Molecular dynamics computer simulation of scratch resistance testing of polymers: visualization

Experimental scratch resistance testing provides two numbers: the penetration depth Rp and the healing depth Rh. In molecular dynamics computer simulations, we create a material consisting of N statistical chain segments by polymerization; a reinforcing phase can be included. Then we simulate the movement of an indenter and response of the segments during X time steps. Each segment at each time step has three Cartesian coordinates of position and three of momentum. We describe methods of visualization of results based on a record of 6NX coordinates. We obtain a continuous dependence on time t of positions of each of the segments on the path of the indenter. Scratch resistance at a given location can be connected to spatial structures of individual polymeric chains.

Coupling Mechanical and Electrical Behavior in the Multi-scale Simulation of Polymer-based CNT Nanocomposites

A numeric model has been proposed to investigate the mechanical and electrical properties of a polymeric/carbon nanotube (CNT) composite material subjected to a deformation force. The reinforcing phase affects the behavior of the polymeric matrix and depends on the nanofiber aspect ratio and preferential orientation. The simulations show that the mechanical behavior of a computer generated material (CGM) depends on fiber length and initial orientation in the polymeric matrix. It is also shown how the conductivity of the polymer/CNT composite can be calculated for each time step of applied stress, effectively providing the ability to simulate and predict strain-dependent electrical behavior of CNT nanocomposites.

A Methodology for GUI Layer Redefinition Through Virtualization and Computer Vision

Nowadays despite improvements in usability and intuitiveness users have to adapt to the proposed systems to satisfy their needs. For instance, they must learn how to achieve tasks, how to interact with the system, and fulfill system's specifications. This paper proposes an approach to improve this situation enabling graphical user interface redefinition through virtualization and computer vision with the aim of increasing the system's usability. To achieve this goal the approach is based on enriched task models, virtualization and picture-driven computing.

Improving FEM crash simulation accuracy through local thickness estimation based on CAD data

In this paper, we present a method for estimating local thickness distribution in nite element models, applied to injection molded and cast engineering parts. This method features considerable improved performance compared to two previously proposed approaches, and has been validated against thickness measured by di erent human operators. We also demonstrate that the use of this method for assigning a distribution of local thickness in FEM crash simulations results in a much more accurate prediction of the real part performance, thus increasing the bene ts of computer simulations in engineering design by enabling zero-prototyping and thus reducing product development costs. The simulation results have been compared to experimental tests, evidencing the advantage of the proposed method. Thus, the proposed approach to consider local thickness distribution in FEM crash simulations has high potential on the product development process of complex and highly demanding injection molded and casted parts and is currently being used by Ford Motor Company.

Voxel-based registration of simulated and real patient CBCT data for accurate dental implant pose estimation

The success of dental implant-supported prosthesis is directly linked to the accuracy obtained during implant’s pose estimation (position and orientation). Although traditional impression techniques and recent digital acquisition methods are acceptably accurate, a simultaneously fast, accurate and operator-independent methodology is still lacking. Hereto, an image-based framework is proposed to estimate the patient-specific implant’s pose using cone-beam computed tomography (CBCT) and prior knowledge of implanted model. The pose estimation is accomplished in a threestep approach: (1) a region-of-interest is extracted from the CBCT data using 2 operator-defined points at the implant’s main axis; (2) a simulated CBCT volume of the known implanted model is generated through Feldkamp-Davis-Kress reconstruction and coarsely aligned to the defined axis; and (3) a voxel-based rigid registration is performed to optimally align both patient and simulated CBCT data, extracting the implant’s pose from the optimal transformation. Three experiments were performed to evaluate the framework: (1) an in silico study using 48 implants distributed through 12 tridimensional synthetic mandibular models; (2) an in vitro study using an artificial mandible with 2 dental implants acquired with an i-CAT system; and (3) two clinical case studies. The results shown positional errors of 67±34μm and 108μm, and angular misfits of 0.15±0.08º and 1.4º, for experiment 1 and 2, respectively. Moreover, in experiment 3, visual assessment of clinical data results shown a coherent alignment of the reference implant. Overall, a novel image-based framework for implants’ pose estimation from CBCT data was proposed, showing accurate results in agreement with dental prosthesis modelling requirements.