Wear of Polished Steel Surfaces in Dry Friction Linear Contact on Polimer Composites with Glass Fibres

It is generally known that the friction and wear between polymers and polished steel surfaces has a special character, the behaviour to friction and wear of a certain polymer might not be valid for a different polymer, moreover in dry friction conditions. In this paper, we study the reaction to wear of certain polymers with short glass fibres on different steel surfaces, considering the linear friction contact, observing the friction influence over the metallic surfaces wear. The paper includes also its analysis over the steel’s wear from different points of view: the reinforcement content influence and tribological parameters (load, contact pressure, sliding speed, contact temperature, etc.). Thus, we present our findings related to the fact that the abrasive component of the friction force is more significant than the adhesive component, which generally is specific to the polymers’ friction. Our detections also state that, in the case of the polyamide with 30% glass fibres, the steel surface linear wear rate order are of 10-4 mm/h, respectively the order of volumetric wear rate is of 10-6 cm3 /h. The resulting volumetric wear coefficients are of the order (10-11 – 10-12) cm3/cm and respectively linear wear coefficients of 10-9 mm/cm

3-D Tracking and Visualization of Hundreds of Pt-Co Fuel Cell Nanocatalysts During Electrochemical Aging

We present an electron tomography method that allows for the identification of hundreds of electrocatalyst nanoparticles with one-to-one correspondence before and after electrochemical aging. This method allows us to track, in three-dimensions (3-D), the trajectories and morphologies of each Pt-Co nanocatalyst on a fuel cell carbon support. The use of atomic-scale electron energy loss spectroscopic imaging enables the correlation of performance degradation of the catalyst with changes in particle/inter-particle morphologies, particle-support interactions and the near-surface chemical composition. We found that, aging of the catalysts under normal fuel cell operating conditions (potential scans from +0.6 V to +1.0 V for 30,000 cycles) gives rise to coarsening of the nanoparticles, mainly through coalescence, which in turn leads to the loss of performance. The observed coalescence events were found to be the result of nanoparticle migration on the carbon support during potential cycling. This method provides detailed insights into how nanocatalyst degradation occurs in proton exchange membrane fuel cells (PEMFCs), and suggests that minimization of particle movement can potentially slow down the coarsening of the particles, and the corresponding performance degradation.Comment: Nano Letters, accepte

Assessing real world imagery in virtual environments for people with cognitive disabilities

People with cognitive disabilities are often socially excluded. We propose a system based on Virtual and Augmented Reality that has the potential to act as an educational and support tool in everyday tasks for people with cognitive disabilities. Our solution consists of two components: the first that enables users to train for several essential quotidian activities and the second that is meant to offer real time guidance feedback for immediate support. In order to illustrate the functionality of our proposed system, we chose to train and support navigation skills. Thus, we conducted a preliminary study on people with Down Syndrome (DS) based on a navigation task. Our experiment was aimed at evaluating the visual and spatial perception of people with DS when interacting with different elements of our system. We provide a preliminary evaluation that illustrates how people with DS perceive different landmarks and types of visual feedback, in static images and videos. Although we focused our study on people with DS, people with different cognitive disabilities could also benefit from the features of our solution. This analysis is mandatory in the design of a virtual intelligent system with several functionalities that aims at helping disabled people in developing basic knowledge in every day tasks

Autonomous Soft Robotic Fish Capable of Escape Maneuvers Using Fluidic Elastomer Actuators

In this work we describe an autonomous soft-bodied robot that is both self-contained and capable of rapid, continuum-body motion. We detail the design, modeling, fabrication, and control of the soft fish, focusing on enabling the robot to perform rapid escape responses. The robot employs a compliant body with embedded actuators emulating the slender anatomical form of a fish. In addition, the robot has a novel fluidic actuation system that drives body motion and has all the subsystems of a traditional robot onboard: power, actuation, processing, and control. At the core of the fish's soft body is an array of fluidic elastomer actuators. We design the fish to emulate escape responses in addition to forward swimming because such maneuvers require rapid body accelerations and continuum-body motion. These maneuvers showcase the performance capabilities of this self-contained robot. The kinematics and controllability of the robot during simulated escape response maneuvers are analyzed and compared with studies on biological fish. We show that during escape responses, the soft-bodied robot has similar input–output relationships to those observed in biological fish. The major implication of this work is that we show soft robots can be both self-contained and capable of rapid body motion.National Science Foundation (U.S.) (NSF IIS1226883)National Science Foundation (U.S.) (NSF CCF1138967)National Science Foundation (U.S.) (1122374

Task-Specific Sensor Planning for Robotic Assembly Tasks

When performing multi-robot tasks, sensory feedback is crucial in reducing uncertainty for correct execution. Yet the utilization of sensors should be planned as an integral part of the task planning, taken into account several factors such as the tolerance of different inferred properties of the scene and interaction with different agents. In this paper we handle this complex problem in a principled, yet efficient way. We use surrogate predictors based on open-loop simulation to estimate and bound the probability of success for specific tasks. We reason about such task-specific uncertainty approximants and their effectiveness. We show how they can be incorporated into a multi-robot planner, and demonstrate results with a team of robots performing assembly tasks