International Journal of Engineering
Volume:35 Issue: 6, Jun 2022

Sustainability of supply chain risk management is one of the main competitive advantages of every organization for long-standing. There are several models in the research literature to manage sustainability risks of the supply chain. Considering that critical risks have the highest impact and have the largest share of risk management resources, they need to be identified using special techniques to make risk management more accurate and more reliable. In this paper, a new three-phase model is presented to supply chain sustainability risks management. This model includes the failure mode and effects analysis phase for identifying and assessing all risks and classification them, fuzzy VIKOR phase for ranking critical risks, and management phase to deal with critical risks. The categorization of risks was conducted according to a new five-dimensional approach to sustainable progress, including environmental, economic, social, technical, and organizational aspects on various sectors of the supply chain. The telecommunication industry of Iran is considered to show the model performance. The results indicated that consideration of the fuzzy VIKOR phase is necessary in order to accurately assess critical risks because of the priority of critical risks is not correctly identified through Failure mode and effects analysis due to the shortcomings of this method and cause errors. It was also found that the technical risks initiated by the organization are the most dangerous risk that threatens the sustainability of the supply chain.

Since load time series are very changeable, demand forecasting of the short-term load is challenging based on hourly, daily, weekly, and monthly load forecast demand. As a result, the Turkish Electricity Transmission Company (TEA) load forecasting is proposed in this paper using artificial neural networks (ANN) and fuzzy logic (FL). Load forecasting enables utilities to purchase and generate electricity, load shift, and build infrastructure. A load forecast was classified into three sorts (hourly, weekly and monthly). Over time, forecasting power loads with artificial neural networks and fuzzy logic reveals a massive decrease in ANN and a progressive increase in FL from 24 to 168 hours. As illustrated, fuzzy logic and artificial neural networks outperform regression algorithms. This study has the highest growth and means absolute percentage error (MAPE) rates compared to FL and ANN. Although regression has the highest prediction growth rate, it is less precise than FL and ANN due to their lower MAPE percentage. Artificial Neural Networks and Fuzzy Logic are emerging technologies capable of forecasting and mitigating demand volatility. Future research can forecast various Turkish states using the same approach.

Hollow concrete blocks (HCBs) are substitutes for conventional bricks and stones in building construction. Experimental testing has been performed on the feasibility of producing HCBs from sawdust (SD). Sawdust is substituted with fine aggregate (pumice) by weight proportions 3, 6, and 9%. Sawdust is treated with tap water to remove foreign materials. Different tests were performed on blocks in order to find the effect of sawdust and to confirm whether blocks produced will satisfy the minimum acceptable standards. Different tests were conducted on the samples for 21 days as in Ethiopia to reflect the pratical application. Compressive strength of sawdust with additions of 3, 6, and 9% was 1.17, 0.99, and 0.51 N/mm2, respectively. Replacement with 9% resulted in a higher rate of water absorption. The density of HCBs was found to be between 633.06 and 638.21 kg/m3. In light of the results, it is concluded that 3% of sawdust can be optimized for the production of blocks.

Batter piles are the piles driven in the soil at an inclination with the vertical to withstand oblique loads or large horizontal loads and have been widely used to support high buildings, offshore buildings, and bridges. These constructions are risky because of the exposure to moments and overturning resulting from winds, waves, and ship impact. A 3D FEA using PLAXIS 3D software was used to investigate the effect of several variables that affect the behavior of single batter piles under pull-out loads. The study is achieved on a steel pipe pile model embedded in a dry sandy soil with three relative densities (loose, medium, and dense) at different inclination angles and three embedment ratios, L/D of 25, 37.5, and 50, respectively. The numerical results showed that the ultimate pull-out resistance of the battered pile raise as the battered angle increases reaches a maximum value, then decreases. The ultimate pull-out load capacity of a single battered pile is directly proportional to the slenderness ratio and relative density; the ultimate pull-out load increases with the increase in the ratio of slenderness and relative density.  The ultimate uplift load of the battered pile was less affected by the free-standing length. Vertical and battered piles at a battered angle of (10ᵒ and 20ᵒ) and free-standing lengths equal to zero have higher ultimate pull-out capacity; by increasing the free-standing length, the ultimate pull-out capacity decreased.

