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

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

Easy preparation of liposome@pda microspheres for fast and highly efficient removal of methylene blue from water

Mussel-inspired chemistry was usefully exploited here with the aim of developing a high-efficiency, environmentally friendly material for water remediation. A micro-structured material based on polydopamine (PDA) was obtained by using liposomes as templating agents and was used for the first time as an adsorbent material for the removal of methylene blue (MB) dye from aqueous solutions. Phospholipid liposomes were made by extrusion and coated with PDA by self-polymerization of dopamine under simple and mild conditions. The obtained Liposome@PDA microspheres were characterized by DLS and Zeta potential analysis, TEM microscopy, and FTIR spectroscopy. The effects of pH, temperature, MB concentration, amount of Liposome@PDA, and contact time on the adsorption process were investigated. Results showed that the highest adsorption capacity was obtained in weakly alkaline conditions (pH = 8.0) and that it could reach up to 395.4 mg g−1 at 298 K. In addition, adsorption kinetics showed that the adsorption behavior fits a pseudo-second-order kinetic model well. The equilibrium adsorption data, instead, were well described by Langmuir isotherm. Thermodynamic analysis demonstrated that the adsorption process was endothermic and spontaneous (∆G0 = −12.55 kJ mol−1, ∆H0 = 13.37 kJ mol−1 ) in the investigated experimental conditions. Finally, the applicability of Liposome@PDA microspheres to model wastewater and the excellent reusability after regeneration by removing MB were demonstrated

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

Observation of periodic variable stars towards the galactic spiral arms by EROS II

We present the results of a massive variability search based on a photometric survey of a six square degree region along the Galactic plane at ($l = 305^\circ$, $b = -0.8^\circ$) and ($l = 330^\circ$, $b = -2.5^\circ$). This survey was performed in the framework of the EROS II (Exp\'erience de Recherche d'Objets Sombres) microlensing program. The variable stars were found among 1,913,576 stars that were monitored between April and June 1998 in two passbands, with an average of 60 measurements. A new period-search technique is proposed which makes use of a statistical variable that characterizes the overall regularity of the flux versus phase diagram. This method is well suited when the photometric data are unevenly distributed in time, as is our case. 1,362 objects whose luminosity varies were selected. Among them we identified 9 Cepheids, 19 RR Lyrae, 34 Miras, 176 eclipsing binaries and 266 Semi-Regular stars. Most of them are newly identified objects. The cross-identification with known catalogues has been performed. The mean distance of the RR Lyrae is estimated to be $\sim 4.9 \pm 0.3$ kpc undergoing an average absorption of $\sim 3.4 \pm 0.2$ magnitudes. This distance is in good agreement with the one of disc stars which contribute to the microlensing source star population.Our catalogue and light curves are available electronically from the CDS, Strasbourg and from our Web site http://eros.in2p3.fr.Comment: 15 pages, 11 figures, accepted in A&A (april 2002

Severity Index for Suspected Arbovirus (SISA) : machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection

Funding: This study was supported, in part, by the Department of Defense Global Emerging Infection Surveillance (https://health.mil/Military-Health-Topics/Combat-Support/Armed-Forces-Health-Surveillance-Branch/Global-Emerging-Infections-Surveillance-and-Response) grant (P0220_13_OT) and the Department of Medicine of SUNY Upstate Medical University (http://www.upstate.edu/medicine/). D.F., M.H. and P.H. were supported by the Ben Kean Fellowship from the American Society for Tropical Medicine and Hygeine (https://www.astmh.org/awards-fellowships-medals/benjamin-h-keen-travel-fellowship-in-tropical-medi). S.J.R and A.M.S-I were supported by NSF DEB EEID 1518681, NSF DEB RAPID 1641145 (https://www.nsf.gov/), A.M.S-I was additionally supported by the Prometeo program of the National Secretary of Higher Education, Science, Technology, and Innovation of Ecuador (http://prometeo.educacionsuperior.gob.ec/).Background: Dengue, chikungunya, and Zika are arboviruses of major global health concern. Decisions regarding the clinical management of suspected arboviral infection are challenging in resource-limited settings, particularly when deciding on patient hospitalization. The objective of this study was to determine if hospitalization of individuals with suspected arboviral infections could be predicted using subject intake data. Methodology/Principal findings: Two prediction models were developed using data from a surveillance study in Machala, a city in southern coastal Ecuador with a high burden of arboviral infections. Data were obtained from subjects who presented at sentinel medical centers with suspected arboviral infection (November 2013 to September 2017). The first prediction model-called the Severity Index for Suspected Arbovirus (SISA)-used only demographic and symptom data. The second prediction model-called the Severity Index for Suspected Arbovirus with Laboratory (SISAL)-incorporated laboratory data. These models were selected by comparing the prediction ability of seven machine learning algorithms; the area under the receiver operating characteristic curve from the prediction of a test dataset was used to select the final algorithm for each model. After eliminating those with missing data, the SISA dataset had 534 subjects, and the SISAL dataset had 98 subjects. For SISA, the best prediction algorithm was the generalized boosting model, with an AUC of 0.91. For SISAL, the best prediction algorithm was the elastic net with an AUC of 0.94. A sensitivity analysis revealed that SISA and SISAL are not directly comparable to one another. Conclusions/Significance: Both SISA and SISAL were able to predict arbovirus hospitalization with a high degree of accuracy in our dataset. These algorithms will need to be tested and validated on new data from future patients. Machine learning is a powerful prediction tool and provides an excellent option for new management tools and clinical assessment of arboviral infection.Publisher PDFPeer reviewe