Layer contour characterization in additive manufacturing through image binarization

On-Machine Measurement adoption will be key to dimensional and geometrical improvement of additively manufactured parts. One possible approach based on OMM aims at using digital images of manufactured layers to characterize actual contour deviations with respect to their theoretical profile. This strategy would also allow for in-process corrective actions. This work describes a layer-contour characterization procedure based on binarization of digital images acquired with a flat-bed scanner. This procedure has been tested off-line to evaluate the influence of two of the parameters for image treatment, the median filter size (S f ) and the threshold value (T), on the dimensional/geometrical reliability of the contour characterization. Results showed that an appropriate selection of configuration parameters allowed to characterize the proposed test-target with excellent coverage and reasonable accuracy.

THE FLAT BED SCANER, A HELPFULL TOOL FOR MARINE ORGANISMS CATALOGUING

The Flat Bed Scaner, a helpfull tool for marine organisms cataloguing.A cheap and versatile methodology to get digital images of dry and wet preserved organisms, using a flat bed scanner, is presented. To obtain the images of wet specimens, a shallow pool was glued on top of the scanner glass. The pool is filled with water or alcohol and the organisms are immersed and their structures expanded. The best images were obtained with organisms between 1.5 and 15 cm in length. The depth of field was limited to 1.2 cm. These images are also useful for taxonomical research without the need of manipulating the organisms; they can be used too, to compare with better accuracy certain structures between different specimens.

An automatic method for detecting missing dots in rotogravure printing

We introduce a new approach to detecting missing dots in rotogravure printing, based on binary morphology and convolution filtering, that addresses the drawbacks of automated techniques using a fast Fourier transform algorithm. Our method was developed for the Heliotest, which is a widely used halftone print quality test for rotogravure printing. A strip of decreasing halftone intensity is printed and the missing dots are counted visually. The distance to the 20th missing dot provides the Heliotest print quality number. To automate the determination of the Heliotest number, the strip is scanned with a standard flat-bed scanner. Using image analysis, an image binarization separates the printed dots from the unprinted area. Then, a black and white convolution is performed to locate the specific shape corresponding to the missing dots. Each possible missing dot is analyzed by the algorithm to determine whether it is a missing dot or a dot that is misshapen, weak, or incomplete. We found that this automatic procedure provides an excellent match to visual assessment. On 90 out of 100 test samples used for validation, our method gives the same result as the visual assessment. The other samples have one or two false positives in the missing dots. This was significantly better than the techniques that were in use before. After validation, the technique was deployed at NewPage; it has been regularly used as a product quality test at the mill and at the NewPage Research Center since 2011.