Abstract
Flatbed scanners (FBSs) provide non-contact scanning capabilities that could be used for the on-machine verification of layer contours in additive manufacturing (AM) processes. Layer-wise contour deviation assessment could be critical for dimensional and geometrical quality improvement of AM parts, because it would allow for close-loop error compensation strategies. Nevertheless, contour characterisation feasibility faces many challenges, such as image distortion compensation or edge detection quality. The present work evaluates the influence of image processing and layer-to-background contrast characteristics upon contour reconstruction quality, under a metrological perspective. Considered factors include noise filtering, edge detection algorithms, and threshold levels, whereas the distance between the target layer and the background is used to generate different contrast scenarios. Completeness of contour reconstruction is evaluated by means of a coverage factor, whereas its accuracy is determined by comparison with a reference contour digitised in a coordinate measuring machine. Results show that a reliable contour characterisation can be achieved by means of a precise adjustment of image processing parameters under low layer-to-background contrast variability. Conversely, under anisotropic contrast conditions, the quality of contour reconstruction severely drops, and the compromise between coverage and accuracy becomes unbalanced. These findings indicate that FBS-based characterisation of AM layers will demand developing strategies that minimise the influence of anisotropy in layer-to-background contrast.
Highlights
Additive manufacturing (AM) processes have reached a high degree of maturity during the last decade
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Overall results were clearly worse than those obtained for S01. This result was common to all the edge-detection methods tested, indicating that the reduction in contrast had a direct impact on the quality of contour characterisation
Summary
Additive manufacturing (AM) processes have reached a high degree of maturity during the last decade. Location refers to the fact that the digital image of an edge reflects a smooth transition between intensity levels, which makes it difficult to fix with high accuracy which point represents the exact location of such transition This factor shall be affected by the image processing sequence, especially by the method or algorithm used to distinguish between contour and its neighbourhood (edge detector). Of AM layer contours should include geometrical Sinufmormmaartiisoinngth, aatreenliaabblleescthhaerdacetsecrriispattiioonnaonfdAmMealsauyreermceonnttoouf rdsisschroeupladnciniecslubdetewgeeeonmacetutraicl a(ml iannfourfamcatutiroend)thcoant teonuarbslaensdththeediresthcreioprteitoincaal nddefimnietaiosnu.reDmigeintatloimf dagisecsreopf aenacchielsaybeertpwroevenidaecdtubayla(nmFaBnSufsahcatullrbeed)pcroocnetossuerds caonndsitdheeririntghethoerentiociasledfielftienriitniogn, t.hDeigeditgael -idmeategcetsioonf emaecthhloadyearnpdrtohveidcreidtebriyonanuFseBdS tsohaplrlobmeoptreoecdesgseecdacnodnidsiadteesritnogctohnetonuorispeofiinlttesr.inAgd,dthiteioendaglley-, dtheetelcatyioenr-tmo-ebtahcokdgraonudndthceocnrtirtaesrtiocnhaursaecdtertoistpicrsomcooutledeadlsgoe icnaflnudeindcaetetshetorecloianbtoiluitry poof icnotns.tour reconstruction Both image processing and contrast pattern are expected to influence the accuracy of feature measurement and could be key to take the OMM of AM layer contours to an industrial level.
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