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volume caused by the presence of a gap would be compensated by a change in wire-
feed-speed and the arc heat input, by adjusting the welding voltage. The high-current
high-speed rotating arc, in this case, produces a keyhole (see Figure 2.21) whose size
must be kept constant to ensure a consistent back bead shape. With the proposed
strategy, the dynamic equilibrium of the molten metal in the keyhole (see Figure 2.21)
was controlled, thus regulating the keyhole size The output from the high speed
rotating arc sensor was compared with a reference value (which corresponds to the
desired keyhole size) and an error signal was generated. The error signal was used in a
proportional control scheme to adjust the values of wire feed speed and welding
voltage. Three other control strategies were also tested [ref. 183]: a) controlling only
travel speed according to the gap variation; b) regulating the welding speed and
current according to joint geometry; c) keeping the weld heat input per unit length
and the wire feed speed constant, irrespective of joint geometry.
Also, knowledge based techniques such as fuzzy logic and neural networks are
being applied to weld modelling and control [refs. 152,184,185,186]. Fuzzy logic
based controllers use fuzzy sets to represent linguistic values of the input and output
variables of a physical system and describe their relationships by fuzzy if-then rules.
The idea of fuzzy control is to simulate a human expert who is able to control the
system by translation of his linguistic inference rules into a control function [ref. 187].
Artificial neural networks are highly parallel architectures consisting of simple
processing elements which communicate through weighted connections. They are able
to approximate functions or to solve certain tasks by learning from examples [ref.
187]. In the learning process, input and output data are provided, weighted values
assigned to the connections within the architecture, and the network (which adjusts
the weights by using several criteria) is run repeatedly until the output is accurate to
the required level of confidence. The resulting weights are then stored, forming the
memory of the network [ref. 188]. A full description of these techniques and their
application for modelling and control can be found in the references 187 and 189.
2.7 Analysis
This literature review has attempted to cover all aspects involved in robotic
welding including the welding process, the use of robots to carry out welding
operations and related production problems, off-line programming applied to welding,
process monitoring, sensing, modelling and adaptive control.
The review of the literature indicates that off-line programming as applied in
robotic welding cannot satisfy by itself the demands imposed by the welding process.
Some kind of adaptive control is obviously needed.
There is a tendency towards the use of CAD/CAM systems in welded
structures (ref. 85), from design to the shop floor, in which the robots should behave
like CNC machines. For this tendency to become widespread a need for adaptive
systems (i. e. monitoring and control systems) is imperative. Although monitoring
systems for welding are commonly available, very few are used integrated with off-
line programming.
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