High-strength steel plays an important role in engineering fields such as infrastructure. For this reason, an accurate determination of its mechanical properties is of critical importance. Considering the inconvenience of conventional mechanical extensometers for the deformation measurement of small-scale specimens, 3D digital image correlation (3D-DIC) was used to measure the deformation of Grade 8.8 bolts and Q690 high-strength steel specimens by means of a uniaxial tensile test, and in this way, stress–strain curves, elastic modulus, yield strength, tensile strength, percentage elongation after fracture, and percentage reduction of area were obtained. Experimental results show that Grade 8.8 bolts and Q690 steel result in higher yield strength and tensile strength than common steel. Moreover, owing to the phenomenon that stress remains constant with strain increase in the yielding stage, the evolution process from elastic deformation to plastic deformation of the specimens during the yielding stage could be studied. Experimental results show that the axial strain of Grade 8.8 bolts increases from 0.3 to 1 % during the yielding stage and for Q690 specimens the corresponding strain increases from 0.4 to 1.8 %.
Lightweight pressure vessels of type IV for hydrogen storage consist of a thermoplastic inner liner, commonly from polyethylene or polyamide. The liner is the permeation barrier against the compressed gas and must prevent the formation of cracks, also after temperature changes, for example after refueling processes. In the present work high-density polyethylene, cross-linked polyethylene, polyamide 6 and polyamide 12 were characterized by tensile tests, single notch impact tests and permeations measurements before and after a cyclic thermal aging process. The aging only lead to slight changes of mechanical properties due to post-crystallization, but to a significant decrease of permeation properties. This decrease was contributed to weakened, amorphous regions where chain splitting occurred. Considerable differences in properties resulted from different peroxide cross-linking times of polyethylene at the same temperature. A longer holding time at 200 °C led to an improvement in impact strength by a factor of more than three. However, the permeation properties decreased by about 50 %, indicating that peroxide cross-linking in the melt inhibited the formation of crystalline regions.
Specimens of P/M 316 L stainless steel and modified P/M 316 L stainless steel with various amounts of Cr, Ni, and Cr along with Ni additions were compressed by a 500 MPa-hydraulic press for a duration of 30 s and then sintered at 1573 K in a hydrogen atmosphere for 2.7 ks. The oxidation resistance tests of those specimens were carried out at 1173 K in air for 360 ks. The 5 wt.-% Cr-added specimen resulted in the best oxidation resistance with the lowest oxidation rate constant of 1.54 × 10 -7 kg 2 × m -4 × s -1 . The oxidation products which formed inside the pores and on the surfaces of all specimens were analyzed using scanning electron microscope/energy dispersive spectroscopic (SEM/EDS) and X-ray diffraction (XRD) analysis techniques. The results show that the oxides forming on both inside pores and surfaces were identified as Cr 2 O 3 , Fe 2 O 3 , (Fe 0.6 Cr 0.4 ) 2 O 3 , NiFe 2 O 4 and NiCr 2 O 4 in all tested conditions. The measured hardness of the modified specimens was in the range of 87-92 HRB, compared to the hardness of the 316 L specimen, which was 93 HRB. Increasing the amount of powder added provided a slightly lower hardness value due to increased porosity.
Mechanical testing/Analysis of physical properties
In this study, the mechanical properties of plain woven jute-epoxy composite materials were investigated after filling graphene nanoplatelets (GNPs) in different proportions. The time dependent changes in the viscosity and temperature of unfilled epoxy resin, with 0.5, 1 and 2 wt.-% graphene nanoplatelets filled epoxy resins were observed. Woven jute reinforced unfilled, 0.5 wt.-% GNPs filled and 1 wt.-% GNPs filled epoxy composite plates were produced by using vacuum assisted resin transfer molding (VARTM) at the same waiting and processing times. Specimens were prepared and subjected to tensile and flexural tests according to ASTM D 3039 and ASTM D 790 standards, respectively. Images were taken and evaluated with a scanning electron microscope (SEM) in areas where tensile damage occurred. It was observed that the gap amount between the fiber and the matrix increased and the interface adhesion decreased as the fill amount increased in the composites produced. The testing results indicated that the tensile and flexural properties of composites decreased at 0.5 wt.-% and 1 wt.-% during the GNPs loading as compared to unfilled composites.
