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The Single point incremental sheet forming process known as a SPIF process. Which got a great attraction among other existed sheet metal forming processes because of their flexibility to manufacture complex products. The aluminum alloy material mechanical properties are adopted and integrated into the finite element (FE) code. The tool paths for the truncated cone shape are modeled in Fusion 360 software, and the coordinates are converted into 3D punch tool coordinates by the tool path generation framework tool for modeling the numerical simulation. In numerical modeling, three kinds of mesh settings are used to construct the mesh for producing consistent results. Afterward, the obtained results are tested against the experimental observations and the desired parts dimensions to verify the accuracy of the established FE model. Thickness variations in the formed parts are discussed in detail in terms of the thinning part, thinning location, and its size in percentage. A comparison of tested geometries displays that reduction in thickness tends to be uniform in the wall region and small fluctuation noticed near the tool retraction location. Overall, the statistical results of the SPIF process are well in contract with the experimental measurements. In terms of geometry dimensions and thickness reduction. In addition, the surface roughness was noticed to be increased when the step size is more extensive, and on the other hand, the machining time tends to be more if the contour step size is small in the SPIF process.

The single-point incremental forming process has witnessed significant advantages in automobiles, aerospace, and medical applications in recent years because of its flexibility in manufacturing complex shapes. In detail, the components are produced only using the toolpath, which is guided by computer-aided manufacturing software. However, during the forming process, the parts might experience fractures, which could heavily impact the formed part's geometric accuracy. The main purpose of this study is to analyze the formability of an AA3003-H18 aluminum alloy material in the SPIF process; for this purpose, the material properties are extracted from the experimental simple tensile test in three directions corresponding to the material rolling direction. At first, a simple tensile test is modeled and estimated the material properties for conducting the numerical simulations. Second, the real-time experiments of the SPIF process in terms of predefined forming conditions are performed, and then the surface roughness was measured to check the surface quality of the formed parts. Then, the formed parts are scanned using a 3D ATOS scanner and compared against the desired computer-aided design (CAD) model. Eventually, the numerical results are discussed in comparison with the experimental outcome and displayed a significant correlation toward the expected results. This results comparison communicates that the introduced finite element (FE) model can be adopted for investigating the appearance of thinning location, thinning reduction, distributions of stress and strain. The overall results show that satisfying material formability in better surface finish and geometric dimensional accuracy can be accomplished when the forming conditions are designed appropriately.

Single point incremental forming (SPIF) is a choice of interest in many manufacturing industries due to its wide range of applications. Materials such as copper, aluminum, steel, and many others formed various complex shapes through this process. However, the forming process could sometimes result in process defects, which could strongly influence the formed parts' geometric accuracy. The twist defect is one of them, which incrementally twists the forming sheet with a small angle at each forming step. In this paper, twist phenomena in the SPIF process have been investigated both numerically and experimentally. In the experiment, Aluminum Alloy (AA5052) was used to form a truncated pyramid shape, and a room temperature tensile test has been conducted to achieve the material's tensile properties. Then, the material property used in the simulation study of the SPIF using LS-DYNA software, where twist defect, stresses, strain, and thickness distribution are studied. The results from simulation and experiment show significant similarity against the expected results and this conveys that the proposed FE model of the SPIF process can be used to investigate the presence of twist, distributions of stress and strain, and thinning locations in the formed part.

The engineering field's main issues are often identified to be estimating the deformation and the strain measurements of the working material. Gauging displacements until the fracture more accurately is crucial in experimental procedures for assessing the chosen material properties. This research paper investigates the commercial magnesium alloy (AZ31B) material using digital images, often called Digital Image Correlation (DIC), which provides complete displacement and strain data information at each timestep rather than utilizing an extensometer. This method provides images taken during the deformation, and subsequently, the material properties computed using correlation software for tested samples. The plastic anisotropy coefficients are computed for test samples that cut down at angles of 0, 45, and 90 to the rolling direction. Also, the tensile test finite element model until the necking region was used to verify the fitted models such as Hollomon power-law and Ramberg–Osgood relationships to define the non-linear relationship between stress and strain. Hence, real models and numerical simulations of incremental forming are created to depict this research work's usefulness to the forming applications.

