Detection of Defects in Groove Textures of Honed Surfaces

The automatic assessment of the texture of honed surfaces of cylinder bores based on microscopic grey level images is a demanding task. Although for some of the problems arising in this context, solutions are already given in literature [6,7,9], there remains a lot of work to be done due to the complexity of honing textures and the high quality demands made by combustion engine manufacturers. This paper deals with the special task of automatically detecting undesired defects like: folded metal, groove interrupts, smudgy groove edges etc. in honing textures. Therefore, two different image processing algorithms are presented. The first one searches for defects locally, whereas the second algorithm aims at detecting defective grooves in their entirety by exploiting the fact that, if a groove is affected, then usually several defects occur within the very same groove. The signal theory necessary to understand the algorithms is shortly presented. It is demonstrated that both approaches deliver reliable detection results for real honing textures.