Influences of processing parameters and heat treatment on microstructure and mechanical behavior of Ti-6Al-4V fabricated using selective laser melting

Selective laser melting (SLM) has provided an alternative to the conventional fabrication techniques for Ti-6Al-4V alloy parts because of its flexibility and ease in creating complex features. Therefore, this study investigated the effects of the process parameters and heat treatment on the microstructure and mechanical properties of Ti-6Al-4V fabricated using SLM. The influences of various process parameters on the relative density, tensile properties, impact toughness, and hardness of Ti-6Al-4V alloy parts were studied. By employing parameter optimization, a high-density high-strength Ti-6Al-4V alloy was fabricated by SLM. A relative density of 99.45%, a tensile strength of 1 188 MPa, and an elongation to failure of 9.5% were achieved for the SLM-fabricated Ti-6Al-4V alloy with optimized parameters. The effects of annealing and solution aging heat treatment on the mechanical properties, phase composition, and microstructure of the SLM-fabricated Ti-6Al-4V alloy were also studied. The ductility of the heat-treated Ti-6Al-4V alloy was improved. By applying a heat treatment at 850 ℃ for 2 h, followed by furnace cooling, the elongation to failure and impact toughness were found to be increased from 9.5% to 12.5%, and from 24.13 J/cm² to 47.51 J/cm², respectively.The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-022-00389-y     

Second Law Analysis and Optimization of Elliptical Pin Fin Heat Sinks Using Firefly Algorithm

One of the most significant considerations in the design of a heat sink is thermal management due to increasing thermal flux and miniature in size. These heat sinks utilize plate or pin fins depending upon the required heat dissipation rate. They are designed to optimize overall performance. Elliptical pin fin heat sinks enhance heat transfer rates and reduce the pumping power. In this study, the Firefly Algorithm is implemented to optimize heat sinks with elliptical pin-fins. The pin-fins are arranged in an inline fashion. The natureinspired metaheuristic algorithm performs powerfully and efficiently in solving numerical global optimization problems. Based on mass, energy, and entropy balance, three models are developed for thermal resistance, hydraulic resistance, and entropy generation rate in the heat sink. The major axis is used as the characteristic length, and the maximum velocity is used as the reference velocity. The entropy generation rate comprises the combined effect of thermal resistance and pressure drop. The total EGR is minimized by utilizing the firefly algorithm. The optimization model utilizes analytical/empirical correlations for the heat transfer coefficients and friction factors. It is shown that both thermal resistance and pressure drop can be simultaneously optimized using this algorithm. It is demonstrated that the performance of FFA is much better than PPA.     

Multiply distorted image quality assessment based on feature level fusion and optimal feature selection

No reference image quality assessment (NR-IQA) has received considerable importance in the last decade due to a rise in the use of multimedia content in our daily lives. Due to limitations in technology, multiple distortions may be introduced in the images that need to be assessed. Recently feature selection has shown promising results for single distorted NR-IQA and their effectiveness on multiple distorted images still need to be addressed. In this paper, impact of feature level fusion and feature selection on multiple distorted image quality assessment is presented. To this end features are extracted from multiple distorted images using six NR-IQA techniques (BLIINDS-II, BRISQUE, CurveletQA, DIIVINE, GM-LOG, SSEQ) that extract features in different (discrete cosine transform, spatial, curvelet transform, wavelet transform, spatial and gradient, spatial and spectral) domains. The extracted features from different domains are fused to generate a single feature vector. All combinations of feature-level fusion from six different techniques have been evaluated. Three different feature selection algorithms (genetic search, linear forward search, particle swarm optimization) are then applied to select optimum features for NR-IQA. The selected features are then used by the support vector regression model to predict the quality score. The performance of the proposed methodology is evaluated for two multiple distorted IQA databases (LIVE multiple distorted image dataset (LIVEMD), multiply distorted image database (MDID2017)), two singly synthetically distorted IQA databases (Tampere image database (TID2013), Computational and subjective image quality database (CSIQ)), and one screen content IQA database (Screen content image quality database (SIQAD)). Experimental results show that the fusion of features from different domains gives better performance in comparison to existing multiple-distorted NR-IQA techniques with SROCC scores of 0.9555, 0.9587, 0.6892, 0.9452, and 0.7682 on the LIVEMD, MDID, TID2013, CSIQ, and SIQAD databases respectively. Moreover, the performance is further improved when the genetic search feature selection algorithm is applied to fused features to remove the redundant and irrelevant features. The SROCC scores are improved to 0.9691, 0.9723, and 0.6897 for LIVEMD, MDID, and TID2013 databases respectively.

     

Use of the Long-Term Pavement Performance Database in the Pavement Engineering Curriculum

In this paper the writers demonstrate the inclusion of the Long-Term Pavement Performance (LTPP-DATAPAVE 3.0) data within the pavement engineering curriculum at Michigan State University (MSU). The paper presents two examples, one from an undergraduate course on pavement rehabilitation and one from a graduate course on pavement analysis and design. These examples illustrate the use of LTPP data in computing responses, predicting traffic, developing rehabilitation strategies, and predicting performance of both rigid and flexible pavements.     

