## 4 Conclusions  
Flatbed scanning has proven to be an adequate and reliable digitising method, suitable for AM contour characterisation, in those situations where contrast variability between the contour and the background is low. In such situations, the quality of contour characterisation is highly dependent on image processing parameters. An adequate combination of filtering and edge-detection parameters has a significant influence upon observed results. Edge-detection method (\(M\)), threshold (\(T\)), and filter size (\(S_{f}\)) must be carefully selected to achieve the best results. It was also observed that it is not possible to simultaneously obtain the best achievable results of the coverage factor (\(C_{f}\)) the mean radial difference (\(\overline{D}\)), and the standard deviation of the radial difference (\(\sigma_{D}\)) using a single image processing configuration. Accordingly, a compromise between coverage and accuracy must be observed. Two alternative approaches have been proposed: coverage priority and accuracy priority. Both approaches could be adjusted to match specific requirements in terms of coverage or accuracy, within the limits observed in this research. It has been also proven that the contour detection is highly dependent on the contrast uniformity between the target contour and the background. A uniform contrast is related to the lower variability of local gradients, enabling the accurate identification of the contour points using a unique \(T\) value. Although providing a strict recommendation would maybe be too ambitious, results from S01 and S02 indicate that using the Canny method with high \(S_{f}\) values (\(S_{f}\)\(>\) 31) and fixing a \(T\) = 40 threshold would provide almost full contour coverage (\(C_{f}\)\(>\) 99%), while keeping low values for the other indicators (\(\overline{D}\)\(<\) 7.5 \(\upmu\)m; \(\sigma_{D}\)\(<\) 10 \(\upmu\)m). These combinations could be used as a reference when processing uniform and highly contrasted contours. On the other hand, when contrast shows an anisotropic distribution along the contour, the compromise between coverage and accuracy becomes unbalanced, and contour characterisation turns unfeasible. Non-uniform brightness and shadow patterns, caused by a reduction in layer-to-background distance, severely influence contour detection. In these situations, because the algorithms use a unique \(T\) for processing the whole image, low \(T\) values will provide good coverage and poor accuracy, whereas increasing \(T\) will progressively decrease coverage and slightly increase accuracy. Although Canny and ApproxCanny methods have shown higher robustness on contrast issues than first derivative algorithms or ZeroCross, the described procedure would not be adequate for highly anisotropic scans. A robust FBS-based OMM verification procedure of AM layers should minimise the influence of contrast anisotropy. We propose that future developments adopt a uniform contrast approach or an adaptive edge-detection approach. A uniform contrast approach would demand a combination of illumination arrangement and image processing to process contour segments with similar contrast characteristics. Adaptive edge-detection would require a new edge-detection procedure that considers local contrast characteristics and influence factors, such as the distance to the background as a function of the geometry of previous layers, the contour orientation, or the optical characteristics of material. An additional conclusion is that local abnormalities should demand a specific treatment during contour extraction and analysis because they have a significant influence upon the overall results. These anomalies are not derived from a lack of mechanical accuracy or deviations in contour path tracing, but from specific errors or deficiencies on the material extrusion dynamics. Accordingly, when an excessive accumulation of material (e.g., seams) or out-of-contour material (e.g., oozing phenomenon) appear, a robust procedure must detect them and apply a specific treatment.