In the micro/nanomaterial reinforced composites, interfacial interactions at the interface of filler/polymer lead to the formation of a third layer called interphase as the secondary reinforcing mechanism. The interphase may be formed due to local adsorption of polymer chains at the interface, mechanical interlocking, and interdiffusion of polymer chains. Since the interactions govern the load transfer at the filler/polymer interface, they play a key role in the mechanical response of reinforced composites. However, there exist only a few well-established and validated studies in the description of the interfacial interactions presented in thermosetting composites. This research aims at the understanding of correlations amongst the mechanical properties of thermosetting polyester composites reinforced with 0-15 wt. % of carbon black (CB) focusing on the nano-size cooperative rearranging region (CRR). To estimate the length of CRR, thermal analysis of the variations in the specific heat capacity or the relaxation strength within the glass transition temperature (Tg) range was measured using a thermodynamic model. A nano-size CRR of 10 nm on average was estimated and correlated to the enhanced impact and toughness behavior of the specimens. The results suggested the presence of softer interphase based on the Tg values influenced by the CBs agglomeration level and cross-linking density, which in turn governs the mechanical response of the composites. The methodology introduced in this study can be used in the explanation of changes in mechanical and physical properties of reinforced composites with a focus on the underlying role of nano-size interfacial interactions.

One of the most important challenges of the Friction Stir Spot Welding (FSSW) process is the appearance of a void in the welded parts. This causes the stress to be stacked against the created void, and as a result, the mechanical properties would be reduced. To solve this problem in this research, the aluminum and polyethylene sheets are joined by means of H13 steel tools, protruding fixtures, and also three types of nanoparticles. Appending three types of Nano-particles, namely Al2O3, TiO2, SiO2, the constituent materials of Al 5083 and high-density polyethylene sheets have been prepared. To improve the mechanical properties of the welded samples, these three types of Nano-materials are integrated to the Stir Zone (SZ). In order to find the maximum strength of welded composite plates, the Design of Experiment (DOE) is performed using the Taguchi method. The Rotation Speed, Dwell Time, Tool d/ Protrusion d besides the type and percentage of Nano-material are chosen as input parameters. The maximum fracture force and the maximum strength are respectively as 2249 N and 4.13 MPa. Without using nanoparticles, a rupture is occurred in the tensile tests of polyethylene samples. Thus, the polyethylene samples capture more sediment by addition of nanoparticles, and the nanoparticles’ deposition improves the mechanical properties of the Al/PE composite. Compared to the base material of pure aluminum and polyethylene, a nearly eightfold increment of the mechanical properties of the Al/PE composite sample is observed by addition of nanoparticles in the welding nugget. According to the S/N ratio analysis, the rotation speed of 2500 rpm, dwell time of 12 s, tool d/ protrusion d of 3 mm, Nano-material’s type of Sio2 and percentage of 10% are considered as the optimum states.

Knowledge transfer can occur on two levels: intra-organizational and inter-organizational. Acquiring knowledge from outside an organization usually requires significant budget and considerable time. However, through awareness and reliance on knowledge already acquired by the personnel, and creating a knowledge flow network, knowledge level of the organization can be increased in the shortest possible time. The present paper addresses the design of a knowledge flow network between the personnel of an organization according to the professional and personal trust levels, teaching and learning capabilities, knowledge level of the personnel, organizational commitment level, type and importance of each knowledge, and the stochastic nature of the knowledge transfer duration. This problem was formulated as a stochastic multi-objective mixed-integer programming. The objectives of the proposed model were maximizing the knowledge level and minimizing the knowledge transfer time. The model was solved using the Lagrangian relaxation algorithm and the CPLEX solver. Results indicate the high efficiency of the Lagrangian relaxation algorithm specially in computational time of large-sized problems. Moreover, the results show that the organizational commitment parameter has more significant influence on the knowledge transfer duration, followed by teaching and learning capabilities.

Due to its ability to remove material quickly while maintaining optimum surface quality, end milling is considered one of the most frequent metal cutting procedures in industry. The present study aimed to investigate the impacts of cutting parameters and tool geometry on milling of Aluminum Alloy 6061-T6 to examine the impact surface roughness by utilizing response surface methodology (RSM).  RSM was used to create a second-order mathematical model of surface roughness for this purpose. A multiple regression analysis used the analysis of variance to demonstrate the effect of machining settings on surface roughness and determine experiment performance. The trials for optimizing surface roughness were set up utilizing the central composite design (CCD) method and various cutting parameters such as spindle speed, feed rate and depth of cut. Also the parameters used in tool geometry are the radial rake angle (10, 13, 16, 19 and 22 degrees), and nose radius (0, 0.2, 0.4, 0.6 and 0.8 mm). The result shows that the nose radius has more significant effect on the surface roughness followed by the radial rake angle. Moreover, the effect of the depth of cut on surface roughness is more dominant than cutting speed. The optimum combinations of cutting and tool geometry parameters were cutting speed (60.53 m/min), feed rate (0.025 mm/tooth), depth of cut (0.84 mm), radial rake angle (12.72 degree) and nose radius (0.34 mm).

