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Mechanical properties of rotomolded parts with abaca fiber: effect of manufacturing with 1, 2 or 3 layers

Ortega Zaida, Suárez Luis, Kelly-Walley Jake, McCourt Mark

Composites Theory and Practice

2023

R. 23, nr 3

158--166

The range of materials suitable for rotational molding is not as wide as for other polymer processing technologies. An option to reduce the carbon footprint of such materials is to introduce natural fibers, such as abaca. In this work, different loadings of abaca fibers (5 to 20 % by weight) were molded using one, two and three-layer constructions. A comparison of the mechanical behavior (tensile, flexural, and impact properties) with the fiber content, considering the method of obtaining the composite (1, 2 or 3 layers) was performed. The thermomechanical behavior of the matrix was not affected due to the introduction of the fibers; apart from a reduction in the storage modulus, especially at low temperature, the curves have a similar profile. In general terms, the tensile and flexural strength were not affected by the incorporation of the fibers, that is, the composites exhibit similar behavior to neat polyethylene. Significant improvements in the tensile modulus were obtained for the parts manufactured with 2 layers, with 10 wt.% fiber in the internal one. As expected, the impact strength was reduced for all the composites, although the layer of PE on the inner side that coats the fibers counteracts this reduction to a certain extent. An increase in the heating time was observed for all the composites made in different layers; although the incorporation of fibers slightly modifies the course of the curve, the heating time is only significantly increased for loadings over 10%. The higher energy consumption needed to obtain the part in the different layers would only then be justified by an increase in the composite properties, which is not the case of the parts obtained in this work.

Fundamental heart sounds analysis using improved complete ensemble EMD with adaptive noise

Altuve Miguel, Suárez Luis, Ardila Jeyson

Biocybernetics and Biomedical Engineering

2020

Vol. 40, no. 1

426--439

Phonocardiogram (PCG) recordings contain valuable information about the functioning and state of the heart that is useful in the diagnosis of cardiovascular diseases. The first heart sound (S1) and the second heart sound (S2), produced by the closing of the atrioventricular valves and the closing of the semilunar valves, respectively, are the fundamental sounds of the heart. The similarity in morphology and duration of these heart sounds and their superposition in the frequency domain makes it difficult to use them in computer systems to provide an automatic diagnosis. Therefore, in this paper, we analyzed these heart sounds in the intrinsic mode functions (IMF) domain, which were issued from two time-frequency decomposition techniques, the empirical mode decomposition (EMD) and the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), with the aim of retrieving useful information on an expanded basis. The decomposition of PCG recordings into IMF allows representing the fundamental cardiac sounds in many oscillating components, increasing thus the observability of the system. Moreover, the time-frequency representation of PCG recordings could provide valuable information to automatically detect heart sounds and diagnose pathologies from characteristic patterns of these heart sounds in the IMF. The analysis was made through the variance and Shannon's entropy of the heart sounds, observed in time windows located among different IMF. In addition, we determined the frequencies ranges of the IMF from the decomposition of the PCG recordings using both techniques. Given that the frequency content of S1 and S2 is different but overlap each other, and the duration of these sounds are also different, these heart sounds were represented in different IMF with different variances and entropies, in both techniques, but the ICEEMDAN offers a more consistent decomposition of S1 and S2 (they were concentrated in IMF 4-6). The decomposition of PCG signals into IMF has allowed us to identify the frequency components of the IMF in which these sounds are found.