Processing of hybrid laminates integrating ZrB2/SiC and SiC layers

Tape casting technique was used to develop hybrid laminates constituting by SiC and ZrB2-SiC layers; the main aim is obtaining a structure which integrate the unique properties of these materials and potentially extent their application temperature range. Multilayer with ZrB2-SiC layers stacked in between SiC ones were successfully processed. Thin cracks propagated in the composite layers without affecting SiC ones; their formation was due to residual stresses developed in the two materials because of the differences in their shrinkage and coefficients of thermal expansion. However, these cracks did not significantly affect the material properties: relative density, elastic modulus and flexural strength of hybrid laminates was indeed only slightly lower than those of laminates made up of layers with the same composition

Process phenomena and material properties in selective laser sintering of polymers: A review

Selective laser sintering (SLS) is a powder bed fusion technology that uses a laser source to melt selected regions of a polymer powder bed based on 3D model data. Components with complex geometry are then obtained using a layer-by-layer strategy. This additive manufacturing technology is a very complex process in which various multiphysical phenomena and different mechanisms occur and greatly influence both the quality and performance of printed parts. This review describes the physical phenomena involved in the SLS process such as powder spreading, the interaction between laser beam and powder bed, polymer melting, coalescence of fused powder and its densification, and polymer crystallization. Moreover, the main characterization approaches that can be useful to investigate the starting material properties are reported and discussed

3D Printing of Low-Filled Basalt PA12 and PP Filaments for Automotive Components

Fused Deposition Modeling (FDM) enables many advantages compared to traditional manufacturing techniques, but the lower mechanical performance due to the higher porosity still hinders its industrial spread in key sectors like the automotive industry. PP and PA12 filaments filled with low amounts of basalt fibers were produced in the present work to improve the poor mechanical properties inherited from the additive manufacturing technique. For both matrices, the introduction of 5 wt.% of basalt fibers allows us to achieve stiffness values comparable to injection molding ones without modifying the final weight of the manufactured components. The increased filament density compared with the neat polymers, upon the introduction of basalt fibers, is counterbalanced by the intrinsic porosity of the manufacturing technique. In particular, the final components are characterized by a 0.88 g/cm3 density for PP and 1.01 g/cm3 for PA12 basalt-filled composites, which are comparable to the 0.91 g/cm3 and 1.01 g/cm3, respectively, of the related neat matrix used in injection molding. Some efforts are still needed to fill the gap of 15–28% for PP and of 26.5% for PA12 in tensile strength compared to injection-molded counterparts, but the improvement of the fiber/matrix interface by fiber surface modification or coupling agent employment could be a feasible solution

Pea protein concentrate as a substitute for fish meal protein in sea bass diet

Pea seeds, even if lower in protein than oilseed meals, have been shown to successfully replace moderate amounts of fish meal protein in diets for carnivorous fish species (Kaushik et al., 1993, Gouveia and Davies, 2000). A further processing of such pulses provides concentrated protein products which look very promising as fish meal substitutes in aquafeeds (Thiessen et al., 2003). The aim of the present study was to evaluate nutrient digestibility, growth response, nutrient and energy retention efficiencies and whole body composition of sea bass (Dicentrarchus labrax, L.) fed complete diets in which a pea protein concentrate (PPC) was used to replace graded levels of fish meal protein

Optimization of selective laser sintering process conditions using stable sintering region approach

The optimization of process parameters represents one of the major drawbacks of selective laser sintering (SLS) technology since it is largely empirical and based on performing a series of trial-and-error builds. This approach is time con-suming, costly, and it ignores the properties of starting powders. This paper provides new results into the prediction of processing conditions starting from the material properties. The stable sintering region (SSR) approach has been applied to two different polymer-based powders: a polyamide 12 filled with chopped carbon fibers and polypropylene. This study shows that the laser exposure parameters suitable for successful sintering are in a range that is significantly smaller than the SSR. For both powders, the best combination of mechanical properties, dimensional accuracy, and porosity level are in fact, achieved by using laser energy density values placed in the middle of the SSR

Deep Learning of Transferable MIMO Channel Modes for 6G V2X Communications

In the emerging high mobility vehicle-to-everything (V2X) communications using millimeter wave (mmWave) and sub-THz, multiple-input multiple-output (MIMO) channel estimation is an extremely challenging task. At mmWaves/sub-THz frequencies, MIMO channels exhibit few leading paths in the space-time (ST) domain (i.e., directions or arrival/departure and delays). Algebraic low-rank (LR) channel estimation exploits ST channel sparsity through the computation of position-dependent MIMO channel eigenmodes leveraging recurrent training vehicle passages in the coverage cell. LR requires vehicles' geographical positions and tens to hundreds of training vehicles' passages for each position, leading to significant complexity and control signaling overhead. Here, we design a deep-learning (DL)-based LR channel estimation method to infer MIMO channel eigenmodes in V2X urban settings, starting from a single least squares (LS) channel estimate and without needing vehicle's position information. Numerical results show that the proposed method attains comparable mean squared error (mse) performance as the position-based LR. Moreover, we show that the proposed model can be trained on a reference scenario and be effectively transferred to urban contexts with different ST channel features, providing comparable mse performance without an explicit transfer learning procedure. This result eases the deployment in arbitrary dense urban scenarios

