Improved surface roughness as a result of free-form surface machining using self-organized neural network

This paper is concerned with the free-form surface reorganization and assessment of a free-form model complexity, grouping particular surface geometrical properties within patch boundaries using self-organized Kohonen neural network (SOKN). Coordinate values of point cloud distributed at a particular surface were used as a surface properties descriptor, which was fed into SOKN where representative neurons for curvature, slope and spatial surface properties were established. On the basis of this approach, the surface patch boundaries were reorganized in such a manner that the finished machining strategies gave the best possible surface roughness results. The patch boundaries were constructed in accordance with the Gaussian and mean curvature, in order to achieve a smooth transition between patches, and in this way, preserve or even improve the desired curve and surface continuities (C2 and G2). It is shown that by reorganization of the boundaries in respect of curvature, slope and spatial point distribution, the surface quality of the finished free-form surface is improved. This approach was experimentally verified on 22 free-form models which were reorganized by SOKN and machined with finish milling tool-path strategies. The results show rather good improvement of the mean surface roughness profile Ra for reorganized surfaces, when compared with unorganized surfaces.