Prediction of the forming results, determination of the thickness distribution and the thinning of the sheet metal blank will decrease the production cost through saving material and production time. In this paper, A Finite Element (FE) model is developed for the 3-D numerical simulation of deep drawing process (Parametric Analysis) by using ABAQUS/EXPLICIT FEA program with the proper material properties (anisotropic material) and simplified boundary conditions. The FE results are compared with experimental results for validation. The developed model can predict the thickness distribution and thinning of the blank with the die design parameters (geometrical and physical parameters). Furthermore, with numerical simulation, working parameters can be optimized without expensive shop trials.

Even though water is a renewable resource, the majority of the available water on the planet is unfit for human use. Moreover, the drinkable water demand is ever-increasing as a result of rising population, urbanization, and life standards, which makes the needs for sustainable, economic, and environment-friendly treatment alternatives of utmost importance. Seawater desalination using solar stills has been proposed as a promising alternative that may help to solve drinkable water scarcity issues. In the past decades, many studies have been conducted to assess the performance of different types of solar stills aiming to enhance their productivity. Computational fluid dynamic (CFD) numerical simulation is one of the approaches that have been used recently to assess the performance of solar stills. The present study performed a systematic review and bibliometric analysis to provide a comprehensive overview of CFD numerical simulation uses as a tool to assess solar stills performance. A total of 486 publications were collected initially from different databases for the period between 2012 and 2022. The collected publications were filtered through several stages reaching 43 publications of highest significance. The collected data were analyzed descriptively, and the bibliometric mapping was presented. Furthermore, the basics and principles of CFD numerical simulation of solar stills efficiency were described and discussed. Later, the previous studies were analyzed to understand the algorithms, methods, and still types used. Finally, future research scopes and conclusions were stated. The presented knowledge in this study can help to provide a deep overview of using CFD in studying the efficiency of solar stills and inspire researchers to identify future research ways and gaps.

Deep drawing is characterized by extremely complex deformation that is influenced by process characteristics such as die and punch shapes, blank shape, blank holding force, material properties, and lubrication. The optimization of the deep drawing process is a challenging issue due to the complicated functions that define and relate the process parameters. However, the optimization is essential to enhance the productivity and the product cost in the deep drawing process. In this paper, a MATLAB toolbox (Pattern Search) was employed to minimize the maximum deep drawing force (F_d-min) at different values of the operating and the geometrical parameters. As a result, a minimum deep drawing force chart (carpet plot) was generated to show the best combination of friction coefficients at the blank contact interfaces. The extracted friction coefficients guided the selection of proper lubricants while minimizing the deep drawing force. A finite element analysis (FEA) was applied through 3D model to simulate the deep drawing process. The material modeling was implemented utilizing the ABAQUS/EXPLICIT program with plastic anisotropy. The optimization results showed that the deep drawing force and the wrinkling decrease when compared with experimental and numerical results from the literature.

In this paper, a modern non-contacting optical technique was used to study the surface roughness of commercially pure copper. Finite element (FE) method was applied to predict the stress during orthogonal cutting by simulating the machining process. The experimental work empathized mainly on the effect of cutting speed (N) and feed rate (f) on the surface roughness of copper. Scanning electron microscope (SEM) was utilized to evaluate the surface variations at different machining conditions. Johnson-Cook mathematical model was adopted and employed to determine the parameters of the material. Furthermore, the maximum Von-Mises stress was predicted as a function of machining conditions. A software package of code (ABAQUS/CAE) was used for the analysis and response surface methodology (RSM) was applied to visualize the results. The results showed a significant effect of the feed rate/cutting speed interaction on surface roughness and Von-Mises stress of copper. An enhancement of 14% in surface roughness was perceived with increasing the cutting speed. A good agreement was observed between experimental and analytical results.

This study developed a multi-objective optimization procedure aimed at minimizing surface roughness and volumetric shrinkage in injection-molded products. Surrogate models for both outputs were constructed using the Kriging technique, based on experimental data and seven input parameters: packing pressure, mold temperature, cooling time, injection speed, injection pressure, melt temperature, and packing time. A multi-objective optimization problem was formulated and solved using the pattern search algorithm, generating a Pareto front that highlights the trade-off between the two objectives. This Pareto front was further analyzed to determine three optimal parameter sets. The first point minimizes volumetric shrinkage at 1.9314 mm³ but results in the highest surface roughness of 0.55956 µm. In contrast, the second point yields the lowest surface roughness of 0.20557 µm but the highest volumetric shrinkage of 3.9286 mm³. The third point offers the best compromise between the two objectives, with a volumetric shrinkage of 2.2348 mm³ and surface roughness of 0.28246 µm. The proposed approach provides an experimentally validated tool for plastic engineers, enabling informed parameter adjustments to achieve optimal trade-offs in surface quality and dimensional stability within practical manufacturing constraints.