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Machining-induced residual stresses (MIRS) are a main driver for distortion of thin-walled monolithic aluminum workpieces. Before one can develop compensation techniques to minimize distortion, the effect of machining on the MIRS has to be fully understood. This means that not only an investigation of the effect of different process parameters on the MIRS is important. In addition, the repeatability of the MIRS resulting from the same machining condition has to be considered. In past research, statistical confidence of MIRS of machined samples was not focused on. In this paper, the repeatability of the MIRS for different machining modes, consisting of a variation in feed per tooth and cutting speed, is investigated. Multiple hole-drilling measurements within one sample and on different samples, machined with the same parameter set, were part of the investigations. Besides, the effect of two different clamping strategies on the MIRS was investigated. The results show that an overall repeatability for MIRS is given for stable machining (between 16 and 34% repeatability standard deviation of maximum normal MIRS), whereas instable machining, detected by vibrations in the force signal, has worse repeatability (54%) independent of the used clamping strategy. Further experiments, where a 1-mm-thick wafer was removed at the milled surface, show the connection between MIRS and their distortion. A numerical stress analysis reveals that the measured stress data is consistent with machining-induced distortion across and within different machining modes. It was found that more and/or deeper MIRS cause more distortion.
Today, polygonal models occur everywhere in graphical applications, since they are easy
to render and to compute and a very huge set of tools are existing for generation and
manipulation of polygonal data. But modern scanning devices that allow a high quality
and large scale acquisition of complex real world models often deliver a large set of
points as resulting data structure of the scanned surface. A direct triangulation of those
point clouds does not always result in good models. They often contain problems like
holes, self-intersections and non manifold structures. Also one often looses important
surface structures like sharp corners and edges during a usual surface reconstruction.
So it is suitable to stay a little longer in the point based world to analyze the point cloud
data with respect to such features and apply a surface reconstruction method afterwards
that is known to construct continuous and smooth surfaces and extend it to reconstruct
sharp features.