Close-Range Machine Vision for Strain Analysis

Abstract

A substantial fraction of the automotive assembly comprises formed sheet metal parts. To reduce vehicle weight and improve fuel economy, total sheet metal mass should be minimized without compromising the structural integrity of the vehicle. Excessive deformation contributes to tearing or buckling of the metal, and therefore a forming limit is investigated experimentally to determine the extent to which each particular material can be safely strained. To assess sheet metal formability, this thesis proposes a novel framework for sheet metal surface strain measurement using a scalable dot-grid pattern. Aluminum sheet metal samples are marked with a regular grid of dot-features and imaged with a close-range monocular vision system. After forming, the sheet metal samples are imaged once again to examine the deformation of the surface pattern, and thereby resolve the material strain. Grid-features are localized with sub-pixel accuracy, and then topologically mapped using a novel algorithm for deformation-invariant grid registration. Experimental results collected from a laboratory setup demonstrate consistent robustness under practical imaging conditions. Accuracy, repeatability, and timing statistics are reported for several state-of-the-art feature detectors.