Isothermal tensile test of medium carbon steel material was conducted on a computer controlled servo-hydraulic testing machine at the deformation temperatures (923 to 1223 K) and the strain rates (0.05 to 1.0 s-1). Using the experimental data, the artificial neural network (ANN) model with a back-propagation (BP) algorithm was proposed to predict the hot deformation behavior of medium carbon steel material. For the model training and testing purpose, deformation temperature, strain rate and strain data were considered as inputs and in addition, the flow stress data were used a targets. Before running the neural network, the test data were normalized to effectively run the problem and after solving the problem, the obtained results were again converted in order to achieve the actual data. According to the predicted results, the coefficient of determination (R2) and the average absolute relative error between the predicted flow stress and the experimental data were determined as 0.997 and 0.913%, respectively. In addition, by evaluating each test conditions, it was found that the average absolute relative error based on an ANN model varied from 0.55% to 1.36% and moreover, the results showed the better predictability compared with the measured data. Overall, the trained BP-ANN model is found to be much more efficient and accurate by means of flow stress prediction with respect to the experimental data for an entire tested conditions.

This paper discusses the wear and friction with the 2 W% Al2O3 nanocomposite content of pure Mg and AZ91D Mg alloys. Sliding speeds of 0.5 and 1.5 m/s in cast materials with normal stress conditions have been used in sliding distances up to 2000 m/s (0.5, 1.0, and 1 MPa). In order to evaluate the work hardness of the materials measured on temperature similar to the contact surface, we used hardness patterns and hot-compression flow curves. Mg and AZ91D magnesium alloy pure monolithic Mg are low wear resistant due to an increase in contact temperature due to the adjustment of working conditions, but the wear rate was significantly lower in composite materials, mainly because of nanoparticle strength improvements. Although wear generally contributes to grain refining, increased wear capacity, and greater durability, wear resilience due to dislocation resistance and nanoparticles is seen as the primary wear mechanism in the existing nanocomposites.

The surface finish is an important characteristic in the incremental sheet forming (ISF) process and is often influenced by numerous factors within the forming process. Therefore, this research was aimed at identifying the optimal forming parameters through the Taguchi method to produce high-quality formed products. The forming tool radius, spindle speed, vertical step increment, and feed rate were chosen as forming parameters in the experimental design, with surface roughness as the response variable. Taguchi L16 orthogonal array design and analysis of variance (ANOVA) test were used to identify the parameter’s optimal settings and examine the statistically significant parameters on the response, respectively. Results confirmed that a significant reduction in surface roughness occurred with a drop in vertical step size and an increase in feed rate. In detail, the vertical step size has the most significant influence on the surface roughness, followed by the feed rate and the forming tool radius. In conclusion, the optimum level settings were obtained: forming tool radius at level 3, spindle speed at level 1, vertical step size at level 1, and feed rate at level 4. Additionally, confirmation experiment results based on the optimal settings indicated a good agreement against the experimental observation. Further, the response surface methodology (RSM) was also exploited to devise a mathematical model for predicting the surface roughness. The results comparison confirmed that both techniques could effectively improvise the surface finish.

Recently, the teaching and learning method in the conventional engineering education system needs a group of learners with personalized learning paths. The introduction of technologies like Artificial Intelligence will aid the learners to identify and detect learning opportunities utilizing historical information, present student profile and success data from an institution, and recommend learning measures to enhance their performance. This study proposes an Artificial Intelligence-based Meta-Heuristic Approach (AIMHA) for personalized learning detection systems and quality management. The proposed model has been utilized to optimize learning effectiveness by considering the nature of the learning path and the number of simultaneous visits to every learning action. In addition, a quality resolution can be determined by a meta-heuristic approach. The simulation findings of the learning actions have been utilized to examine the efficiency of the suggested method. The proposed method is evaluated learning activities achieved an efficiency ratio of 92.3%, sensitivity analysis ratio of 88.4%, performance ratio of 92.3%, precision ratio of 94.3% compared to other existing models.

