Nowadays, the basic requirements of gear transmissions are not limited to resistance and reliability, but often include good efficiency and low vibration and noise emissions. This article investigates the role of tooth flank micro-geometry in fulfilling these needs. A non-linear finite element approach has been conceived and exploited to investigate in detail the influence\ud of the shape of profile modifications (PMs) on transmission error, root stress, and contact pressure.\ud In this approach, the contact between teeth flanks is handled by ABAQUS general purpose contact algorithm without introducing any simplification based on gear geometry peculiarities.\ud The boundary conditions are defined so that it is possible to automatically run a sequence of static analyses. The numerical results are first assessed by comparison with experimental measurements and then a comparison of contact and bending stresses of the same gear with long linear and long circular PMs is presented and discussed. The results of these comparisons show\ud that the optimal amount of PMs is not independent of PM shape; hence, the procedures used to design linear PMs cannot be directly applied to the design of non-linear PMs

For the observation of human joint cartilage, X-ray, computed tomography (CT) or magnetic resonance imaging (MRI) are the main diagnostic tools to evaluate pathologies or traumas. The current work introduces a set of novel measurements and 3D features based on MRI and CT data of the knee joint, used to reconstruct bone and cartilages and to assess cartilage condition from a new perspective. Forty-seven subjects presenting a degenerative disease, a traumatic injury or no symptoms or trauma were recruited in this study and scanned using CT and MRI. Using medical imaging software, the bone and cartilage of the knee joint were segmented and 3D reconstructed. Several features such as cartilage density, volume and surface were extracted. Moreover, an investigation was carried out on the distribution of cartilage thickness and curvature analysis to identify new markers of cartilage condition. All the extracted features were used with advanced statistics tools and machine learning to test the ability of our model to predict cartilage conditions. This work is a first step towards the development of a new gold standard of cartilage assessment based on 3D measurements.

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