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for selecting indirect weld parameters suitable to producing the required weld quality,
as a response to the bead geometry information provided by sensors [refs. 11,178,
179,180]. Although these controllers are reported to provide the parameter values
that most likely suit the observed seam geometry, they essentially work in open loop.
Closed-loop weld process control requires the observation of parameters that describe
the current state of the process [ref. 178].
Hunter et al. [ref. 168] used experimentally obtained steady state models to
control gas metal arc welding. The authors [ref. 168] developed the control models by
fitting to equation (2.37) the experimental data obtained from a factorial experiment,
in which travel speed, wire feed speed, welding voltage and contact-tip-to-workpiece
distance were used as the process inputs and the geometry of a flat position fillet
weld, as the output. The authors [ref. 168] combined the resulting models in a matrix
form in order to realise a multivariable controller for the gas metal arc welding
process.
D= ac + 1SSWFS&2V83SO84
+ yG
(2.37)
where D is a weld bead dimension (leg length, throat thickness, deposited metal
height and fused metal leg length), SR-
is the travel speed, WFS is the wire feed speed,
V is the welding voltage, SO is the contact tip-to-workpiece distance, G is the gap
size, and a, P, S; (i=1... 4) and y are constants.
Some authors are using identification techniques to obtain steady-state and
dynamic models which are used for developing controllers for welding process.
Generally the dynamic models are obtained by fitting first order [ref. 179], second
order [refs. 181,182] or higher order dynamics [ref. 181] to the open loop response
of the direct weld parameters to input steps in the indirect weld parameters. This
normally results in locally linearised models which implies either the use of robust
linear controllers, such as a robust servomechanism control framework as applied by
Huisson et al. [ref. 179], or adaptive control algorithms (e. g. a pseudogradient
adaptive algorithm for automatically tuning a proportional integral controller [ref.
181] or a multivariable one-step-ahead adaptive algorithm for adjusting model
parameters in different operating ranges [ref. 182]). Although good results have been
reported, problems were encountered in dealing with gap [ref. 179] and in
implementing a true multivariable control due to the strong coupling between the
direct weld parameters [ref. 182].
More recently, intelligent control techniques have been applied in an attempt
to overcome the complicated coupling between welding variables. This normally
involves a substantial amount of conditions, or heuristic logic, which is developed
based on previous process knowledge. One example of such control systems was
presented by Sugitani et al. [refs. 183], who used heuristic rules and process
knowledge to develop an inteligent control system that simultaneously control weld
bead height and back bead shape for V-groove butt joints with backing plate, in the
presence of varying gap size. The proposed method used the high-current high-speed
rotating arc welding process and was based on keeping the arc heat input per unit
length of weld bead constant irrespective of root gap. This was accomplished by
regulating only the wire feed speed and the welding voltage, such that the excess joint
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