LIBRARY ı6ıul 0) - Cranfield University

In-process welding control is a much more complicated problem, since highly
coupled and non-linear dynamics are usually present [ref. 175]. It generally involves
three principal aspects: sensing, modelling, and control. The first two aspects have
been discussed in the previous sections. The control issue entails defining control
objectives and selecting suitable input and output variables to achieve these objectives
[ref. 175].
Cook et al. [ref. 175] define the objectives of feedback control in fusion
welding as to continuously sense and control in the presence of disturbances: a) the
placement of the heat source relative to the joint; b) the geometry of the weld
reinforcement and fusion zone; c) the mechanical properties of the completed weld; d)
the microstructural evolution during solidification and cooling; e) the discontinuity
formation. Most published works concentrate on controlling the first four objectives
[refs. 175,176]. Apart from seam tracking, the geometrical characteristics of the weld
bead are the most controlled features, due to their dominant influence on the
mechanical properties of the joint as well as the availability of real-time optical
measurement methods (e. g. for bead width) and estimation models (e. g. for bead
penetration) [ref. 176].
The variables involved in gas metal arc welding can be classified into two main
groups [refs. 175,177,178]: a) indirect weld parameters (IWP) and b) direct weld
parameters (DWP). The indirect weld parameters are the inputs to the process and
the direct weld parameters are the outputs. The indirect weld parameters include
welding voltage, wire feed speed (or current), travel speed, torch-to-workpiece
distance, torch angles relative to the joint, wire diameter, wire composition and
shielding gas [refs. 168,175]. The direct weld parameters are geometry of the weld,
mechanical properties, microstructure, level of discontinuities, etc. [ref. 175]. It
should be noted that some of the indirect weld parameters, such as wire diameter and
composition, shielding gas and torch angle are not (or cannot be) varied on line.
These do influence the process behaviour, but are normally fixed before welding [ref.
179].
Two main types of models are normally used for control purposes: a)
theoretical models, b) empirical models (see section 2.6.4). In the gas metal arc
welding process, the complexity of the physical phenomena involved makes them
difficult to model accurately over the entire operating range of the process. Normally,
the theoretical models result in a set of non-linear differential equations which always
need a numerical solution. For control purposes these models are not applicable, since
they cannot be computed in real time. However, they may be useful in developing
models that can be applied in design and control of multivariable weld feedback
control systems [ref. 175]. Empirical models, on the other hand, provide simple
relationships that can be computed in real time for controlling the process, although,
they do not provide much insight on the process phenomena.
Most work on the control of gas metal arc welding use empirical models,
obtained by using some kind of process identification. This involves selecting the
input and output variables and determining a mathematical relationship that fits some
experimental data. This normally results in locally linearised models of the process
which can be used for control purposes.
Several different control strategies have been applied to controlling the
welding process. Schedule controllers, based on look-up tables, have often been used
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