Influence of end-group modification on interaction of amphiphilic poly(oxyethylene)-<Emphasis Type="Italic">b</Emphasis>-poly(oxybutylene) block copolymers with ionic surfactants

Amphiphilic poly(oxyethylene)-b-poly(oxybutylene) block copolymers having hydrophilic block end-capped with neutral dimethylamino (DE₇₉B₃₄) and cationic trimethyl ammonium (TE₇₉B₃₄) groups, respectively were investigated for their interactions with ionic surfactants, sodium dodecyl sulfate (SDS) and cetyltrimethylammonium bromide (CTAB) using tensiometry, conductometry and dynamic light scattering. The self-assembly of DE₇₉B₃₄ and TE₇₉B₃₄ occur at 0.7 g/dm³ and 0.8 g/dm³, respectively. TE₇₉B₃₄ binds favorably with oppositely charged SDS, causing a substantial change in surface tension compared to other surfactant-polymer systems. In mixed polymer-surfactant systems, the micellization was promoted in the presence of SDS, but it was suppressed with CTAB. Such behavior is expected for cationic block copolymers, since they can engage into ion-pair formation with anionic SDS. For neutral polymers, the behavior is reflective of surfactants’ head group hydrophobicity. The head groups of CTAB are more hydrophobic and their existence in the proximity of hydrophobic segments of polymers induces shape transition to non-spherical morphologies. Gibb’s free energy of adsorption at air – water interface is negative for SDS, CTAB and surfactant-block copolymer systems, indicating that the process is highly spontaneous. The increase in entropy of TE₇₉B₃₄ during micelle formation with temperature is due to disturbance of hydrophobic structure of water molecules, thus hydrophobic parts are removed from bulk solution to the interface and also in the interior of micelle the freedom of hydrophobic part is increased. The dynamic laser light scattering results revealed that due to presence of block copolymers pre-micellar aggregates were favored.     

Design and development of ceramic-based composites with tailored properties for cutting tool inserts

A successful approach to the development of tailored cutting tool materials requires the development of innovative concepts at each step of manufacturing, from the material design, synthesis of composite powders, to their processing and sintering. In this paper, a computational design approach is applied in the development of reinforced ceramic-based cutting tool inserts with tailored structural and thermal properties. Several potential filler materials are considered at the material design stage for the improvement of structural and thermal properties of a selected matrix material. Properties, such as an improved thermal conductivity and reduced coefficient of thermal expansion are essential for an effective cutting tool insert to absorb thermal shock at varying temperatures. In addition, structural properties such as elastic modulus have to be maintained within a moderate range. A mean-field homogenization theory and effective medium approximation using an in-house code are applied for predicting potential optimum structural and thermal properties for the required application. This is done by considering the effect of inclusions as a function of volume fraction and particle size in the ceramic base matrix. Single inclusion composites such as alumina-silicon (Al₂O₃-SiC) and alumina-cubic boron nitride (Al₂O₃-cBN) as well as hybrid composite such as alumina-silicon-cubic boron nitride (Al₂O₃-SiC-cBN) are developed using the Spark Plasma Sintering (SPS) process in line with the designed range of filler size and volume fraction to validate the computational results. It is found that the computational material design approach is precise enough in predicting the target properties of a designed hybrid composite material for cutting tool inserts.     

LC-MS/MS-based determination of chloramphenicol,thiamphenicol, florfenicol and florfenicol amine in poultry meat from the Punjab-Pakistan

A simple, reliable and sensitive liquid chromatography-tandem mass spectrometry-based confirmatory method was redeveloped and validated for the simultaneous determination of chloramphenicol, thiamphenicol, florfenicol and florfenicol amine in chicken muscles. The analytes were extracted from minced chicken muscle with acetonitrile and ammoniated water mixture. A second extraction with ethyl acetate was followed by evaporation and dissolution of the residue in ammoniated methanol before defatting with n-hexane. Finally, the extract was further cleaned up by dispersive solid phase extraction using C-18 end-capped dispersive material. The validation protocol was adapted from the European Commission Decision 2002/657/EC and all the performance characteristics were successfully satisfied. The recoveries of all the analytes were found to be in the range of 86.4–108.1% and the precision values, within day and between days, ranged from 2.7% to 11% and 4.4% to 16.3%, respectively. The method was tested in various incurred samples and applied to analyse a wide range of random poultry market samples (n = 120) collected from three cities of the Punjab, Pakistan. Chloramphenicol and florfenicol residues were detected at low levels in less than 11% of the samples. Chloramphenicol was detected only in 4 samples with the concentration range of 0.17–0.477 µg kg^–1, whereas the levels of florfenicol/florfenicol amine residues detected in 9 samples ranged from 8.7 to 32.8 µg kg^–1. Moreover, most of the florfenicol residues were identified as tissue bound, extractable only after strong acid hydrolysis.     

Physicochemical characterization of Pommeau de Normandie: establishing a relationship between metal contents and turbidity of the Pommeau samples

Pommeau is a traditional beverage of France which is elaborated by mixing Calvados and clarified apple must. In this study, this beverage was physicochemically characterized for the first time by different analytical methods. Pommeau contains a mean content of ethanol around 16.3 % (v/v) while pH values are observed between 3.9 and 4.3. Total and volatile acidities are rather low if they are compared to other beverages, and this is correlated with relatively low quantities of malic acid (around 1.5 g/L) and high concentrations of reducing sugars (79–109 g/L). Alkali and alkaline earth metals (K, Na, Ca and Mg) are dominant in concentration as compared to the other metals. Pommeau is a sweet beverage which reflects that producers are selecting higher proportions of sweet apples and lower proportions of acidic apples. The concentration of metals may be correlated to the turbidities of Pommeau samples up to some extent. The statistical analyses have shown the association of metals with more turbid sample.