Multi-feature detection for quality assessment in laser beam welding: Experimental results

Abstract

Laser beam welding (LBW) has been largely used in manufacturing processes ranging from automobile production to precision mechanics. The complexity of LBW requires the development of strategies for the real-time control of the process. Most of the available feedback systems lack of temporal and/or spatial resolution and, therefore, they hardly allow observing more than one characteristic of the process. In the last years, we proposed some high-speed visual algorithms for image feature extraction from process images. The detection of the full penetration hole (FPH) allowed controlling the laser power at rates of up to 14 kHz. Another strategy enables observing the occurrence of spatters at monitoring rates of 15 kHz. The achievement of these results was made possible by the adoption of a visual system including a focal plane processor programmable by typical Cellular Neural Network (CNN) operations. This paper is focused on a new visual algorithm for the simultaneous detection of FPH and spatters, which led to real-time control rates of about 8 kHz. Besides the algorithm description, some interesting experimental results will be presented.