Dynamic mechanical-thermal, microstructural and mechanical analysis of ultra-light polymer-metal composites: influence of forming

A really particular and innovative metal-polymer sandwich material is Hybrix. Hybrix is a product developed and manufactured by Lamera AB, Gothenburg, Sweden. This innovative hybrid material is composed by two relatively thin metal layers if compared to the core thickness. The most used metals are aluminum and stainless steel and are separated by a core of nylon fibres oriented perpendicularly to the metal plates. The core is then completed by adhesive layers applied at the PA66-metal interface that once cured maintain the nylon fibres in position. This special material is very light and formable. Moreover Hybrix, depending on the specific metal which is used, can achieve a good corrosion resistance and it can be cut and punched easily. Hybrix architecture itself provides extremely good bending stiffness, damping properties, insulation capability, etc., which again, of course, change in magnitude depending in the metal alloy which is used, its thickness and core thickness. For these reasons nowadays it shows potential for all the applications which have the above mentioned characteristic as a requirement. Finally Hybrix can be processed with tools used in regular metal sheet industry and can be handled as solid metal sheets. In this master thesis project, pre-formed parts of Hybrix were studied and characterized. Previous work on Hybrix was focused on analyze its market potential and different adhesive to be used in the core. All the tests were carried out on flat unformed specimens. However, in order to have a complete description of this material also the effect of the forming process must be taken into account. Thus the main activities of the present master thesis are the following: Dynamic Mechanical-Thermal Analysis (DMTA) on unformed Hybrix samples of different thickness and on pre-strained Hybrix samples, pure epoxy adhesive samples analysis and finally moisture effects evaluation on Hybrix composite structure.

Dynamic Bayesian network for semantic place classification in mobile robotics

In this paper, the problem of semantic place categorization in mobile robotics is addressed by considering a time-based probabilistic approach called dynamic Bayesian mixture model (DBMM), which is an improved variation of the dynamic Bayesian network. More specifically, multi-class semantic classification is performed by a DBMM composed of a mixture of heterogeneous base classifiers, using geometrical features computed from 2D laserscanner data, where the sensor is mounted on-board a moving robot operating indoors. Besides its capability to combine different probabilistic classifiers, the DBMM approach also incorporates time-based (dynamic) inferences in the form of previous class-conditional probabilities and priors. Extensive experiments were carried out on publicly available benchmark datasets, highlighting the influence of the number of time-slices and the effect of additive smoothing on the classification performance of the proposed approach. Reported results, under different scenarios and conditions, show the effectiveness and competitive performance of the DBMM.