This paper presents the use of a projector-based spatial augmented reality system in an industrial quality assurance setting to highlight spot-weld locations on vehicle panels for manual welding operators. The aim of this work is to improve the precision and accuracy of manual spot-weld placements with the aid of visual cues as a proactive step by the automotive manufacturer to enhance product quality. The prototype system was deployed at General Motors (GM) Holden plant in Elizabeth, Australia on the production line building Holden Cruze vehicles. Production trials were conducted and techniques developed to analyse and validate the precision and accuracy of spot-welds both with and without the visual cues. A reduction of 52 % of the standard deviation of manual spot-weld placement was observed when using augmented reality visual cues. The average standard deviation with-AR assistance (19 panels and 114 spot-welds) was calculated at 1.94 mm compared to without-AR (45 panels and 270 spot-welds) at 4.08 mm. All welds were within the required specification and panels evaluated in this study were used as the final product made available to consumers. The visual cues enabled operators to spot-weld at a higher degree of precision and accuracy.
This paper proposes a new approach, coupling physical models and image estimation techniques, for modelling the movement of fluids. The fluid flow is characterized by turbulent movement and dynamically changing patterns which poses challenges to existing optical flow estimation methods. The proposed methodology, which relies on Navier-Stokes equations, is used for processing fluid optical flow by using a succession of stages such as advection, diffusion and mass conservation. A robust diffusion step jointly considering the local data geometry and its statistics is embedded in the proposed framework. The diffusion kernel is Gaussian with the covariance matrix defined by the local second derivatives. Such an anisotropic kernel is able to implicitly detect changes in the vector field orientation and to diffuse accordingly. A new approach is developed for detecting fluid flow structures such as vortices. The proposed methodology is applied on artificially generated vector fields as well as on various image sequences.
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