Browsing by Author "Rivero, Asun"
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Item An intelligent process model: predicting springback in single point incremental forming(2015-02) Khan, Muhamad S.; Coenen, Frans; Dixon, Clare; El-Salhi, Subhieh; Penalva, Mariluz; Rivero, Asun; FABRIC_INTEL; SGThis paper proposes an intelligent process model (IPM), founded on the concept of data mining, for predicting springback in the context of sheet metal forming, in particular, single point incremental forming (SPIF). A limitation with the SPIF process is that the application of the process results in geometric deviations, which means that the resulting shape is not necessarily the desired shape. Errors are introduced in a nonlinear manner for a variety of reasons, but a contributor is the geometry of the desired shape. A local geometry matrix (LGM) representation is used that allows the capture of local geometries in such a way that they are suited to input to a classifier generator. It is demonstrated that a rule-based classifier can be used to train the classifier and generate a classification model. The resulting model can then be used to predict errors with respect to new shapes so that some correction strategy can be applied. The reported evaluation of the proposed IPM indicates that very promising results can be obtained with regard to reducing the shape deviations due to springback.Item A non-destructive quality assessment for blind-fastener installations based on the combination of ultrasound techniques and real-time monitoring of the fastening process(2016-05-01) Camacho, Javier; Rivero, Asun; Veiga, Fernando; Guzman, Desiree; SG; Tecnalia Research & InnovationThis paper proposes and evaluates a non-destructive method in assessing the quality of blind-fastener installations. The method is based on combining the time of flight of an ultrasonic pulse along the pin of the fastener and the fastener installation time. The former has to be measured after the fastening process; the latter has to be acquired at the machine during the fastening. The tests performed on carbon fibre reinforced plastic laminates joined by means of ABS0257 blind fasteners have proved that the method can distinguish incorrectly installed fasteners due to improper grip selection. The hit rate of the developed non-destructive method is above 95 % for the tested range of fastener sizes. The implementation of this method might prevent the use of extra fasteners, currently required, in order to compensate for the uncertainty of blind-fastener installations