A New Research Tool for Intrinsic Hardware Evolution
ICES '98 Proceedings of the Second International Conference on Evolvable Systems: From Biology to Hardware
Genetic programming for image analysis
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
| Hi-index | 0.00 |
Surface roughness is one of the essential quality control processes that the carried out to ensure that manufactured parts conform to specified standards and influences the functional characteristics of the work-piece such as fatigue, fracture resistance and surface friction. The most widely used surface finish parameter in industry is the average surface roughness (Ra) and is conventionally measured by using a stylus type instrument, which has a disadvantage that it requires direct physical contact and may not represent the real characteristics of the surface. Alternately, surface roughness monitoring techniques using non – contact methods based on computer vision technology [1] are becoming popular. In this paper, an evolvable hardware (EHW) configuration using Xilinx Virtex xvc1000 architecture to perform adaptive image processing i.e. noise removal and improve the accuracy of measurement of surface roughness is presented.