Modelling the Mechanical Behavior of Polymer-Based Nanocomposites

Molecular dynamics simulations were employed to analyze the mechanical properties of polymer-based nanocomposites with varying nanofiber network parameters. The study was focused on nanofiber aspect ratio, concentration and initial orientation. The reinforcing phase affects the behavior of the polymeric nanocomposite. Simulations have shown that the fiber concentration has a significant effect on the properties, with higher loadings resulting in higher stress levels and higher stiffness, matching the general behavior from experimental knowledge in this field. The results also indicate that, within the studied range, the observed effect of the aspect ratio and initial orientation is smaller than that of the concentration, and that these two parameters are interrelated.

Artificial neural networks for automatic modelling of the pectus excavatum corrective prosthesis

Pectus excavatum is the most common deformity of the thorax and usually comprises Computed Tomography (CT) examination for pre-operative diagnosis. Aiming at the elimination of the high amounts of CT radiation exposure, this work presents a new methodology for the replacement of CT by a laser scanner (radiation-free) in the treatment of pectus excavatum using personally modeled prosthesis. The complete elimination of CT involves the determination of ribs external outline, at the maximum sternum depression point for prosthesis placement, based on chest wall skin surface information, acquired by a laser scanner. The developed solution resorts to artificial neural networks trained with data vectors from 165 patients. Scaled Conjugate Gradient, Levenberg-Marquardt, Resilient Back propagation and One Step Secant gradient learning algorithms were used. The training procedure was performed using the soft tissue thicknesses, determined using image processing techniques that automatically segment the skin and rib cage. The developed solution was then used to determine the ribs outline in data from 20 patient scanners. Tests revealed that ribs position can be estimated with an average error of about 6.82±5.7 mm for the left and right side of the patient. Such an error range is well below current prosthesis manual modeling (11.7±4.01 mm) even without CT imagiology, indicating a considerable step forward towards CT replacement by a 3D scanner for prosthesis personalization.