The automotive industry is one of the most contaminant; for this reason, solutions in efficient matter has been proposed over the years. This research contributes to this subject by evaluating the thermal comfort in the internal air of a vehicle by using a 20 mm layer of a phase-change material attached to the rooftop interior of a car. The phase-change material selection is based on a list of other materials proposed in previous research and chosen by multicriteria decision methods. In this sense, the material savENRG PCM-HS22P proved to be the best. Moreover, a simulation using the finite elements method showed how the PCM reduced the temperature of the air by 9 °C when heating and by 4 °C when the temperature drops. To conclude, the multicriteria selection methods chose the best material to absorb energy during the charging process and released it during the discharging event in this automotive application.
This paper shows a methodology for on-line recognition and classification of pieces in robotic assembly tasks and its application into an intelligent manufacturing cell. The performance of industrial robots working in unstructured environments can be improved using visual perception and learning techniques The object recognition is accomplished using a neuronal network with FuzzyARTMAP architecture for learning and recognition purposes, which receives a descriptor vector called CFD&POSE as the input. This vector represents an innovative methodology for classification and identification of pieces in robotic tasks, every single stage of the methodology, is described step by step and the proposed algorithms explained. The vector compresses 3D object data from assembly parts and is invariant to scale, rotation and orientation. The approach in combination with the fast learning capability of ART networks indicates the suitability for industrial robot applications as it is shown in experimental results and the possibility to add concatenated information into the descriptor vector to achieve a much more robust methodology.
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