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Material Design & Processing Communications seeks to spread and promote materials research advancing the understanding and applicability of novel design methodologies, production technologies, and failure prediction models technologies.
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Chief Editor, Professor Filippo Berto, is based at Sapienza University of Rome, Italy. His research focusses on the applications of structural integrity.
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Latest Articles
More articlesPorous Titanium Scaffold: A New Design for Controlled Drug Delivery
Gelatin crosslinking using conventional methods is usually associated with some toxic side effects. In this research, therefore, the vacuum heating method at 10 Pascal and 140°C under different times of 8, 16, and 32 h was used to cross-link strontium-loaded gelatin microparticles with varying degrees obtained by the oil/water mixing method on titanium scaffolds by the dip-coating method to avoid toxicity and also to control the strontium release rate to the surrounding tissue. The possible phases formed on the surface of the porous titanium scaffolds, the gelatin microparticle distribution, gelatin strontium loading, and strontium release were characterized using thin film X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy (SEM), and inductively coupled plasma-mass spectrometer (ICP-MS) machines, respectively. The results indicated that at 600°C, the rutile phase was formed on the surface of the heat-treated titanium scaffolds. Furthermore, strontium was successfully loaded in the spherical gelatin microparticles, and the strontium-loaded gelatin microparticles were distributed uniformly on the surface of the titanium scaffolds, while the rate of the in vitro strontium release decreased by increasing the time of the gelatin microparticle vacuum-heat crosslinking, whereas at the burst release step, the in vitro strontium release rates were around 5, 4.4, and 2.5 ppm/h, for the 8, 16, and 32 h vacuum-heat cross-linked gelatin microparticles, respectively.
Characterization of Refractory Alloys Produced by Laser Additive Manufacturing
Refractory alloys often possess superior thermomechanical properties compared to conventional materials, such as steels, Ni-based superalloys, and Ti alloys, especially in high-temperature environments. While these materials promise to revolutionize numerous industries, significant hurdles remain for insertion into applications due to an incomplete understanding of structure-property relationships and conventional processing challenges. We explore laser-based additive manufacturing (AM) to construct refractory alloys consisting of combinations of Mo, Nb, Ta, and Ti with systematically increasing compositional complexity. Microstructure, composition, and hardness of the AM-processed alloys were characterized. Results are discussed in the context of pairing additive manufacturing with refractory metals to enable next-generation alloys.
Investigating the Effect of Cooling Media on Hardness, Toughness, Coefficient of Friction, and Wear Rate of Mild Steel Heat Treated at Different Temperatures
Mild steel is a common material used extensively in the manufacturing industry. This manuscript investigates the effect of cooling processes on the hardness, toughness, coefficient of friction, and wear rate of mild steel heat treated at different temperatures. The material was heat treated in a furnace at two different temperatures (500 and 900°C) and cooled by water, oil, and air. Microhardness and impact tests were conducted using ASTM E384 and ASTM E23-12C. For dry conditions, the tribology ASTM G99 test standard was used to determine the coefficient of friction and wear rate per sample. The results show that mild steel heat treated at 900°C and cooled with water increased the material’s hardness by 24% and toughness by 23.3% as compared to oil- and air-cooling media. The same heating temperature and water-cooling media produce the material with a low wear rate (3.223E-008).
Design of Mechanical Properties of Poly(butylene-adipate-terephthalate) Reinforced with Zein-TiO2 Complex
Mechanical properties of polymer biocomposites are influenced by the interaction between the matrix and the filler surface. In this work, composites based on poly(butylene-adipate-terephthalate) (PBAT) filled with micrometric particles of zein-TiO2 complex (ZTC) were realized via solvent casting technique at different concentrations, equal to 0, 5, 10, and 20 wt%. After pelletization, the resulting materials were injection molded into standard specimens, employed for the uniaxial tensile test (UTT) characterization. From the stress-strain curves, Young’s modulus (), yield stress (), stress at break (), elongation at break (), and toughness () were collected. The addition of the ZTC proved to show a reinforcing effect on the polymeric matrix, with an increase in both and . Modelling of the mechanical properties was performed by applying Kerner’s and Pukánszky’s equations. Kerner’s model, applied on experimental values, returned a very good correspondence between collected and theoretical values. From the application of Pukánszky’s model to , the obtained value showed a good interfacial interaction between the matrix and the filler. Due to the enhanced stiffness of the composites, a reduction in the true stress at break () was observed. The modified Pukánszky’s model gave a value lower than the one obtained for the yield, but still in the range of acceptable values for microcomposites.
Investigating Static Deflection of Non-Prismatic Axially Functionally Graded Beam
In this study, the static deflection of non-prismatic axial function graded tapered beam (A-FGB) under distribution load has been analyzed using ANSYS workbench (17.2). According to a power-law model, the elastic modulus of the beam varies continuously in the axial direction of the beam. Also, the beam’s geometry, i.e., width, thickness, or both width and thickness of the beam, varies linearly in the axial direction with different values of non-uniformity parameter (1, 0.5,0, −0.5, and −0.75). The effects of martial distribution, i.e., power-law index, and non-uniformity parameter on the static deflection for A-FGB with different boundary conditions, in such free-clamped, clamped-free, and simply-supported, are studied. This research deals with functionally graded materials FGMs in more than one aspect in terms of using different boundary conditions; in addition, it studies the response of the non-prismatic beam non-uniformity parameter (α); therefore, this research studies comprehensively the deflection of the beam. The results show that the increase in power-law index causes decreasing in dimensionless deflection and its rate of change depends on the supporting types of the beam and non-uniformity parameters. The variation in both width and thickness for a free-clamped axial function–graded beam gives a significant decrease in dimensionless deflection at decreasing in non-uniformity parameter, whereas the variation in thickness for clamped-free axial function graded beam gives a significant decrease in dimensionless deflection at decreasing of non-uniformity parameter.
A Survey of Machine Learning in Friction Stir Welding, including Unresolved Issues and Future Research Directions
Friction stir welding is a method used to weld together materials considered challenging by fusion welding. FSW is primarily a solid phase method that has been proven efficient due to its ability to manufacture low-cost, low-distortion welds. The quality of weld and stresses can be determined by calculating the amount of heat transferred. Recently, many researchers have developed algorithms to optimize manufacturing techniques. These machine learning techniques have been applied to FSW, which allows it to predict the defect before its occurrence. ML methods such as the adaptive neurofuzzy interference system, regression model, support vector machine, and artificial neural networks were studied to predict the error percentage for the friction stir welding technique. This article examines machine learning applications in FSW by utilizing an artificial neural network (ANN) to control fracture failure and a convolutional neural network (CNN) to detect faults. The ultimate tensile strength is predicted using a regression and classification model, a decision tree model, a support vector machine for defecting classification, and Gaussian process regression (UTS). Machine learning implementation mainly promotes uniformity in the process and precision and maximally averts human error and involvement.