During the oil production, the occurrence of such a complication as the formation of wax deposits is not uncommon. The fight against these deposits, as well as the development of modern methods of dealing with them, is one of the most important tasks of the subsoil user. Many modern methods of modeling deposits require the exact determination of such a quantity as the thermal conductivity of organic deposits. Based on the analysis of scientific literature, it can be concluded that there is no method developed for estimating this value under conditions of wax formation without affecting their pore structure. The paper describes a method for determining this value based on the results of a study of the process of formation of organic deposits on the laboratory installation "Wax Flow Loop" based on the laws of heat and mass transfer. Based on the results of applying this technique, it becomes possible to determine the thermal conductivity of organic deposits, the value of which correlates with the values given in the reference and scientific literature. In addition, the presence of a correlation between the value of the thermal conductivity of deposits and the component composition of the studied fluid was determined. The application of the described technique will make it possible to most accurately simulate the processes of oil production and determine the technological effectiveness of the use of modern methods of combating organic deposits.

α-Fe2O3 is a stable, cheap, and non-toxic metal oxide with many advantages and different fields of application. Many attempts have been devoted to the synthesis of α-Fe2O3 with different crystal structures and morphologies to obtain the desired properties. In this research, nanostructured α-Fe2O3 were synthesized by a facile solvothermal route. The as-obtained samples are characterized by XRD, FESEM, EDS, FTIR, and BET surface area analysis. The results showed that the as-synthesized hematite consists of nanostructures with the morphology of distorted microspheres with an average diameter in the range of 1 to 1.5 µm each composed of self-assembled nanoparticles with an average size in the range of 10 to 30 nm. The results showed that the hematite nanostructures had a specific surface area of 41.86 m2g-1. The influence of temperature and duration of the solvothermal process as well as, calcination on the structural properties of the α-Fe2O3 samples was investigated. The results reveal that the crystallite size of the samples increases with increasing the temperature and duration of solvothermal treatment. Moreover, calcination leads to an increase in the crystallite size of the samples. The α-Fe2O3 nanostructures with a minimum crystallite size of 13.6 nm were synthesized at 150 °C for 4 h while the largest crystallite size of 75.4 nm was obtained at 180 °C and 8 h with subsequent calcination of the sample at 500 °C for 1 h. The results of the present study can be useful to enhance the properties of α-Fe2O3 nanostructures in various fields of application.

A self-excited oscillating jet can be naturally produced by discharging a plane jet into a rectangular cavity due to pressure effects and without a need for external aid. In recent years, the self-oscillatory jet in non-isothermal conditions has attracted research interests because of its wide range of industrial applications. Therefore, the current study aimed to compare the oscillatory behavior of downward vertical self-excited jet with Reynolds number (Re) 1000 and 3000 under various temperature differences (0, 100, and 300 K) between inletflow and cavity’s wall. Computational solutions were obtained using unsteady Reynolds averaged Navier-Stokes (URANS) and energy equations for an incompressible flow. The numerical simulation was carried out by the finite-volume based tool OpenFOAM code. The results showed that depending on the value of temperature difference, oscillatory and non-oscillatory flows were observed. Also, at Re=3000, the temperature differences can change oscillation frequency up to 10% compared to isothermal conditions. This value reaches 58% at Re=1000. The results indicated that where the Archimedes number is less than 0.1, the effects of temperature differences between jet and cavity walls on the oscillating behavior are negligible.

Electric vehicle is an adaptation of conventional vehicle, with an integration of electrical motors. It seems to be one of the most promising technologies that can lead to significant improvements in vehicle performance and polluting emissions. However, for any vehicle in urban traffic requires regime changes, frequent acceleration, deceleration, and stopping phases, which lead to serious breakdowns. During the above phases, electric motors are continuously being exposed to thermal and mechanical effects.This paper highlights the possibility of representing the wheel load torque emulation of an electric propulsion structure using dual induction motors vector-controlled. The emulation of load torque acting on one of both electric motors placed at the rear wheels of electric vehicle (EV) structure is accomplished by a DC-generator coupled with an induction motor during vehicle drive cycle operation andunpredictable load profiles. Simulation results confirm widely the feasibility and the effectiveness of the proposed emulator scheme of induction motor-based vector-control in the electric vehicle application.

The purpose of this paper is to investigate the effective factors in accepting marketing through mobile social media by end users. The research method, is descriptive-survey and the research type is practical based on the research purpose. The statistical population of this study is the online customers of mobile phones from the Mobile Online website in 2019. Infinite population (384) was selected as statistical sample according to Cochran formula. Data collection was accomplished through questionnaire which involves 25 questions. The reliability of the questionnaire using Cronbach's alpha coefficient was 87%, which indicates an appropriate reliability. The results showed the importance of the effective factor of profitability on the attitude towards mobile marketing and the orientation towards mobile marketing on mobile marketing activities is significant.