Iranian Journal of Materials Science and Engineering

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In this investigation, α 2-D Finite Volume Method (FVM) with unstructured triangular mesh is developed to simulate the mould filling process. The simulation of fluid flow and track of free surface is based on the Marker And Cell (MAC) technique. This technique has capability ofhandling the arbitrary curved solid boundaries in the casting processes. In order to verify the computational results of the simulation, a thin disk plate with transparent mould was tested. The mould filling process was recorded using a 16mm high-speed camera. Images were analyzed frame by frame, in order to tracking of free surface and filling rate during mould filling. Comparison between the experimental method and the simulation results has shown a good agreement.

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ABSTRACT Macrosegregation has been received high attention in the solidification modeling studies. In the present work, a numerical model was developed to predict the macrosegregation during the DC Casting of an Al-4.5wt%Cu billet. The mathematical model developed in this study consists of mass, momentum, energy and species conservation equations for a two-phase mixture of liquid and solid in an axisymmetric coordinates. The solution methodology is based on a standard Finite Volume Method. A new scheme called Semi-Implicit Method for Thermodynamically-Linked Equations (SIMTLE) was employed to link energy and species equations with phase diagram of the alloying system. The model was tested by experimental data extracted from an industrial scale DC caster and a relatively good agreement was obtained. It was concluded that a proper macrosegregation model needs two key features: a precise flow description in the two-phase regions and a capable efficient numerical scheme

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Relations between the microstructure, mechanical properties and machinability of as-cast 65Cu-35Zn brass with various amounts of Al from 0 to 4.72 and Si from 0 to 3.62 wt% were investigated. Both Si and Al initially enhanced the UTS and toughness of the brass samples, which led to improvement in machinability due to a reduction in the main cutting force. A duplex brass with random oriented α plates in β’ matrix was found to have the best machinability among the other microstructures. It was found that beside the presence of brittle phases, such as β’ phase in the microstructure, the morphology and hardness of the phases involved had significant influence on machinability.

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The growth of industries, populations, and industrial activities includes environmental pollutants. Pollution causes problems such as reduced light transmission, anaerobic conditions, and complications such as allergies and cancer for humans and other living organisms. The adsorption method is one of the most attractive, and efficient methods for removing environmental pollutants such as pharmaceuticals. Among the standard methods for wastewater treatment, adsorption is more efficient than other methods and is more economical. They have a meager price. Adsorption of pollutants can be an excellent way to remove toxic substances from polluted waters and industrial effluents. In this review, pharmaceutical removal by adsorption process was reviewed in details.

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Aluminide coatings are widely used in high-temperature applications due to their excellent corrosion resistance and thermal stability. However, optimizing their composition and thickness is crucial for enhancing performance under varying operational conditions. This study investigates the optimization of aluminide coatings through a data-driven approach, aiming to predict the coating thickness based on various composition and process parameters. A comparative analysis of six machine learning models was conducted, with the k-nearest neighbors regressor (KNNR) demonstrating the highest predictive accuracy, yielding a coefficient of determination R² of 0.78, a root mean square error (RMSE) of 18.02 µm, and mean absolute error (MAE) of 14.42. The study incorporates SHAP (Shapley Additive Explanations) analysis to identify the most influential factors in coating thickness prediction. The results indicate that aluminum content (Al), ammonium chloride content (NH₄Cl), and silicon content (Si) significantly impact the coating thickness, with higher Al and Si concentrations leading to thicker coatings. Zirconia (ZrO₂) content was found to decrease thickness due to competitive reactions that hinder Al deposition. Furthermore, the level of activity in the aluminizing process plays a crucial role, with high-activity processes yielding thicker coatings due to faster Al diffusion. The pack cementation method, in particular, produced the thickest coatings, followed by gas-phase and out-of-pack methods. These findings emphasize the importance of optimizing composition and processing conditions to achieve durable, high-performance aluminide coatings for high-temperature applications.