Thesis-1998-Khalaf.pdf (15.3 MB)
Download fileNeuro-fuzzy control modelling for gas metal arc welding process
thesis
posted on 2010-10-26, 10:39 authored by Gholam H. KhalafWeld quality features are difficult or impossible to directly measure and control
during welding, therefore indirect methods are necessary. Penetration is the most
important geometric feature since in most applications it is the most significant factor
affecting joint strength. Observation of penetration is only possible from the back face
of the full penetration weld. In all other cases, since direct measurement of depth of
penetration is not possible, real time control of penetration in the Gas Metal Arc
Welding (GMAW) process by sensing conditions at the top surface of the joint is
necessary. This continues to be a major area of interest for automation of the process.
The objective of this research has been to develop an on-line intelligent process
control model for GMAW, which can monitor and control the welding process. The
model uses measurement of the temperature at a point on the surface of the workpiece
to predict the depth of penetration being achieved, and to provide feedback for
corrective adjustment of welding variables. Neural Network and Fuzzy Logic
technologies have been used to achieve a reliable Neuro-Fuzzy control model for
GMAW of a typical closed butt joint having 60° Vee edge preparation.
The neural network model predicts the surface temperature expected for a set of fixed
and adjustable welding variables when a prescribed level of penetration is achieved.
This predicted temperature is compared with the actual surface temperature occurring
during welding, as measured by an infrared sensor. If there is a difference between the
measured temperature and the temperature predicted by the neural network, a fuzzy
logic model will recommend changes to the adjustable welding variables necessary to
achieve the desired weld penetration.
Large scale experiments to obtain data for modelling and for model validation, and
various other modelling studies are described. The results are used to establish the
relationships between the output surface temperature measurement, welding variables
and the corresponding achieved weld quality criteria. The effectiveness of the modelling methodology in dealing with fixed or variable root gap has also been
tested.
The result shows that the Neuro-fuzzy models are capable of providing control of
penetration to an acceptable degree of accuracy, and a potential control response time,
using modestly powerful computing hardware, of the order of one hundred
milliseconds. This is more than adequate for real time control of GMAW. The
application potential for control using these models is significant since, unlike many
other top surface monitoring methods, it does not require sensing of the highly
transient weld pool shape or surface.
History
School
- Mechanical, Electrical and Manufacturing Engineering
Publisher
© Gholam Hossein KhalafPublication date
1998Notes
A Doctoral Thesis. Submitted in partial fulfillment of the requirements for the award of Doctor of Philosophy of Loughborough University.EThOS Persistent ID
uk.bl.ethos.263585Language
- en