.iitb.ac.in/jspui/handle/100/15229
 
Language en