In the welding industry, we see an increasing need for automated inspection, both in partnership with automated
seam tracking and as a completely separate function.
The aim of this project is to develop algorithms for computer vision capable of analysing 3D scans of robotic welds, by extracting underlying
geometry, identifying a range of standard defects, and classifying the welds as acceptable or not according to geometrical
Three key stages of the project can be identified:
- Performing automatic analysis of 3D data requires an in depth understanding and application of the underlying
mathematics involved. It will be necessary to use this knowledge to define the basis for the operation of the algorithms.
- The second step will be to use the mathematical development in the form of a set of algorithms for matching the 3D
datasets of actual parts to be inspected to either theoretical models of good and bad welds or stored, processed 3D models
of good and bad welds, and thereby making a determination of the overall quality of the weld in question and identifying any
- To support the first two items above, it may be necessary to have a database which extracts key geometric information
about good and bad shapes and makes that available to the inspection algorithms themselves. The database will also store
basic 3D representations of complete parts.