Metallurgical and Materials Engineering (E-Journal)
Not a member yet
915 research outputs found
Sort by
Fabrication Of Al 2024/SiC Nanocomposite with Al and Cu Pure Coatings
No full textComposites find an important place as new advanced materials in last decades; those especially produced with nanoparticles reinforcements, attracts researchers and a number of researches were executed on this topic. In this study, Al-base 2024 alloy composites reinforced with SiC nanoparticles were fabricated and the effects of two different coating materials were investigated. Coatings were pure Al and Cu powder with constant grain particle size. The results show that the Al coating has impacts on grain size and the interface layer between reinforcement and matrix. The mechanism of formation of interface layer between SiC nanoparticles and the Al-base 2024 matrix with reinforced with Cu coated SiC particles is quite different
Predicting Concrete Compressive Strength: A Comparative Analysis Of Artificial Neural Networks And Adaboost For Enhanced Generalization Performance
Get PDFIntroduction: The accurate prediction of concrete compressive strength is critical for structural design and efficiency. Traditional testing methods are time-consuming, creating a demand for reliable machine learning (ML) models. This study compares the predictive performance and generalization capabilities of an Artificial Neural Network (ANN) and an AdaBoost algorithm for concrete strength forecasting, incorporating SHAP analysis for enhanced model interpretability.
Methods: Using a dataset of 1030 concrete mixtures, models were developed and hyperparameter-tuned. The ANN was configured with a single hidden layer (100 neurons, tanh activation), while AdaBoost used 1000 estimators. The dataset was split 80-20 for training and testing, with performance evaluated using R², RMSE, MAE, and MAPE. K-fold cross-validation and SHAP analysis were conducted to assess model stability and feature interpretability.
Results: Both models achieved a test R² of 0.84. However, AdaBoost exhibited significant overfitting, indicated by a near-perfect training R² (≈1.0) and a higher test MAPE (22.86%) compared to the ANN's consistent R² (0.84 on both sets) and lower test MAPE (17.17%). SHAP analysis revealed fundamentally different feature importance patterns: AdaBoost showed disproportionate reliance on Blast Furnace Slag with wide value dispersion indicating instability, while ANN demonstrated balanced, physically consistent relationships with cement and age as primary predictors.
Discussion: The ANN model demonstrated superior generalization and robustness by effectively learning underlying data patterns without memorization, making it more reliable for practical applications than the overfitted AdaBoost model. SHAP analysis provided crucial insights into model decision-making processes, validating ANN's alignment with concrete science principles while revealing AdaBoost's sensitivity to specific dataset characteristics
Cooling curve analysis in binary Al-Cu alloys: Part II- Effect of Cooling Rate and Grain Refinement on The Thermal and Thermodynamic Characteristics
Get PDFThe Al-Cu alloys have been widely used in aerospace, automobile, and airplane applications. Generally Al-Ti and Al-Ti-B master alloys are added to the aluminium alloys for grain refinement. The cooling curve analysis (CCA) has been used extensively in metal casting industry to predict microstructure constituents, grain refinement and to calculate the latent heat of solidification. The aim of this study is to investigate the effect of cooling rate and grain refinement on the thermal and thermodynamic characteristics of Al-Cu alloys by cooling curve analysis. To do this, Al-Cu alloys containing 3.7, and 4.8 wt.% Cu were melted and solidified with 0.04, 0.19, 0.42, and 1.08 K/s cooling rates. The temperature of the samples was recorded using a K thermocouple and a data acquisition system connected to a PC. Some samples were Grain refined by Al-5Ti-1B to see the effect of grain refinement on the aforementioned properties. The results show that, in a well refined alloy, nucleation will occur in a shorter time, and a undercooling approximately decreases to zero. The other results show that, with considering the cooling rate being around 0.1 °C/s, the Newtonian method is efficient in calculating the latent heat of solidification
Stability Analysis of Nonlinear Fluid Flows through Mathematical and Computational Approaches
Get PDFFluid nonlinear stability is a basic problem in fluid dynamics, which has a great impact in applications to industrial, meteorological, or engineering processes. This paper studies the mathematical and computational methods to analyze the stability of fluid flows governed by the nonlinear equations like Navier-Stokes equations. The transition scenarios, bifurcations and turbulence onset are also investigated by computational simulations. It is shown that combining analytical and numerical approaches improves understanding of flow stability, and thus facilitates predictive modeling of such systems
Malware Images Visualization and Classification with Parameter Tunned Deep Learning Model
Get PDFMalwares can be termed as a malicious program that can gain unauthorized access to the computer. This unauthorized access can damage and harm computing world in many capacities. There are many malware detection approaches present in the world. These approaches include static and dynamic analysis, machine learning, semi -supervised and deep learning-based models. These approaches cannot be visualized, thus cyber security experts face difficulty in interpreting underlying patterns. Conversion of malware byte code into images exits. An improved approach that can not only visualize malware, but also predict malware with high accuracy can be beneficial. For this purpose, we have used existing malware visualization technique. A technique which converts malware samples into images and then applies a contrast-limited adaptive histogram equalization algorithm to enhance the similarity between malware image regions in the same family. After conversion into images, we have applied parametrized tunned Convolutional Model to predict malware images. Comparing with existing our approach not only visualizes malware images but also outperforms previous approach by almost 2%, by achieving 98.27% accuracy.
