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In the present study, Al-Zn-Mg alloy has been fabricated through the powder metallurgy route by keeping Zn content at 5.6% and varying Mg from 0% to 3%. The optimum composition of Mg was found to be 2% based on relative density, microhardness and microstructure. Al-5.6Zn-2Mg was subjected to deformation at various temperatures (300 ° C, 400 ° C and 500 ° C) and strain rates (0.5, 0.05 and 0.005). Potentiodynamic polarization and electrochemical impedance spectroscopy were used to assess the electrochemical behaviour of deformed preforms. Scanning electron microscopy was utilized to study the microstructure and corrosion morphology of Al-5.6Zn-2Mg under different conditions. In the present study, deformation behaviour (axial strain (ε z ), formability stress index (β σ )) has been related to mechanical (hardness) and electrochemical properties (corrosion rate, pitting potential (E pit )). By increasing deformation, potentiodynamic polarization results showed a decrease in corrosion current density (i corr ) and an increase in pitting potential, which increased the corrosion resistance of the alloy. The corrosion resistance of the alloy increased significantly by increasing deformation temperature and lowering strain rate. Corrosion rate also decreases with an increase in axial strain and formability stress index. The corrosion mechanisms found in deformed preforms were pitting and intergranular corrosion. The corrosion morphologies also revealed the closure of pores due to increase in temperature and a decrease in strain rate.
Abstract In predicting flow stress, machine learning (ML) offers significant advantages by leveraging data-driven approaches, enhancing material design, and accurately forecasting material performance. Thus, the present study employs various supervised ML models, including linear regression (Lasso and Ridge), support vector regression (SVR), ensemble methods (random forest (RF), gradient boosting (GB), extreme gradient boosting (XGB)), and neural networks (artificial neural network (ANN), multilayer perceptron (MLP)), to predict flow stress in the hot deformation of an Al–Zn–Mg alloy. The ML methodology involves sequential steps from data extraction to cross-validation and hyperparameter tuning, which is conducted using the hyperopt library. Model performance is assessed using average absolute relative error (AARE), root-mean-squared error (RMSE), and mean squared error (MSE). The results show that ensemble methods (RF, GB, XGB) and neural networks outperform traditional regression methods, demonstrating superior predictive accuracy. Visualization using half-violin plots reveals the models' error ranges, with XGB consistently exhibiting the best performance. SVR, RF, GB, XGB, ANN, and MLP showed better performance than the Arrhenius model in the context of AARE and MSE metrics. Interestingly, SVR had a somewhat higher AARE of 1.89% and an MSE of 0.251 MPa2, while XGB had the lowest AARE of 0.2% and the lowest MSE of 0.011 MPa2. When ML models were evaluated using the skill score in relation to the Arrhenius model, XGB scored higher than the support vector machine (SVM) at 0.714, with a score of 0.986. In contrast, Lasso and Ridge exhibited negative scores of −0.847 and −0.456, respectively.
Friction taper plug welding a variant of friction welding is useful in welding of similar and dissimilar materials. It could be used for joining of composites to metals in sophisticated aerospace applications. In the present work numerical simulation of friction taper plug welding process is carried out using finite element based software. Graphite reinforced AA6063 is modelled using the software ANSYS 15.0 and temperature distribution is predicted. Effect of friction time on temperature distribution is numerically investigated. When the friction time is increased to 30 seconds, the tapered part of plug gets detached and fills the hole in the AA6063 plate perfectly.
The microstructure evolution of sintered and extruded samples of Al–4Si–0.6Mg powder alloys at various semi-solid temperature ranges of 560 °C, 580 °C, and 600 °C, holding times of 600, 1200, and 1800 s, and strain rates of 0.1, 0.2, and 0.3 s −1 was studied. From the stress–strain curves and metallographic studies, Arrhenius grain growth model and Avrami dynamic recrystallization model have been formulated by means of linear regression. Parameters such as peak strain, critical strain, recrystallization fraction, and material constants have been found using the above equations. The experimental and calculated values of various material parameters agree with each other, indicating the accuracy of the developed model. Finite element method-based simulations were performed using DEFORM 2D software, and the average grain size obtained from experiments and simulations was validated by means of average grain size. The relative density of the compacted specimens as well as the extruded specimens was also simulated. The simulation results showed that large grains appeared at high temperatures and at the bottom of the specimen.
