Metallurgical and Materials Engineering (E-Journal)
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Pictureperfect: A Fusion Of Sparsenas And Ensemble Magic For Advanced Image Classification
Get PDFThis research endeavors to advance the frontiers of image classification through the harmonious integration of SparseNAS, a novel neural architecture search algorithm, and Ensemble Magic, an ensemble learning approach. By synergistically fusing these two methodologies, we aim to propel the performance of image classification models to unprecedented heights. Our proposed model, dubbed Picture Perfect, demonstrates a remarkable synthesis of efficiency and accuracy, showcasing the potential for breakthroughs in the realm of computer vision
Optimization Of GTAW Process Parameters For Joining Dissimilar Alloys, SS 202 And SS 304
Get PDFThis research investigates the joining of two dissimilar stainless-steel alloys, SS 304 and SS 202, using Gas Tungsten Arc Welding (GTAW). SS 304 is widely recognized for its excellent intergranular corrosion resistance, making it suitable for applications such as pressure vessels and automotive components. In contrast, SS 202 is a cost-effective alternative with good tensile strength and toughness, commonly employed in structural applications and kitchenware. TIG welding, known for its precision and control, enhances productivity while reducing time and cost. The study utilizes Taguchi’s Design of Experiments (DOE) methodology to determine the optimal welding parameters—including welding current, root gap, gas flow rate, groove angle, and filler material—to achieve uniform tensile strength and hardness in the dissimilar weld joint. Taguchi’s orthogonal array design, combined with Analysis of Variance (ANOVA), was employed to analyze weld characteristics and optimize process parameters for improved joint performance. The optimal tensile strength (553.543 MPa) was achieved with a welding current of 160 A, gas flow rate of 3 l/min, groove angle of 60°, root gap of 1 mm, and filler material 308 L. The optimal hardness (284.99 BHN) was obtained with a current of 60 A, gas flow rate of 9 l/min, groove angle of 40°, root gap of 1 mm, and filler material 304 L. These results were further validated and compared using predictive modeling techniques, including Linear Regression, Random Forest, Artificial Neural Networks (ANN), and a Genetic Algorithm, which served as a confirmatory approach for the optimal parameter combinations. The predicted outcomes were then compared with experimental values to assess the accuracy and reliability of the models in enhancing weld joint performanc
Quantum Computing: Implications for Artificial Intelligence and Machine Learning
Get PDFQuantum computing has transformed into a revolutionary technology which can revolutionize artificial intelligence (AI) and machine learning (ML). The processing capabilities of quantum systems rely on the principles of superposition and entanglement to complete operations at quantum-fast speeds which drives solutions for optimization problems and pattern detection along with deep learning breakthroughs. The document analyzes quantum computing fundamentals and its leadership over conventional systems and their applications toward enhancing AI and ML capabilities. This paper examines modern advancements as well as present obstacles and future prospects of this fast-growing field
Impact of Forecast Time-Step on PV Production Accuracy Using Machine Learning for Micro-Grid Efficiency
Get PDFEfficient energy management solutions are becoming more important with emergence of micro-grids that include photovoltaic generation & storage. Their prediction of energy output over the near to long term is an important part of their work. An essential parameter influencing the forecast's accuracy, optimum control time discretisation, efficiency, and computing load is the forecast time-step. This trade-off is measured by putting four machine learning (ML) forecast methods through their paces on two different sites, with time-steps ranging from 2 to 60 minutes as well as horizons from 10 minutes to 6 hours. The methods are evaluated on both horizontal and tilted global irradiance charts, depending on the availability of data. All of the methods show comparable findings, which show that for predictions less than an hour and between one and six hours, the error measure may be decreased by up to 1.9% every minute on the time-step, and by up to 2.8% every ten minutes. Additionally, it is demonstrated that for short-term horizons, it could be beneficial towards make high-resolution forecasts & then average results at time-step required by energy control scheme
Dielectric Characterization of Sodic Soils in the C-Band: Implications for Moisture Monitoring
Get PDFSodic soils, characterized by high sodium accumulation, exhibit poor structure, low permeability, and reduced fertility, impacting agricultural productivity. This study investigates the dielectric properties of sodic soils within the C-band (4–8 GHz) microwave frequency range to assess their correlation with moisture content, electrical conductivity (EC), and bulk density. Soil samples from Buldhana district, India, were analyzed using a vector network analyzer (VNA) and the coaxial probe technique. The results indicate a strong correlation between dielectric constant (ε') and soil moisture (R² = 0.991) and dielectric loss (ε'') with EC (R² = 0.997). Comparative analysis of Dobson, Hallikainen, and Mironov models revealed that Mironov’s model best predicts sodic soil behaviour. The findings demonstrate the potential of microwave remote sensing (MRS) for soil moisture and salinity assessment, aiding precision agriculture and reclamation strategies. Future research should extend to higher frequency bands and machine learning models for enhanced prediction accuracy
Methodology of Applying Sustainable Design to Achieve Energy Efficiency for Eco-Tourism Hotel buildings in Urban Areas at Siwa Oasis
