An Implementation of Model-Based Visual Feedback for Robot Arc Welding of Thin Sheet Steel

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Abstract
Using conventional robots for metal/inert-gas (MIG) arc welding on thin (1- to 2-mm) sheet steel pressings is restricted by the difficulty of maintaining accurate fit-up and fixturing. Dimensional variations are introduced by wear of tools and fixtures, springback, and thermal distortion during welding. These variations cause errors in the position of the welding torch relative to the seam of as much as ±3.0 mm, preventing the formation of good welds. We describe an implemented system that forms and uses models for the automatic visually guided control of a MIG welding robot. The system uses its models to aid the detection of the following dimensional variations in sheet steel assemblies: gap width, standoff error, and lateral error. Lap, T, and butt joints are dealt with. By use of the model-based visual feedback techniques reported in this paper, errors in the position of the welding torch are corrected to within ±0.5 mm, which is a suitable tolerance for producing good welds in such assemblies. Furthermore, seam widths are detected to dynamically alter welding pa rameters. Artificial intelligence principles other than models are also used, including island parsing, back constraints, and pattern-directed inference.

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