Thermomechanical environment characterisation in injection moulding and its relation to the mechanical properties of talc-filled polypropylene

This study is focused on the establishment of relationships between the injection moulding processing conditions, the applied thermomechanical environment (TME) and the tensile properties of talc-filled polypropylene,adopting a new extended concept of thermomechanical indices (TMI). In this approach, TMI are calculated from computational simulations of the moulding process that characterise the TME during processing, which are then related to the mechanical properties of the mouldings. In this study, this concept is extended to both the filling and the packing phases, with new TMI defined related to the morphology developed during these phases. A design of experiments approach based on Taguchi orthogonal arrays was adopted to vary the injection moulding parameters (injection flow rate, injection temperature, mould wall temperature and holding pressure), and thus, the TME. Results from analysis of variance for injection-moulded tensile specimens have shown that among the considered processing conditions, the flow rate is the most significant parameter for the Young’s modulus; the flow rate and melt temperature are the most significant for the strain at break; and the holding pressure and flow rate are the most significant for the stress at yield. The yield stress and Young’s modulus were found to be governed mostly by the thermostress index (TSI, related to the orientation of the skin layer), whilst the strain at break depends on both the TSI and the cooling index (CI, associated to the crystallinity degree of the core region). The proposed TMI approach provides predictive capabilities of the mechanical response of injection-moulded components, which is a valuable input during their design stage.

Automated traffic route identification through the shared nearest neighbour algorithm

Many organisations need to extract useful information from huge amounts of movement data. One example is found in maritime transportation, where the automated identification of a diverse range of traffic routes is a key management issue for improving the maintenance of ports and ocean routes, and accelerating ship traffic. This paper addresses, in a first stage, the research challenge of developing an approach for the automated identification of traffic routes based on clustering motion vectors rather than reconstructed trajectories. The immediate benefit of the proposed approach is to avoid the reconstruction of trajectories in terms of their geometric shape of the path, their position in space, their life span, and changes of speed, direction and other attributes over time. For clustering the moving objects, an adapted version of the Shared Nearest Neighbour algorithm is used. The motion vectors, with a position and a direction, are analysed in order to identify clusters of vectors that are moving towards the same direction. These clusters represent traffic routes and the preliminary results have shown to be promising for the automated identification of traffic routes with different shapes and densities, as well as for handling noise data.

Fast left ventricle tracking in CMR images using localized anatomical affine optical flow

In daily cardiology practice, assessment of left ventricular (LV) global function using non-invasive imaging remains central for the diagnosis and follow-up of patients with cardiovascular diseases. Despite the different methodologies currently accessible for LV segmentation in cardiac magnetic resonance (CMR) images, a fast and complete LV delineation is still limitedly available for routine use. In this study, a localized anatomically constrained affine optical flow method is proposed for fast and automatic LV tracking throughout the full cardiac cycle in short-axis CMR images. Starting from an automatically delineated LV in the end-diastolic frame, the endocardial and epicardial boundaries are propagated by estimating the motion between adjacent cardiac phases using optical flow. In order to reduce the computational burden, the motion is only estimated in an anatomical region of interest around the tracked boundaries and subsequently integrated into a local affine motion model. Such localized estimation enables to capture complex motion patterns, while still being spatially consistent. The method was validated on 45 CMR datasets taken from the 2009 MICCAI LV segmentation challenge. The proposed approach proved to be robust and efficient, with an average distance error of 2.1 mm and a correlation with reference ejection fraction of 0.98 (1.9 ± 4.5%). Moreover, it showed to be fast, taking 5 seconds for the tracking of a full 4D dataset (30 ms per image). Overall, a novel fast, robust and accurate LV tracking methodology was proposed, enabling accurate assessment of relevant global function cardiac indices, such as volumes and ejection fraction.