In this work, the corrosion behavior of titanium composites of titanium-titanium boride composites for marine applications was investigated by conducting electrochemical polarization experiments in...

The single-point incremental forming process (SPIF) is one of the emerging manufacturing methods because of its flexibility in producing the desired complex shapes with higher formability at low-cost compared to traditional sheet forming methods. In this research work, we experimentally investigate the forming process to determine the influence of process parameters and their contribution to enhancing the formability without causing a fracture by combining the design of experiments (DOE), grey relational analysis (GRA), and statistical analysis of variance (ANOVA). The surface morphology and the energy dispersive X-ray spectroscopy (EDS) method are used to perform elemental analysis and examine the formed parts during three forming stages. The DOE procedure, a central composite design with a face-centered option, is devised for AA3003-H18 Al alloy sheet for modeling the real-time experiments. The response surface methodology (RSM) approach is adopted to optimize the forming parameters and recognize the optimal test conditions. The statistically developed model is found to have agree with the test measurements. The prediction model’s capability in R2 is computed as 0.8931, indicating that the fitted regression model adequately aligns with the estimated grey relational grade (GRG) data. Other statistical parameters, such as root mean square error (RMSE) and average absolute relative error (AARE), are estimated as 0.0196 and 2.78%, respectively, proving the proposed regression model’s overall closeness to the measured data. In addition, the prediction error range is identified as −0.05 to 0.05, which is significantly lower and the residual data are distributed normally in the design space with variance and mean of 3.3748 and −0.1232, respectively. ANOVA is performed to understand the adequacy of the proposed model and the influence of the input factors on the response variable. The model parameters, including step size, feed rate, interaction effect of tool radius and step size, favorably influence the response variable. The model terms X2 (0.020 and 11.30), X3 (0.018 and 12.16), and X1X2 (0.026 and 9.72) are significant in terms of p-value and F-value, respectively. The microstructural inspection shows that the thinning behavior tends to be higher as forming depth advances to its maximum, the deformation is uniform and homogeneous under the predefined test conditions.

The cold roll forming process is broadly used to produce a specific shape of cold-roll formed products for their applications in automobiles, aerospace, shipbuilding, and construction sectors. Moreover, a proper selection of strip thickness and forming speed to avoid fracture is most important for manufacturing a quality product. This research aims to investigate the presence of longitudinal bow, the reason behind flange height deviation, spring-back, and identification of thinning location in the cold roll-forming of symmetrical short U-profile sheets. A room temperature tensile test is performed for the commercially available AA5052&ndash, H32 Al alloy sheets using Digital Image Correlation (DIC) technique, which allows complete displacement and strain data information at each time-step. The material properties are estimated from the digital images using correlation software for tested samples, the plastic strain ratios are also calculated from samples at 0&deg, 45&deg, and 90&deg, to the rolling direction. The tested sample&rsquo, s surface morphology and the elemental analysis are conducted using scanning electron microscopy (SEM) method and energy-dispersive X-ray spectroscopy (EDS) analytical technique combined with element mapping analysis, respectively. The cold roll forming experiments are systematically carried out, and then finite element analysis is utilized to correlate the experiment with the model. The performed cold roll forming numerical model outcome indicates a good agreement with the experimental measurements. Overall, the presented longitudinal strain was observed to influence the geometry profile. The spring-back is also noticed at the profile tail end and is more pronounced at high forming speed with lower strip thickness. Conversely, while the forming speed is varied, the strain and stress variations are observed to be insignificant, and the similar results also are recognized for the thinning behavior.

