Effect of the Impact Conditions on the Mechanical Properties of Injection-Molded Parts

This work focused on the study of the impact event on molded parts in the framework of automotive components. The influence of the impact conditions and processing parameters on the mechanical behavior of talc-filled polypropylene specimens was analyzed. The specimens were lateral-gate discs produced by injection molding, and the mechanical characterization was performed through instrumented falling weight impact tests concomitantly assisted with high-speed videography. Results analyzed using the analysis of variance (ANOVA) method have shown that from the considered parameters, only the dart diameter and test temperature have significant influence on the falling weight impact properties. Higher dart diameter leads to higher peak force and peak energy results. Conversely, higher levels of test temperatures lead to lower values of peak force and peak energy. By means of high-speed videography, a more brittle fracture was observed for experiments with higher levels of test velocity and dart diameter and lower levels of test temperature. The injection-molding process conditions assessed in this study have an influence on the impact response of moldings, mainly on the deformation capabilities of the moldings.

Virtual simulation of the postsurgical cosmetic outcome in patientswith pectus excavatum

Pectus excavatum is the most common congenital deformity of the anterior chest wall, in which several ribs and the sternum grow abnormally. Nowadays, the surgical correction is carried out in children and adults through Nuss technic. This technic has been shown to be safe with major drivers as cosmesis and the prevention of psychological problems and social stress. Nowadays, no application is known to predict the cosmetic outcome of the pectus excavatum surgical correction. Such tool could be used to help the surgeon and the patient in the moment of deciding the need for surgery correction. This work is a first step to predict postsurgical outcome in pectus excavatum surgery correction. Facing this goal, it was firstly determined a point cloud of the skin surface along the thoracic wall using Computed Tomography (before surgical correction) and the Polhemus FastSCAN (after the surgical correction). Then, a surface mesh was reconstructed from the two point clouds using a Radial Basis Function algorithm for further affine registration between the meshes. After registration, one studied the surgical correction influence area (SCIA) of the thoracic wall. This SCIA was used to train, test and validate artificial neural networks in order to predict the surgical outcome of pectus excavatum correction and to determine the degree of convergence of SCIA in different patients. Often, ANN did not converge to a satisfactory solution (each patient had its own deformity characteristics), thus invalidating the creation of a mathematical model capable of estimating, with satisfactory results, the postsurgical outcome