Iranian Journal of Materials Forming
Volume:8 Issue: 4, Autumn 2021

The “Iranian Journal of Materials Forming (IJMF)” is an international open access journal in the fields of materials deformation and forming processes, which was established at Shiraz University in 2014. The journal is pleased to receive papers from scientists and engineers from academic and industrial areas related to all manufacturing processes. In addition, all deformations, including the elastic and plastic behaviors of materials and deformations due to failure, are part of this journal’s field of interest. The quality and credibility of the journal have been ensured by appointing some of the most well-known professors in the world as members of its editorial board. Recently, some world-renowned scientists have also been joined the editorial board, making it stronger than before. In addition, the wide range of the selected referees in this issue is a sign of its scientific quality. It is a matter of pride that for the first year this journal has been successfully released quarterly and 24 articles were published in 2021.

This article investigates the repair process of damaged steel pipes of drinking water transmission lines by polyester resin or putty and a composite patch, as well as the internal pressure capacity of the repaired specimens during burst pressure tests carried out by the experimental method. The main aim is to introduce a completely practical method for repairing the pipelines in out-of-town areas without using electrical or other advanced equipment. In addition, an innovative novelty is employed in the burst pressure tests. Experiments demonstrate that the repaired pipes by the molded polyester putty and a composite patch of 100×100 mm2 dimension with 3 or 5 layers can approximately sustain the maximum internal pressures up to 44 or 55 bar, respectively; while the repaired pipes by the molded polyester resin and the corresponding composite patch with 3 or 5 layers can withstand the maximum pressures up to 28 or 40 bar, respectively. Repairing the pipes by the presented methods helps achieve the main purpose of the present research, with an admissible safety factor.

Texture evolution of commercially pure copper processed by multiple compressions in a channel die was investigated and interpreted by the use of continuity equation for the crystallographic orientation distribution function (ODF), which is considered as a kind of conservation law in the crystal orientation (Euler angle) space. The specimen has been subjected to multiple compressions in a channel die up to six passes, and its bulk texture after even passes of the process were measured by x-ray diffraction. Based on these experiments, it was found that during the multiple compressions in a channel die, Beta fiber strengthens at the beginning of the process and then weakens. The Cube component is observed in the texture of processed specimens, while Goss component is missing. Calculation of the texture index shows that the overall texture strength decreases during multiple compressions in a channel die. Deformation of each pass during the process is assumed to be analogous to that of flat rolling. After each pass of the process, the sample is rotated around two mutually perpendicular directions. The ODF of the processed sample during each pass of compression was predicted analytically as a function of the initial ODF and crystallographic rotation speed in the Euler angle space. Based on the predicted analytical function of ODF, the locations of the stable texture components and the evolution of texture can be estimated. After each pass of the process, as mentioned above, the specimen is rotated, therefore, the texture of the specimen with respect to this new sample frame should be considered as the initial texture for the subsequent pass of the process. Using the analytical function of ODF during each pass, texture evolution and stable texture components can be estimated during this new pass. Following the aforementioned stages, texture evolution during multiple compressions in a channel die was predicted, and a good agreement with experimentally determined texture evolution was obtained.

A laminar composite structure was developed in a CuZn alloy plate by non-equilibrium heat treatment and subsequent submerged friction stir processing. For this aim, Cu-37 wt.% Zn alloy was initially heat treated to produce a double phase structure. Then, the double phase plate was friction stir processed in underwater media at room temperature. The microstructure and mechanical properties of the samples were analyzed using optical microscopy and tensile test. During heat treatment, the large α grains containing annealing twins converted to a double phase structure with β grains on the α grain boundaries. Heat treatment caused an increase in ultimate tensile strength from 240 MPa to 275 MPa, and a reduction in elongation from 67 to 49%. After friction stir processing, the ultimate tensile strength and elongation were obtained as 380 MPa and 48%, respectively. This desirable mechanical property was achieved due to the formation of a novel composite structure containing parallel ultra-fine grained β layers between dynamically recrystallized α layers.

Resistance spot welding process of AISI 1060 steel has been experimentally investigated by studying the effects of welding current, electrode force, welding cycle and cooling cycle on tensile-shear strength. Using the response surface methodology, experimental tests are performed. An adaptive neural-fuzzy inference system is applied to model and predict the behavior of tensile-shear strength. Additionally, the optimal parameters of adaptive neural-fuzzy inference systems are obtained by the gray wolf optimization algorithm. For modeling the process behavior, the results of experiments have been employed for training (70% of data) and testing (30% of data) of the inference system. The results show that the applied network has been very successful in predicting the tensile-shear strength and the coefficient of determination and mean absolute percentage error for the test section data are 0.96 and 6.02%, respectively. This indicates the considerable accuracy of the employed model in the approximation of the desired outputs. After that, the effect of each input parameter on tensile-shear strength is quantitatively evaluated with the Sobol sensitivity analysis method. The results show that the tensile-shear strength of the joint rises by increasing the welding current and welding cycle and also decreasing the electrode force and cooling cycle.

This study investigates the effect of friction stir back extrusion (FSBE) input parameters such as traverse speed, rotational speed, and wire diameter on the mechanical and microstructural properties of the produced wire. Numerous experiments were performed with different input parameters, and the grain size, hardness, and ultimate pressure strength (UPS) of each of the produced wires were investigated. In addition, to better understand the effect of input parameters, the process was simulated using the finite element method (FEM) model, and the temperature, material flow, and strain distributions in the wires were investigated. Then, using the artificial neural network (ANN), a relationship was obtained between the input parameters of the process, such as traverse speed, rotational speed, and wire diameter, with the mechanical and microstructural properties of the produced wires. This relationship was then used in a hybrid multi-objective optimization to find the optimal process parameters. Due to the higher importance of UPS in comparison to the grain size and microhardness, the weighting of 0.6, 0.2, and 0.2 were used in the TOPSIS model, and the optimum input parameters were achieved as 6 mm, 36.35 mm/min, and 456 rpm, for the traverse speed, rotational speed, and wire diameter, respectively.

In this paper, by considering the temperature, time, and process pressure, as the most important factors in producing the thermoplastic composites, an experimental design was performed. An adaptive neuro-fuzzy inference system (ANFIS) was utilized to estimate the important characteristics containing flexural strength, porosity volume ratio, fiber volume ratio, and flexural modulus. Then, the parameters of the ANFIS network were optimized by the teaching-learning-based optimization (TLBO) algorithm. For the purpose of modeling material behavior in the process, the experimental results were utilized for the training and validation of the adaptive inference system. The accuracy of the obtained model has been investigated by using different graphs, based on the statistical criteria of the mean absolute error, correlation coefficient, mean square error, and the percentage of mean absolute error. Based on the obtained results, the TLBO-ANFIS approach has been very effective in estimating the above-mentioned properties in the production process. The network error for estimating flexural strength, porosity volume ratio, fiber volume ratio, and flexural modulus in the teaching section was equal to 0.159%, 0.0003%, 1.074%, and 0.0001%, and the corresponding values were equal to 0.852%, 42.413%, 33.95%, and 4.894% in the testing section.