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The BTA deep hole drilling process is applied in order to produce bores with a large length to diameter ration at comparably high diameters of the bores. During drilling, first, the bore is cut by the cutting edge and shortly after that, a guide pad slides over the newly produced bore wall, burnishing the surface. These two effects condition the bore wall and alter its surface integrity depending on the process parameters. In the past, extensive destructive and non-destructive tests were conducted to obtain information on the surface integrity of the machined parts. Furthermore, a large number of in- and off-process measurements were carried out in material and cost-intensive experiments to determine values such as residual stresses or white etching layers. The presented work shows different finite element models of the BTA deep hole drilling process using continuous remeshing and, in order to reduce the calculation times, the Coupled Eulerian-Lagrangian (CEL) method as well. The results simulated based on this models comprise temperatures, process forces and surface properties that are compared to measured values from pervious works for validation. The various approaches are evaluated to determine whether they predict the bore hole surface integrity and thus support future investigations. [ABSTRACT FROM AUTHOR]

The reliable detection and precise assessment of white etching layers (WEL) are key challenges in the investigation of a component's surface integrity. This paper proposes an innovative methodology for evaluating the extent of WEL in quenched and tempered steels, machined by Boring and Trepanning Association (BTA) deep hole drilling. Micrographs obtained by light microscopy were partitioned into classes by three methods, separating the WEL from the base material and the embedding resin. Traditional manual segmentation was performed as a benchmark for automatic segmentation methods. A gray level thresholding-based method served for the segmentation of micrographs partitioned into subsets. In addition to conventional manual and thresholding-based segmentation, a machine learning-based approach for image segmentation was applied. The segmented images were further analyzed by a newly developed set of algorithms, implemented to obtain detailed information on the WEL, e.g. their average thickness as well as the area covered by WEL in the micrographs. Results indicate that both, gray level thresholding, as well as machine learning-based image segmentation, show potential for the automated diagnosis and assessment of WEL. They both yield quantitatively similar, but less biased results compared to manual segmentation. [ABSTRACT FROM AUTHOR]