Volume 21 (2021): Issue 3 (September 2021)

A significant number of investigations have been reported on the elaboration and characterization of Polymer/Clays composites, via different methods. In our work, new composites materials were successfully prepared by in-situ polymerization of 4-vinylpyridine (4VP), in presence of two different types of Algerian modified clays (Maghnia and Mostaganem), noted (BC) and (MC), respectively. Different percentage clays (1 wt%, 3 wt% and 5 wt%) have been used. The differential scanning calorimetry analysis reveals the variation of glass transition temperature (Tg) of the copolymer in the composite materials. We show a decrease glass transition temperature (Tg) from 147°C to 131°C for P4VP-BC and from 147°C to 124°C for P4VP-MC according to the increase percentage of clays. Thermogravimetric analysis (TGA) shows good stability of composite materials at high temperature. Fourier Transformed Infrared (FTIR), Scanning Electron Microscopy coupled with Energy dispersive X-Ray Spectroscopy (SEM-EDX) and ¹H NMR spectroscopy are used to show the presence of the clays in the materials.

This research aimed to study the induction in-situ heated hybrid friction stir welding (IAFSW) method to join AA5052 aluminium alloy with X12Cr13 stainless steel (SS) to enhance joint strength. The potency of this method on the mechanical properties and microstructural characterizations were also investigated. The results show that the transverse tensile strength gained was 94% of the AA5052 base metal that is 229.5 MPa. This superior strength was achieved due to the annealing that happened to the AA 5052 region and elevated plastic flow in the weld zone by the in-situ induction heating, which resulted in the elongation of the weld region. The microstructure characterization indicates that a refined grain structure was gained in the nugget zone without defects.

This paper presents a method of determining the coefficient of friction in metal forming using multilayer perceptron based on experimental data obtained from the pin-on-disk tribometer. As test material, deep-drawing quality DC01, DC03 and DC05 steel sheets were used. The experimental results show that the coefficient of friction depends on the measured angle from the rolling direction and corresponds to the surface topography. The number of input variables of the artificial neural network was optimized using genetic algorithms. In this process, surface parameters of the sheet, sheet material parameters, friction conditions and pressure force were used as input parameters to train the artificial neural network. Some of the obtained results have pointed out that genetic algorithm can successfully be applied to optimize the training set. The trained multilayer perceptron predicted the value of the friction coefficient for the DC04 sheet. It was found that the tested steel sheet exhibits anisotropic tribological properties. The highest values of the coefficient of friction under dry friction conditions were registered for sheet DC05, which had the lowest value of the yield stress. Prediction results of coefficient of friction by multilayer perceptron were in qualitative and quantitative agreement with the experimental ones.

This research mainly concentrates on eco-friendly construction material. Production of cement and concrete industries release huge amount of carbon dioxide (CO₂) and greenhouse gases which affect the environment and also there is a demand in construction material by man-made or nature. The construction sector finds an economic and eco-friendly cement replacement material to achieve the demand for green concrete that improve the energy conservation and better energy saving material. In marine Bio-refinery waste produce huge quantity of calcium carbonate, whose disposal is cause of major concern. Pre-eminent solution for this problem is utilizing the marine shell waste in cement and concrete. It revises the manufacturing process to reduce the raw material usage in production and adoptable material for global warming. Therefore, the researchers focus on marine waste sea shells as the replacement material in construction industry to save the energy and also give sustainable green material. As per the previous studies by the researchers to determine the chemical composition, specific gravity, water absorption, particle size distribution of seashells and also compressive, flexural and tensile strength of concrete. It shows the seashell is filler material that slightly increases the strength when compared to the conventional materials and therefore the sea shells are suitable for the construction field to manufacture the cement and concrete with eco-friendly manner.

The article presents the results of experimental studies determining the influence of the type of adhesive on the static strength properties of the Glass Fiber Reinforced Polymer (GFRP) composite joint determined on the basis of the T-peel test. As part of the static tests on peeling joints, a comparison of peak load and stiffness for individual joints was made. The fracture surfaces were also analyzed, showing various failure mechanisms. It was shown that the variant of joints made with the Enguard BP72A polyester adhesive was characterized by the highest strength properties with a mean peak load of 836.73 N.

Dual-phase duplex stainless steel (DSS) has shown outstanding strength. Joining DSS alloy is challenging due to the formation of embrittling precipitates and metallurgical changes during the welding process. Generally, the quality of a weld joint is strongly influenced by the welding conditions. Mathematical models were developed to achieve high-quality welds and predict the ideal bead geometry to achieve optimal mechanical properties. Artificial neural networks are computational models used to address complex nonlinear relationships between input and output variables. It is one of the powerful modeling techniques, based on a statistical approach, presently practiced in engineering for complex relationships that are difficult to explain with physical models. For this study robotic GMAW welding process manufactured the duplex stainless steel welds at different welding conditions. Two tensile specimens were manufactured from each welded plate, resulting in 14 tensile specimens. This research focuses on predicting the yield strength, tensile stress, elongation, and fracture location of duplex stainless steel SAF 2205 welds using back-propagation neural networks. The predicted values of tensile strength were later on compared with experimental values obtained through the tensile test. The results indicate <2% of error between observed and predicted values of mechanical properties when using the neural network model. In addition, it was observed that the tensile strength values of the welds were higher than the base metal and that this increased when increasing the arc current. The welds’ yield strength and elongation values are lower than the base metal by 6%, ~ 9.75%, respectively. The yield strength and elongation decrease might be due to microstructural changes when arc energy increases during the welding.