Position-agnostic Algebraic Estimation of 6G V2X MIMO Channels via Unsupervised Learning

MIMO systems in the context of 6G Vehicle-to-Everything (V2X) will require an accurate channel knowledge to enable efficient communication. Standard channel estimation techniques, such as Unconstrained Maximum Likelihood (U-ML), are extremely noisy in massive MIMO settings, while structured approaches, e.g., compressed sensing, are sensitive to hardware impairments. We propose a novel multi-vehicular algebraic channel estimation method for 6G V2X based on unsupervised learning which exploits recurrent vehicle passages in typical urban settings. Multiple training sequences from different vehicle passages are clustered via K-medoids algorithm based on their algebraic similarity to retrieve the MIMO channel eigenmodes, which can be used to improve the channel estimates. Numerical results show the presence of an optimal number of clusters and remarkable benefits of the proposed method in terms of Mean Squared Error (MSE) compared to standard U-ML solution (15 dB less)

Position-agnostic Algebraic Estimation of 6G V2X MIMO Channels via Unsupervised Learning

MIMO systems in the context of 6G Vehicle-to-Everything (V2X) will require an accurate channel knowledge to enable efficient communication. Standard channel estimation techniques, such as Unconstrained Maximum Likelihood (U-ML), are extremely noisy in massive MIMO settings, while structured approaches, e.g., compressed sensing, are sensitive to hardware impairments. We propose a novel multi-vehicular algebraic channel estimation method for 6G V2X based on unsupervised learning which exploits recurrent vehicle passages in typical urban settings. Multiple training sequences from different vehicle passages are clustered via K-medoids algorithm based on their algebraic similarity to retrieve the MIMO channel eigenmodes, which can be used to improve the channel estimates. Numerical results show the presence of an optimal number of clusters and remarkable benefits of the proposed method in terms of Mean Squared Error (MSE) compared to standard U-ML solution (15 dB less)

Selective Laser Sintering versus Multi Jet Fusion: A Comprehensive Comparison Study Based on the Properties of Glass Beads-Reinforced Polyamide 12

Selective laser sintering (SLS) and multi jet fusion (MJF) are the most widespreadpowder bed fusion additive manufacturing techniques for fabricating polymericparts since they offer great designflexibility, productivity, and geometricalaccuracy. However, these technologies differ in the thermal energy source usedto melt the powders as well as the innovative use of printing agents featured inthe latter one to promote material consolidation and to avoid thermal bleeding atthe part contours. The use of a single powder made of glass beads-reinforcedpolyamide 12 (PA12/GB) for the fabrication of MJF and SLS samples makespossible a systematic comparison of the printed parts properties. A thoughtfulanalysis of the microstructure and mechanical properties of the samples revealsdifferences and peculiarities between the two technologies. SLS exhibits lowerporosity and higher mechanical performances when the parts are printed alongthe build plane thanks to the powerful heating ensured by the laser. In contrast,MJF samples show higher mechanical isotropy with greatflexural and tensilebehavior for vertically oriented parts. The role of glass beads in the materialbehavior is defined by their mechanical properties, meaning higher rigidity andlower strength compared to neat PA12, and fracture mechanism

Novel 3D printable bio-based and biodegradable poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) microspheres for selective laser sintering applications

Selective laser sintering (SLS) has become the most popular additive manufacturing process due to its high accuracy, productive efficiency, and surface quality. However, currently there are still very few commercially available polymeric materials suitable for this technique. This research work focused on the fabrication and characterization of bio-based and biodegradable microspheres obtained by oil-in-water emulsion solvent evaporation, starting from a poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) (PHBH) biopolymer matrix. First, the fabrication parameters were optimized to improve the morphological, thermal, and flowability properties of the synthetized microspheres. Once the best production conditions were established, the PHBH microspheres were further used to study their effective 3D printability on an SLS 3D printer using geometries varying from simple shapes to architectures with more complex internal patterns. The results of this research revealed that PHBH has promising applicability for the SLS technique. This study undertook the first step toward broadening the range of polymeric materials for this additive manufacturing technology. These findings will contribute to a greater and wider dissemination of the SLS technique in the future, as well as they will bring this manufacturing process closer to applications, such as the biomedical sector, where the use of biodegradable and biocompatible materials can add value to the final application