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In the field of engineering, automobile and aerospace components are manufactured based on the desired applications from the metal forming process. For producing better quality of both symmetry and asymmetry mechanical parts, understanding the material deformation and analytical representation of the material ductility behavior for the working material is necessary as the forming procedures carried out mostly in the warm processing conditions. In this work, the hot tensile test flow stress-strain data were utilized to construct the constitutive equation for describing AISI-1045 steel material hot deformation behavior, and the test conditions, such as deformation temperatures and strain rates were 750&ndash, 950 &deg, C and 0.05&ndash, 1.0 s &minus, 1 , respectively. The surface morphology and elemental identification analysis were performed using the field emission scanning electron microscopy (FESEM) coupled with the energy-dispersive X-ray spectroscopy (EDS) mapping setup. In this work, the Arrhenius-type constitutive equation, including the strain compensation, was used to formulate the flow stress prediction model for capturing the material behavior. Besides, the Zener-Hollomon parameter was altered, employing incorporating the effect of strain rate and strain on the flow stress. The empirical model approach was employed to estimate the material model constants from the constitutive equation using the actual test measurements. The population metrics such as coefficient of determination ( R 2 ), sample standard deviation of the error (SSD), standard error of the regression (SER), coefficient of residual variation (CRV), and average absolute relative error (AARE) was employed to confirm the predictability of the proposed models. The computed results are discussed in detail, using numerical and graphical verification&rsquo, s. From the graphical comparison, the flow stress-strain data achieved from the proposed constitutive model are in good agreement with the actual test measurements. The constitutive model prediction accuracy is found to be improved, like the prediction error range from 3.678% to 2.984%. This evidence proves to be feasible as the newly developed model displayed a significant improvement against the experimental observations.

In this work grain boundary (GB) wetting phase transition of Inconel 718 is investigated. 15% GBs are totally wetted when the temperature is raised to 1200 °C. GB was characterized using Electron Backscattering Diffraction (EBSD). The experimental results revealed that the wetted GB fraction increases to 80% at 1200 °C and no evidence of transformation beyond this limit. The GBs clearly show that the wetting state and the cooling rate at about 1200 °C are almost similar to that of quenching. Under ambient condition, it exhibits low ductility which leads to the formation of second phase and increase in hardness and decrease of tensile strength.

A comparative study was performed on strain-compensated Arrhenius-type constitutive models established with two regression methods: polynomial regression and regression Kriging. For measurements at high temperatures, experimental data of 70Cr3Mo steel were adopted from previous research. An Arrhenius-type constitutive model necessitates strain compensation for material constants to account for strain effect. To associate the material constants with strain, we first evaluated them at a set of discrete strains, then capitalized on surrogate modeling to represent the material constants as a function of strain. As a result, disparate flow stress models were formed via the two different regression methods. The constructed constitutive models were examined systematically against measured flow stresses by validation methods. The predicted material constants were found to be quite accurate compared to the actual material constants. However, notable mismatches between measured and predicted flow stresses were revealed by the proposed validation techniques, which carry out validation with not the entire, but a single tensile test case.

In this paper, the performance of a novel desiccant assisted packed bed passive solar dryer was evaluated for copra processing and compared with conventional passive solar dryer. This novel solar dryer consists of a desiccant assisted packed bed solar air heater attached with a dryer cabin. The desiccant and phase change materials packed in the solar air heater has control the humidity and retains the heat for longer duration, respectively. The performance of the dryer was evaluated (in terms of drying time to attain the final equilibrium moisture content, drying rate, specific moisture extraction rate, pick-up efficiency, and dryer efficiency) under the meteorological conditions of Coimbatore city in India during March and April 2016. The copra was dried from initial moisture content (wet basis) of about 52% to the final moisture content (wet basis) of about 8% in 62 hours with specific moisture extraction rate of 0.82 kg/kWh. The drying time was reduced by about 44 hours when compared to the conventional passive solar dryer. The dryer pick-up efficiency was varied between about 10% and 65%. The average dryer thermal efficiency was calculated to be about 32%. The quality of final dried product was found to be good.

