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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.
Chief Editor, Professor Filippo Berto, is International Chair of Fatigue and Fracture at the Norwegian University of Science and Technology, Norway. His research focusses on the applications of structural integrity.
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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.
Gear Tooth Root Bending Strength Estimation under the Assumption of Fatigue Limit Existence
Being able to properly predict gear failure is a key aspect to achieve a reliable light-weight gearbox. Among the several gear failures, tooth root bending fatigue is considered as the most dangerous one because it implies the stoppage of the whole gearbox. In order to characterize a gear for this phenomena, Single Tooth Bending Fatigue (STBF) tests are the most performed ones. However, as in STBF test THERE IS no sliding/rolling contact and as the specimens are teeth rather than gears, some differences occur between the test conditions and those of the real case. This paper deals with the statistical ones that is the estimation of the gear SN curve starting from the teeth one. The teeth SN curve has been estimated by means of a statistical model developed considering Murakami’s idea of nonpropagating crack. Then, a methodology based on statistic of extreme is adopted for the purpose of estimating the gear SN curve.
Maximum Inclusion Size Evaluation and Fatigue Strength Analysis of 40Cr Structural Steel
Statistics of extreme values (SEV) and generalized Pareto distribution (GPD) are adopted to predict the maximum inclusion size in 40Cr structural steel, and the fatigue strength was estimated according to the obtained maximum inclusion size. The estimated results were compared with the experimental results obtained in rotating bending fatigue testing, where all failure-relevant inclusions of the present study were quantitatively analyzed with respect to (square root of the projected inclusion area). Both the estimation results are consistent with the experimental results. Furthermore, a suitable maximum inclusion size equal to the prior austenite grain size is proposed for the material manufacturing process.