Page 101 - Welding Robots Technology, System Issues, and Applications

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Sensors for Welding Robots

which deviate both sideways and in height. In Figure 3.8, a work-piece (T-pipe) is
shown as an example of a complex weld path that is difficult to generate a program
for and thus, a sensor with a capability to both track and generate the weld path is a
suitable technique.

3.3 Monitoring

The ability to monitor the weld quality automatically is important in order to
reduce production costs and to assure and improve weld quality. An automatic
detection system should be able to classify different weld defects such as porosity,
metal spatter, irregular bead shape, excessive root reinforcement, incomplete
penetration and burn-through.

Monitoring systems for weld parameters such as ADM III, Arc guard, Analysator
Hannover 10.1 and Weldcheck are commercially available [2],[6]. They all work in
a similar way: voltage, current and other process signals are measured, presented
and compared with preset nominal values. An alarm is triggered when any
difference from the preset values exceeds a given threshold.

Thus, an important feature of monitoring is that it is done during welding and using
data that exist during the welding process. To be able to make any judgment about
the quality, reference data must be available including models or algorithms that
describes and evaluate measured parameter.

An important task of any monitoring system which is used for quality assurance or
quality control purposes is to be able to present the data with respect to quality
measures as consistently as possible. This means that alarm thresholds defined
must be correlated with real weld defects or relate to specifications defined in the
WPS. An important aspect in this context is to understand that the welding process
displays a more unstable situation when the data frequency of the readings are
increased, and consequently, measurements of process parameters at lower
frequencies, providing they display mean values, will display a more stable
process.

The information within the WPS does not normally account for this but includes
nominal operating data for different controllable parameters. Thus, part of the
monitoring system for control purposes is to define alarm thresholds with respect
to the WPS to maintain the process within nominal parameter limits and at the
same time produce a weld at the defined quality and productivity levels.

The examples given here are limited to the detection of changes in the weld quality
both automatically and on-line in spray GMAW when using signal processing
methods. However, the method as such can in principle be applied to any welding
method providing that knowledge exists about the stability criteria of the process
and how to measure significant parameters related to the stability.