 
The Role of Banking in sustainable Development: A study of Environmental and social Governance Factors
Get PDFGlobal economic tides have shifted, and sustainability has key priorities stemming from these trends — where banks have a fundamental function in addressing and funding sustainable processes. This paper seeks to investigate the significant influence of the banking sector in facilitating sustainable development by adopting it through the inclusion of Environmental, Social, and Governance (ESG) considerations in their operations, investment activities and policies. The paper explores how public and private sector banking institutions can urge corporates to align themselves with corporate and green financing by conducting an ESG practice in the finalizing of financial positions. The study employs a mix of primary and secondary data to examine the degree to which ESG is embedded in banking strategies and had influence on lending, risk management, and stakeholder engagement. The results show that banks with proactive ESG policies make, on average, a strong contribution on all three dimensions of ESG: environmental protection, social welfare and long-term economic sustainability. With this study proposes policy recommendations that would aid ESG compliance and bolster the role of banks as drivers for sustainable development in India and abroad
The Impact of Innovative Technologies on Contemporary Media Arts Evolution
Get PDFThe work is devoted to media art as a broad, dynamically developing area of contemporary art based on the use of media as tools for creating, distributing, and presenting works of arts. It is shown that contemporary media art embodies such important ideas as a work with an open form, interactive interaction of the viewer with the work, the viewer’s creativity, collective creativity, shifting the emphasis from the result of creativity to the process, the use of interactivity and virtuality as expressive means, the unification of life and art, and the democratization of artistic forms. Particular attention is paid to the consideration of the paradigm shift in the distribution of the roles of the artist and the viewer, as well as in the characteristics of the environment for creating a contemporary work of art, the elemental composition of this environment
Benchmarking Optimizers in Transfer Learning for Automated Weed Image Recognition
Get PDFThis study investigates the performance of different optimization algorithms within Transfer Learning for weed image analysis. Utilizing pre-trained Convolutional Neural Networks (CNNs), we compare Adam, SGD, and RMSprop optimizers for fine-tuning, aiming to enhance weed classification accuracy with limited data. The research evaluates each optimizer's impact on model convergence, accuracy, and robustness across diverse datasets. Experiments, conducted using MATLAB R2020a, employ the AlexNet architecture and a dataset of farming images from the Vidarbha region, Maharashtra, India. Results highlight significant variations in performance based on optimizer selection, demonstrating the critical role of optimization in achieving efficient and effective weed image analysis. This comparative analysis provides valuable insights for researchers and practitioners seeking optimal optimizer choices in Transfer Learning applications for agricultural image processing
Corrosion Detection Techniques for Asset Integrity and Maintenance in the Oil and Gas Industry: A Review
Get PDFCorrosion detection is essential for maintaining infrastructure safety, reliability, and longevity, particularly in industries such as oil and gas, where harsh environmental conditions accelerate material degradation. Corrosion in this industry affects the structural integrity of pipelines and increases life cycle costs. Carbon steel, a commonly used material, is highly susceptible to corrosion due to extreme operational conditions like high pressure, temperature fluctuations, and exposure to corrosive elements such as CO₂, H₂S, and chlorides. The extensive network of pipelines and remote locations make real-time corrosion detection challenging, as traditional inspection methods often prove insufficient, particularly for internal monitoring. Deep gas wells add another layer of difficulty, requiring reliable wireless communication for data acquisition. However, challenges persist in effectively detecting corrosion, especially in large, complex systems such as pipelines and offshore rigs, where traditional methods may not be sufficient. Additionally, advanced monitoring techniques using artificial intelligence (AI), and machine learning (ML), based solutions offer promising advancements, but they introduce new challenges related to cybersecurity, data management, and the need for specialized personnel. This review paper explores the different types of corrosion, detection techniques, their respective limitations, and the potential solutions to address these issues to ensure the long-term sustainability of the oil and gas industry
Mechanisms of Color Switching in Electrochromic Materials: A Comprehensive Review of Inorganic and Organic Systems
Get PDFElectrochromic materials, capable of reversible color changes upon electrical stimulation, have garnered significant attention for applications in smart windows, displays, and energy storage devices. This comprehensive review delves into the underlying mechanisms of color switching in both inorganic and organic electrochromic systems. Inorganic materials, such as transition metal oxides (e.g., tungsten oxide and nickel oxide), exhibit electrochromism primarily through intercalation processes where ions like Li⁺ reversibly insert into the material's lattice, altering its optical properties. Recent advancements have introduced multicolored inorganic electrochromic materials, expanding their application potential. Organic electrochromic materials, including conjugated polymers like polyaniline (PANI) and polythiophene derivatives, undergo color changes via redox reactions that modulate their conjugation length and electronic structure. For instance, PANI transitions from a yellow reduced state to a green oxidized state upon voltage application. Additionally, innovations in organic systems have led to devices capable of modulating between primary colors, covering the entire visible spectrum. Hybrid materials, such as MXenes, have emerged as promising candidates by combining the advantageous properties of both inorganic and organic systems. Notably, Nb₁.₃₃C MXene-based devices demonstrate colorless-to-black switching with significant transmittance modulation across a broad wavelength range, attributed to reversible ion insertion mechanisms. This review synthesizes recent progress in understanding the color-switching mechanisms of diverse electrochromic materials, highlighting their structural and compositional influences on optical behavior. By elucidating these mechanisms, we aim to inform the design and development of next-generation electrochromic devices with enhanced performance and expanded color palettes