A comparative study of three vision systems for metal surface defect detection

Abstract

In this paper we present a comparative analysis of three vision systems to nondestructively predict defects on the surfaces of aluminum castings. A hyperspectral imaging system, a thermal imager, and a digital color camera have been used to inspect aluminum metal cast surfaces. Hyperspectral imaging provides both spectral and spatial information, as each material produces specific spectral signatures which are also affected by surface texture. Thermal imager detects infrared radiation whereby hotspots can be investigated to identify possible trapped inclusions close to the surface, or other superficial defects. Finally, digital color images show apparent surface defects that can also be viewed with the naked eye but can be automated for fast and efficient data analysis. The surface defect locations predicted using the three systems are then verified by breaking the casings using a tensile tester. Of the three nondestructive methods, the thermal imaging camera was found to produce the most accurate predictions for defect location that caused breakage.