Abstract: Detailed surface characteristics of the particles produced during fretting may well be significant in determining their biological effects. Apart from a broad size determination very few attempts have been made at devising means of describing profile textures of particles. An approach is presented for describing the nature of particle shape and surface texture, as detected on the particle profile. Careful processing and analysis of the digitised image enabled both the sizes of micro-projections and their relative numbers to be determined. Such processing of images of a large number of particles generated a considerable amount of data. An artificial neural network was used to categorise the data and to compare the nature of fretting particles generated by titanium, titanium-molybdenum and stainless steel. Although showing a tendency towards a spherical form, all three metals produced different results, with titanium showing the greatest diversity of textures and sizes, steel the least.

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