In the current study, the effect of applied load, sliding speed, and type and weight percentages of reinforcements on the wear properties of ultrahigh molecular weight polyethylene (UHMWPE) was theoretically studied. The extensive experimental results were taken from literature and modeled with artificial neural network (ANN). The feed forward (FF) back-propagation (BP) neural network (NN) was used to predict the dry sliding wear behavior of UHMWPE composites. Eleven input vectors were used in the construction of the proposed NN. The carbon nanotube (CNT), carbon fiber (CF), graphene oxide (GO), and wollastonite additives are the main input parameters and the volume loss is the output parameter for the developed NN. It was observed that the sliding speed and applied load have a stronger effect on the volume loss of UHMWPE composites in comparison to other input parameters. The proper condition for achieving the desired wear behaviors of UHMWPE by tailoring the weight percentage and reinforcement particle size and composition was presented. The proposed NN model and the derived explicit form of mathematical formulation show good agreement with test results and can be used to predict the volume loss of UHMWPE composites.

In order to investigate the structure of welds, austenitic stainless steel (SS) studs with a diameter of 6 mm were welded to austenitic SS plates with a thickness of 5 mm using an arc stud welding (ASW) method. The effects of the welding current, welding time, and tip volume of the stud on the microstructure and ultimate tensile strength (UTS) of the welded samples were investigated in detail. The formation of δ-ferrites was detected in the weld zone because of the higher heat generated during the welding process. Higher welding current and time adversely affected the stud and significantly reduced the UTS of the samples. The UTS of the joints was also estimated using artificial neural network (ANN) and Taguchi approaches. The mathematical formulations for these two approaches were given in explicit form. Experimental results showed that the neural network results are more consistent with experimental results than those of the Taguchi method. Overall, it can be concluded that in order to achieve good welding joints and high strength values, ASW parameters should be investigated properly to determine the optimum conditions for each metal.

In this paper, the effect of different alloying elements on the ultimate tensile strength of Al-Mg₂Si composites is theoretically studied. The feed forward back propagation neural network with sigmoid function is used. The extensive experimental results taken from literature are modeled and mathematical formula is presented in explicit form. In addition, it is observed that magnesium and copper have a stronger effect on the ultimate tensile strength of Al-Mg₂Si composites comparison to other alloying elements. The proposed model shows good agreement with test results and can be used to find the ultimate tensile strength of Al-Mg₂Si composites.

The multi-walled carbon nanotubes (MWCNTs) have drawn great attention due to their exceptional mechanical, physical, thermal and electrical properties. The MWCNTs as the reinforcements significantly improved the properties of materials. However, the major challenges in composites containing CNTs are the poor wettability and poor interfacial bonding between matrix and CNTs. In this study, the used MWCNTs have a diameter of 8–10 nm and 1.5 μm in length. MWCNTs are purified in HNO₃: H₂SO₄, sensitized in Sn solution and activated in Pd solution at 90 °C, and coated with the Nickel and Cobalt elements using an electroless coating method. The holding time in the bath is 15, 30 and 60 min, and the bath concentration is also changed. The coatings are characterized by Scanning Electron Microscopy (SEM) equipped with electron dispersive spectrum (EDS), elemental mapping, x-ray diffraction (XRD), Raman spectroscopy, Transmission Electron Microscopy (TEM) and Fourier Transform Infrared (FTIR) spectrometer. The results showed that the Ni and Co coating layers are successfully formed on the surface of MWCNTs. The deposition rate is affected by the holding time and the bath concentration. The optimal results are obtained at the holding time of 60 min in the C concentration sample.

In this study, the feed-forward (FF) neural networks (NNs) with back-propagation (BP) learning algorithm is used to estimate the ultimate tensile strength of unrefined Al-Zn-Mg-Cu alloys and refined the alloys by Al-5Ti-1B and Al-5Zr master alloys. The obtained mathematical formula is presented in great detail. The designed NN model shows good agreement with test results and can be used to predict the ultimate tensile strength of the alloys. Additionally, the effects of scandium (Sc) and carbon (C) rates are investigated by using the proposed equation. It was observed that the tensile properties of Al-Zn-Mg-Cu alloys improved with the addition of 0.5 Sc and 0.01 C wt.%.