This article provides the fatigue life estimation of the frame of a new electrically powered shuttle used in airports and resorts. The frame and components of the electrically powered shuttle were designed by expert employers of the OSCAR Co. using a CATIA computer program. Accelerometers were mounted under and above the prototype electric vehicle frame and external load path interaction data were collected by road tests. By means of five varied degrees of road roughness and three different conditions for vehicle loading, the raw road data were recorded via accelerometers on the vehicle frame. The recorded raw data was made available by processing with the FDesign program. After the external load was determined, a 3D drawing of the frame of the vehicle was transferred to the ANSYS program as a step file. In the chassis model transferred to the ANSYS program; material assignment, meshing of the frame structure, defining the boundary conditions of the structure and static analysis were determined according to the most critical road load, following which fatigue analyses were performed for five different roads. The fatigue life and damage amount of each road on the vehicle frame were calculated separately by considering Miner’s rule according to the S-N high cycle method. Experiments showed that no fatigue damage occurred within the predicted 200,000 km.
Vehicle component design is crucial for developing a vehicle prototype, as optimum parts can lead to cost reduction and performance enhancement of the vehicle system. The use of metaheuristics for vehicle component optimization has been commonplace due to several advantages: robustness and simplicity. This paper aims to demonstrate the shape design of a vehicle bracket by using a newly invented metaheuristic. The new optimizer is termed the ecogeography-based optimization algorithm (EBO). This is arguably the first vehicle design application of the new optimizer. The optimization problem is posed while EBO is implemented to solve the problem. It is found that the design results obtained from EBO are better when compared to other optimizers such as the equilibrium optimization algorithm, marine predators algorithm, slime mold algorithm.
In present the study, sudden cooling, in other words thermal shock, is applied to a plate that is originally a functionally graded material (FGM). The flat plate is assumed to have an edge crack on it. Hence a numerical couple-field analysis is performed on the plate. The FGM is a combination of Ni and Al 2 O 3 . The thermal and mechanical properties of the FGM are assumed to depend on temperature variation. The mixing percentages of the Ni and Al 2 O 3 throughout the plate are considered to vary (i) linearly, (ii) quadratically and (iii) in half-order. In order to solve the problem, a new subroutine depending on temperature is written using APDL (ANSYS Parametric Design Language) codes. Three values of the heat transfer coefficient are applied to the initially heated plate. As a result, the transient temperature variation and stress intensity factor are presented to show the thermo-mechanical relation of the plate. The material properties changing with temperature results in more reliable temperature values. Increasing the heat transfer coefficient results in better cooling and in a lesser amount of time to reach ambient air temperature.
To be able to successfully produce ceramic-reinforced aluminum matrix composites by using the powder metallurgy method, the wetting of ceramic reinforcements should be increased. In addition, the negative effects of the oxide layer of the aluminum matrix on sinterability should be minimized. In order to break the oxide layer, the deoxidation property of Mg can be used. Furthermore, by creating a liquid phase, both wettability and sinterability can be improved. In this study, the effects of Mg and Cu alloy elements and sintering phase on the wettability, sinterability, and mechanical properties of Al/B 4 C composites were investigated. For this purpose, various amounts (5, 10, 20, and 30 wt.-%) of B 4 C reinforced Al5Cu and Al5Mg matrix composites were produced by the powder metallurgy method. After pressing under 400 MPa pressure, composite samples were sintered for 4 hours. The sintering was carried out in two different groups as solid phase sintering at 560 °C and liquid phase sintering at 610 °C. Despite the deoxidation effect of Mg in Al5Mg matrix composites, higher mechanical properties were determined in Al5Cu composites which were sintered in liquid phase because wettability increased. The highest mechanical properties were obtained in the 20 wt.-% B 4 C reinforced Al5Cu sample sintered in liquid phase.
This article focuses on minimizing product costs by using the newly developed political optimization algorithm (POA), the Archimedes ‘optimization algorithm(AOA), and the Levy flight algorithm(LFA) in product development processes. Three structural optimization methods, size optimization, shape optimization, and topology optimization, are extensively applied to create inexpensive structures and render designs efficient. Using size, shape, and topology optimization in an integrated way, It is possible to obtain the most efficient structures in industry. The political optimization algorithm (POA) is a metaheuristic algorithm that can be used to solve many optimization problems. This study investigates the search capability and computational efficiency of POA for optimizing vehicle structures. By examining the results obtained, we prove the apparent superiority of the POA to other recent famous metaheuristics such as the Archimedes optimization algorithm and the Levy flight algorithm. The most important result of this paperwill be to provide an impressive aid for industrial companies to fill the gaps in their product design stages.