A numerical model has been proposed in this work for predicting the energy performances of the heat pump assisted regenerative solar still with phase changing material heat storage under Kazakhstan climates. The numerical model is based on energy and mass balance. A new regenerative heat pump configuration with phase changing material heat storage is proposed to improve the performance. A comparison of results has been made between the conventional solar still and heat pump assisted regenerative solar still with phase changing material. The numerical simulation was performed for wide range of ambient temperatures between -30 and 30°C with wide range of solar intensities between 100 and 900 W/m2. The numerical simulation results showed that heat pump assisted regenerative solar still is more energy efficient and produce better yield when compared to the conventional simple solar still. The influences of solar intensity, ambient temperature, different phase changing materials, heat pump operating temperatures are discussed. The predicted values were found to be in good agreement with experimental results reported in literature.

In this paper, the performance of a solar photovoltaic water pumping system was improved by maintaining the cell temperature in the range between 30°C and 40°C. Experiments have been conducted on a laboratory experimental set-up installed with 6.4 m2 solar panel (by providing air cooling either on the top surface or over the beneath surface of the panel) to operate a centrifugal pump with a rated capacity of 0.5 HP. The performance characteristics of the photovoltaic panel (such as, cell temperature, photovoltaic panel output, and photovoltaic efficiency), pump performance characteristics (such as pump efficiency and discharge), and system performance characteristics are observed with reference to solar irradiation, ambient temperature and wind velocity. A thermal model has been developed to predict the variations of photovoltaic cell temperature based on the measured glass and tedlar temperatures. The influences of cell temperature and solar irradiation on the performance of the system are described. The results concluded that cooling of photovoltaic panel on beneath surface has maintained the cell temperature in the range between 30°C and 40°C and improved the overall efficiency by about 1.8% when compared to the system without panel cooling.

Ductile fracture prediction is often considered as a challenging task in complex stress state applications, such as metal joining process. A systematic method for predicting the joint strength in the hot shear joining process is presented in this paper by considering two different ductile damage models, namely Johnson-Cook and modified Lemaitre damage models. Since the crack formation in metalworking is considered to be a combination of both load conditions and metallurgical properties, the microstructure evolution is taken into account by characterizing the considered material, AISI-1045, at different temperatures and for different strain rates and by testing different specimen shapes, namely round bar, joint bar, flat and notched specimens. The derived material properties have been inversely calibrated in order to be used in the developed numerical model. A shear joining process simulation has been implemented in DEFORM and have been validate with shear joining experiments carried out at 950°C, allowing to validate the accuracy of the proposed model in simulating the hot shear joining, both in terms of joining strength and identification of the damage locations. Thanks to a precise material characterization, and by utilizing the proposed numerical model, both the joining tool design and the process condition can be studied and improved.

The present research work focusses on both the material characterization and the preliminary test applications for hybrid structures made of high strength steel skin and sheet molding compound (SMC) core, aimed for the production of lightweight automotive body frames. A full material characterization has been carried out in order to determine the properties of the single materials, high strength steel and sheet molding compound. In addition, laminate specimens made of steel skin and SMC core have been manufactured utilizing the compression molding process and the relevant stress-strain behavior have been tested. By means of shear test, the shear strength resistance of the adhesive, utilized for the cohesion between these two materials, have been also characterized. In order to test the proposed hybrid material for the application in the automotive industry, the case of a commercial vehicle B-pillar component have been considered and tested by means of FEM analysis. By simulating the severe load conditions to which the B-pillar has to stand, the numerical analysis implemented in ANSYS will show how the developed material is able to withstand the same load conditions of the original full-steel one while granting a considerable reduction of weight.