Manufacturing of conductive structural composites through spraying of CNTs/epoxy dispersions on dry carbon fiber plies

In this work, multiscale Carbon Fiber-Reinforced Polymers have been manufactured by inserting carbon nanotubes in the matrix of the composite material to improve and homogenize the through-thickness electrical conductivity. A first part of this work introduces a spraying technique and manufacturing process followed to produce the CNT-doped multiscale CFRP. A quality assessment of the produced material is also presented. A second part investigates the electrical conductivity, as well as a few mechanical properties of the newly manufactured material, to be able to conclude on the viability and potential of this technique. This paper presents the further development of an earlier study presenting the thermal, rheological and electrical behavior of the CNT doped epoxy matrix (Fogel et al., 2015)

Process monitoring with FBG sensors during vacuum infusion of thick composite laminates

During manufacturing of thick (>20 mm) laminates, thermal gradients through the thickness may arise due to, for example, exothermal reactive heat release. These thermal variations may result in residual strain gradients through the thickness as well as variations in polymer matrix properties, such as degree of cure. For prediction and simulation of the residual strains, it is essential that the manufacturing process is monitored, in order to identify the parameters responsible for the residual strain build-up. The research described in this paper, proposes the use of Fibre Bragg Grating sensors as an experimental tool to determine variations in (thermal) residual strain levels through the thickness in a thick glass fibre reinforced thermoset laminate. In addition, other manufacturing issues, such as the flow behaviour that could be identified with these sensors were addressed. Moreover, the results of a first attempt to identify polymer property variations through the thickness by means of the microhardness test are reported

Optical thermal model for LED heating in thermoset-automated fiber placement

Control of material temperature distribution and governing phenomena during automated fiber placement is an important factor. Numerical modeling of the radiative heat transfer for a newly presented LED-based heating unit is developed and analyzed in theory. An optical model allows taking into account the radiative energy output of every individual LED. By adjusting the electrical input to the multiple LED arrays on the heating unit, the irradiance distribution on the substrate’s surface can be controlled. To investigate the capability to adjust the surface temperature distribution resulting from this feature, thermal models for two and three dimensions are developed and employed for the calculated irradiance distributions. The resulting temperature distributions show that temperature gradients can be avoided or created, depending on the input to the heating unit. The results from the two models are compared and a method to select an appropriate model in general is proposed

Thermal, rheological and electrical analysis of MWCNTs/epoxy matrices

International audienceIn this study, the cure kinetics, rheological and electrical behaviors of the MWCNTs/epoxy nanocomposites produced using a three-roll mill are studied. After defining the domain of linear response, the influence of temperature and MWCNTs on the shear viscosity has been investigated. The shear-thinning effect caused by adding CNTs to the epoxy matrix is more pronounced at increased temperature and MWCNT weight content. Furthermore, a mechanical manifestation of the percolation phenomenon may have been observed. At last the electrical conductivity was investigated to characterize the percolation behavior and determine the best CNT content/electrical properties ratio

Autonomous Flying With Neuromorphic Sensing

International audienceAutonomous flight for large aircraft appears to be within our reach. However, launching autonomous systems for everyday missions still requires an immense interdisciplinary research effort supported by pointed policies and funding. We believe that concerted endeavors in the fields of neuroscience, mathematics, sensor physics, robotics, and computer science are needed to address remaining crucial scientific challenges. In this paper, we argue for a bio-inspired approach to solve autonomous flying challenges, outline the frontier of sensing, data processing, and flight control within a neuromorphic paradigm, and chart directions of research needed to achieve operational capabilities comparable to those we observe in nature. One central problem of neuromorphic computing is learning. In biological systems, learning is achieved by adaptive and relativistic information acquisition characterized by near-continuous information retrieval with variable rates and sparsity. This results in both energy and computational resource savings being an inspiration for autonomous systems. We consider pertinent features of insect, bat and bird flight behavior as examples to address various vital aspects of autonomous flight. Insects exhibit sophisticated flight dynamics with comparatively reduced complexity of the brain. They represent excellent objects for the study of navigation and flight control. Bats and birds enable more complex models of attention and point to the importance of active sensing for conducting more complex missions. The implementation of neuromorphic paradigms for autonomous flight will require fundamental changes in both traditional hardware and software. We provide recommendations for sensor hardware and processing algorithm development to enable energy efficient and computationally effective flight control

Autonomous Flying With Neuromorphic Sensing

Autonomous flight for large aircraft appears to be within our reach. However, launching autonomous systems for everyday missions still requires an immense interdisciplinary research effort supported by pointed policies and funding. We believe that concerted endeavors in the fields of neuroscience, mathematics, sensor physics, robotics, and computer science are needed to address remaining crucial scientific challenges. In this paper, we argue for a bio-inspired approach to solve autonomous flying challenges, outline the frontier of sensing, data processing, and flight control within a neuromorphic paradigm, and chart directions of research needed to achieve operational capabilities comparable to those we observe in nature. One central problem of neuromorphic computing is learning. In biological systems, learning is achieved by adaptive and relativistic information acquisition characterized by near-continuous information retrieval with variable rates and sparsity. This results in both energy and computational resource savings being an inspiration for autonomous systems. We consider pertinent features of insect, bat and bird flight behavior as examples to address various vital aspects of autonomous flight. Insects exhibit sophisticated flight dynamics with comparatively reduced complexity of the brain. They represent excellent objects for the study of navigation and flight control. Bats and birds enable more complex models of attention and point to the importance of active sensing for conducting more complex missions. The implementation of neuromorphic paradigms for autonomous flight will require fundamental changes in both traditional hardware and software. We provide recommendations for sensor hardware and processing algorithm development to enable energy efficient and computationally effective flight control.Control & SimulationControl & Operation