The present paper elucidates an experimental study on the surface modification of a ZE41 A magnesium alloy by electrical discharge coating (EDC) process with a tungsten carbide-copper (WC-Cu) powder metallurgy (PM) electrode. Investigated EDC parameters were compaction load, current and pulse on time. Measurement of coating characteristics such as material transfer rate (MTR) and surface roughness (Ra) were undertaken on the coated workpiece. As the design of experiment, response surface methodology was applied and analysis of variance (ANOVA) test was completed to study the influence of process parameters. Mathematical models were developed for coating characteristics to optimize the parameters. In this study, the reliability of the regression model is considered satisfactory with a value larger than 99 %. It was found from the study that the current plays a vital role in increasing the material transfer rate and minimizing the surface roughness of the coated surface followed by compaction load and pulse on time. Various studies such as scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) were carried out to determine the characteristics of the coated layer. These analyses confirmed the presence of the electrode materials in the coated surface.
Fe-MoNiAl-Al 2 O 3 powders were mechanically alloyed by a SPEX type attritor. The Fe based composite samples were reinforced with complex mechanically alloyed Fe-MoNiAl-Al 2 O 3 particles in different ratios and Fe matrix composites were obtained. Scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), X-ray diffraction (XRD), elemental surface mapping and microhardness tests were used to analyze the microstructures of the composites. The sintered composites showed that thin Al 2 O 3 particles were dispersed. High volume fractions of the reinforcement having nanometer dimensions were produced in metallic matrices. Additionally, as the content of Al 2 O 3 increased, the micro-hardness increased, which indicates that fine Al 2 O 3 particles had a reinforcing effect.
The circulation of recycled low density polyethylene (r-LDPE) globally, using Nigeria as point of reference is emphasized in this work. The need for combining r-LDPE with a less expensive organic fiber as an economical alternative material in panel production for printer component to reduce waste through recycling. In this study, the particle size (PS) and fiber content (FC) of date palm wood fiber (DPWF) in a r-LDPE matrix are essential factors to be considered for optimizing flexural strength (FS), flexural modulus (FM) and Izod impact strength (IIS) of r-LDPE-DPWF (recycled low density polyethylene-date palm wood fiber) composite for producing printer components. The variant FC and PS of the DPWF was compounded in r-LDPE matrix to optimize the FS, FM and IIS of r-LDPE-DPWF composite, using a central composite design (CCD) as a response surface methodology (RSM). The DPWF and r-LDPE-DPWF composite were analyzed by Fourier transformed infrared (FTIR). The results indicated that the FS, FM and IIS of r-LDPE-DPWF composite measured 46.66002 MPa, 1.150043 GPa and 1.99899 KJ × m -1 at optimal operation, respectively. Under these operating conditions, PS and FC were 60.78 mesh (250 μm) and 30 wt.-%, respectively. Finally, the main coefficient of determination (R 2 ) for the factors correlated with the characteristics of the r-LDPE-DPWF composite at an approximate value of 1 with a differential error of RSM and experiment values < 0.05 %. It was concluded that the RSM model yielded the necessary parameters for the r-LDPE-DPWF composite to be considered as a potential material for printer components.
The test data for static burst strength and load cycle fatigue strength of pressure vessels can often be well described by Gaussian normal or Weibull distribution functions. There are various approaches which can be used to determine the parameters of the Weibull distribution function; however, the performance of these methods is uncertain. In this study, six methods are evaluated by using the criterion of OSL (observed significance level) from Anderson-Darling (AD) goodness of Fit (GoF), These are: a) the norm-log based method, b) least squares regression, c) weighted least squares regression, d) a linear approach based on good linear unbiased estimators, e) maximum likelihood estimation and f) method of moments estimation. In addition, various approaches of ranking function are considered. The results show that there are no outperforming methods which can be identified clearly, primarily due to the limitation of the small sample size of the test data used for Weibull analysis. This randomness resulting from the sampling is further investigated by using Monte Carlo simulations, concluding that the sample size of the experimental data is more crucial than the exact method used to derive Weibull parameters. Finally, a recommendation is made to consider the uncertainties of the limitations due to the small size for pressure vessel testing and also for general material testing.
Materials testing for welding and additive manufacturing applications
In this paper, an effective process optimization approach based on artificial neural networks with a back propagation algorithm and response surface methodology including central composite design is presented for the modeling and prediction of surface roughness in the wire electrical discharge machining process. In the development of predictive models, cutting parameters of pulse duration, open circuit voltage, wire speed and dielectric flushing are considered as model variables. After experiments are carried out, the analysis of variance is implemented to identify the contribution of uncontrollable process parameters effecting surface roughness. Then, a comparative analysis of the proposed approaches is carried out to determine the most efficient one. The performance of the developed artificial neural networks and response surface methodology predictive models is tested for prediction accuracy in terms of the coefficient of determination and root mean square error metrics. The results indicate that an artificial neural networks model provides more accurate prediction than the response surface methodology model.
