Recently, developing Additive Manufacturing (AM) technologies have been increased, because its advantages toward the rapid manufacturing of physical model from the CAD system. In the AM area, the designer specifies the desired surface quality on the working drawing to be considered during the building operation. The produced surface depends on the building parameters. The aim of this work is to develop new empirical models for predicting the building orientation that satisfy required surface roughness based on FDM m/c. In this study, a new 3D CAD specimen was proposed to decrease the number of experiments, measuring errors and building cost. The specimen contains the surface orientation from 0 o to 90 o with step 10 o that was built three times at three different layer thickness (0.1, 0.3, and 0.4mm). The order of the model was determined by the test of all orientations accept at 30 o and 60 o that was used for model verification. Results show the three prediction models at certain three values of layer thicknesses. The prediction of building orientation has several benefits as follows; it is very useful information for the designer before exporting STL file, the AM users can choose the process parameters without extra trails, increase the opportunity of technology to shear in Rapid Manufacturing (RM), Rapid Tooling (RT), and in medical applications.

Breast cancer is becoming the leading form of cancer among women worldwide, indeed, there are no effective ways to prevent this disease at present, therefore, it's early screening and detection is the key to rise the success of treatment, hence, the reduce of the associated mortality rates. This work aims to improve the performance of the current computer-aided detection and diagnosis approaches (CADe/CADx) of breast cancer which involve the application of the computer technology in mammograms analysis and understanding; for this purpose, we deal with the power laws: Zipf and inverse Zipf. The originality of this research lays in the contribution of the power laws for mammograms analysis; it is the first attempt to use them in the field of mammograms masses segmentation and classification, indeed, these laws characterize the structural complexity of texture within mammograms and provide the curves of Zipf and inverse Zipf which carry significant information that could be used to mammograms masses detection and classification along a new set of textural features extracted from the curves of Zipf and inverse Zipf. According to our experiments conducted on a mammogram database used in the framework of a bilateral project between our university and the hospital CHU at Algeria, we can assert that our approach based Zipf's and inverse Zipf's laws is a powerful and efficient approach for automated mammograms masses detection and classification.

Nowadays, Solid Freeform Fabrication (SFF) takes a great attention by creating complex solid objects directly from geometric models without specific tooling information. SFF also referred to layered manufacturing (LM) which builds up 3D objects by successive 2D layer deposition after exporting it as an STL file format from its CAD environment. This paper aims at overcoming the approximation problem of 2D layer contour from sliced STL model. The author used a Lagrange interpolation method to increase the point's coordinates of the sliced layer contour in order to reconstruct an accurate and near net contour of the original sliced CAD model. This method reduced the error between the original sliced CAD model and the actual reconstructed layer contours to 0.006%.

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