Get PDFThe world is steadily embracing the concept of sustainability, with a growing focus on incorporating sustainable design principles into building construction—particularly in hotels. This approach aims to improve energy performance and minimize construction and operational expenses by utilizing locally sourced environmental materials. Such practices are especially prominent in urban areas characterized by a unique identity and distinctive style, like the Siwa Oasis. The main objective of this article was to examine the impact of utilizing local materials in buildings within Siwa Oasis on enhancing their energy efficiency. This was achieved through an analytical exploration of structures in the region, complemented by an applied study employing DESIGN BUILDER V 7.0 for buildings in Siwa Oasis. The results indicate an improvement in energy performance in hotel buildings in Siwa Oasis by using local materials by a percentage of approximately more than 14% compared to the base case
Sustainable Ground Improvement Using Waste Plastic Bottle Mattresses Beneath Strip Footings
Get PDFThe persistence of plastic waste, particularly polyethylene terephthalate (PET) bottles, poses a major environmental concern due to its non-biodegradable nature. Repurposing these materials as ground improvement elements provides an environmentally friendly and cost-effective alternative to traditional geosynthetics. This study investigates the use of waste plastic bottle mattresses as reinforcement for sandy subgrades supporting strip footings. A series of physical model tests was conducted to evaluate how variations in mattress embedment depth, width, and height influence foundation performance. The results showed that the inclusion of bottle mattresses consistently improved both the load-bearing capacity and the initial stiffness of the foundation system. The degree of improvement was closely tied to the interaction between the stress influence zone and the confinement effect of the reinforcement, with efficiency stabilizing beyond certain geometric limits. The outcomes of this study provide practical guidance for optimizing reinforcement geometry in shallow foundation systems and underscore the value of integrating recycled materials into geotechnical design frameworks
Optimizing the Structural and Photocatalytic Properties of TiO₂ Composites Synthesized via Sol-Gel Method
Get PDFTitanium dioxide (TiO₂) is widely studied for its photocatalytic and optical properties, but its performance can be enhanced by forming composites with other materials. This research focuses on synthesizing TiO₂ composites using the sol-gel technique by incorporating Al₂O₃, WO₃, ZnO, CNT, and SiNT. Additionally, optimizing the calcination process by modifying time and temperature is crucial to improving structural and functional characteristics. TiO₂ composites were synthesized via the sol-gel method, followed by controlled calcination under varying temperature and time conditions. X-ray diffraction (XRD) was employed to analyze the crystalline phases, while surface area and porosity were examined to assess material properties. UV-Visible spectroscopy was used to study the absorption spectra and band structure alterations in the composites. XRD analysis confirmed that the TiO₂-Al₂O₃ composite contained both anatase and rutile phases, along with Al₂O₃, exhibiting a specific surface area of 32.1 m²/g and an average crystallite size of 18.09 nm. The TiO₂-WO₃ composite demonstrated the formation of a mixed oxide system at the interface, altering its electronic properties. Optical absorption spectra revealed significant modifications in band structures, affecting light absorption and photocatalytic efficiency. The structural and optical characteristics of TiO₂ composites were significantly influenced by the choice of secondary material and calcination conditions. The findings suggest that optimizing these parameters can enhance photocatalytic performance, making TiO₂ composites promising for applications in environmental remediation and materials science
Statistical Analyses on the Seasonal Rainfall Trend and Annual Rainfall Variability in Baramati Tehsil of Pune District, Maharashtra
Get PDFRainfall is an extremely important environmental factor as changes in rainfall patterns have a major impact on agriculture. Understanding rainfall trends is an important tool for the development and future of agriculture. The variable attributes of climate are the annual climate temperature and precipitation, which have received a great deal of attention worldwide. The degree of variability or fluctuation of this factor varies from place to place and time to time. Therefore, it is necessary to design and examine climate science software systems in various formats to assess climate induced change and suggest adaptive strategies in countries with rain-fed agriculture attributing to a changing climate. In the present study we have studied the pattern and changes of rainfall and its distribution in Baramati Tehsil. For this research, the average rainfall period and rainfall from 2007 to 2023 have been studied. Also these correlations have been studied through statistical systems
AI in Financial Services: Fraud Detection and Risk Management
Get PDFToday Artificial Intelligence serves as a substantial force that advances financial institutions by strengthening their ability to detect fraud and handle organizational risks. The growth of sophisticated financial fraud requires modern-day solutions which AI has proven to be better than traditional detection methods. This research investigates the impact of AI technology in banking institutions through its use cases while exploring methodologies together with advantages along with barriers it creates. This research shows that AI-controlled financial systems use data protection methods to minimize losses and produce better decisions with additional needed steps to protect data security and ethical standards