ABSTRACT Segmenting metallographic pictures is being done in material science and related domains in order to detect the features within them. Therefore, it becomes crucial to find grains and secondary phase particles. It is necessary to label every pixel in order to obtain satisfactory segmentation outcomes. However, labeling takes a lot of time and is physically taxing. Therefore, in order to obtain higher performance, we have suggested a semi‐supervised deep learning technique in the current study that uses fewer labeled images. Other deep learning algorithms, such as Segnet, Resnet, and FCN, were compared with the Unet approach that was suggested. Additional comparisons have been made using the Dice score (0.85), IOU score (0.74), F1 (0.85), and recall (0.96) measures. Different loss functions were also compared, including binary, SS loss, and Tversky. Furthermore, the dataset was expanded, and these datasets were also subjected to result analysis. The trials show that, both numerically and qualitatively, the suggested approach can produce superior outcomes with fewer labeled photos.
The aim of the present research study was to Formulate and Evaluate sustained release tablets of Cimetidine 500mg tablet using direct compression method. Nine formulations were prepared using different polymers like HPMC, Xanthan Gum and Sodium CMC. These Polymers were used in different proportion to control the released of the drug. All the formulations were evaluated of physicochemical tests. All the physicochemical characters of the formulated tablets were within the acceptable limits. The release pattern of the drug was observed over a period of 12 hours and determined the amount of drug by the UV-visible method. Dissolution data showed all formulations showed drug release for 12hrs. Among all the formulations C6 showed best drug release 99.08%. The optimized formulations were followed zero–order kinetics, korsmeyer –peppas and Higuchi models.
Commercial simulation software often lacks comprehensive material data for alloys fabricated through powder metallurgy (PM), creating challenges in developing accurate constitutive models. Establishing a clear understanding of the correlation between microstructure and mechanical behaviour during hot deformation is essential for optimizing alloy performance across diverse processing and service conditions. Thus, this study addresses these challenges by constructing and comparing the traditional Johnson Cook (JC) and Modified Johnson Cook (MJC) models for predicting flow stress. Additionally, the study investigates the correlation between microstructure and mechanical properties during the hot compression of an Al-Zn-Mg PM alloy. Experiments were conducted at various temperatures (300 °C and 500 °C) and strain rates (0.1 s⁻¹ and 0.0001 s⁻¹) to evaluate the effect of compression parameters on microhardness, flow stress and microstructural evolution. Electron Backscatter Diffraction (EBSD) analysis revealed that Kernel Average Misorientation (KAM), high-angle grain boundaries (HAGBs), low-angle grain boundaries (LAGBs), and average grain size are significantly influenced by the deformation conditions. The MJC model, with advanced features like a quadratic strain term and strain-rate-dependent temperature factor, achieves higher accuracy, evidenced by an R value of 0.994, AARE of 3.935%, and RMSE of 1.05 MPa. These results highlight the MJC model’s superiority in capturing complex deformation behaviours. At 300 °C and 0.1 s⁻¹, the microhardness reached 130 HV, with a high LAGB percentage (97.33%) and a fine average grain size of 10.91 μm, indicating a strain-hardened microstructure. Conversely, at 500 °C and 0.0001 s⁻¹, the microhardness decreased to 62 HV due to the dominance of dynamic recrystallization, which increased HAGBs percentage (7%) and grain size (19.72 μm). The Zener-Hollomon parameter and activation energy effectively correlate temperature and strain rate effects on microhardness and stress. Higher Z values indicate restricted grain growth and increased dislocation density, resulting in higher microhardness and stress values.
