Sri Shakthi SIET Journals
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Stir casting studies on aluminium (Al8011) and zirconia (ZrO2) metal matrix composites
Get PDFThis study investigates the properties of Aluminium (Al8011) and Zirconia (ZrO2) metal matrix composites (MMCs) fabricated through stir casting, which is a common technique used to produce MMCs. Al8011 was used as the matrix material whilst ZrO2, a ceramic material, was used as the reinforcement. The composites were developed with varying weight percentages of ZrO2 (4, 8 and 12 wt%) and were analysed using tensile, wear and hardness, scanning electron microscopy (SEM) and hardness testing. The findings show that as the percentage of ZrO2 increased in the composites, the density and hardness significantly increased. SEM results revealed that the distribution of ZrO2 was homogenous throughout the matrix, indicating the method of stirring was effective in improving particle dispersion. Overall, the study concluded that MMCs with high percentage of ZrO2 has the potential to enhance mechanical and physical properties whilst maintaining insignificant level of porosity. However, future research is needed to address the impurities identified in this study
Study of Staphylococcus aureus Adhesion on Surface Modified Silver Coated Non-Woven Polyethylene Fabric subjected to Atmospheric Plasma treatment
Get PDFThis study reveals the adhesion of Staphylococcus aureus on the modified surface of silver-coated non-woven disposable polyethylene fabric used in hospitals to cover the patient’s bed. The bacteria Staphylococcus aureus is responsible for many Nosocomial infections. Therefore, we should take action to reduce the spread of Staphylococcus aureus. The present study is focused on a nonwoven polyethylene fabric used as a bedspread that has been plasma-treated and coated in silver to prevent the adhesion of S. aureus and its growth. Non-woven polyethylene fabric is plasma-treated for quick silver adherence before being coated with silver and treated with S. aureus. Tests for fabric characterization were performed. It includes contact angle, FTIR, and SEM.SEM, FTIR, and contact angle measurements are made on the control, plasma-treated, silver-coated, and S. aureus samples. The plasma treatment will cause the fabric to enhance its surface properties. The increased surface roughness will cause the silver to adhere rapidly. The Silver will also prevent the bacteria from multiplying. Silver's antibacterial characteristics, guarantee the destruction of the germs. A bedspread made of nonwoven polyethylene fabric with a silver coating is possible. so that the sufferers can rest comfortably. The number of nosocomial infections spread by the clothing will decline. It is possible to prevent bacterial infections in the patients and medical staff
Functional Group Analysis of Hybrid Polyurethane Foam Derived from Waste Cooking Oil
Get PDFAnnually, a staggering three billion gallons of Waste Cooking Oil (WCO) are generated globally. To foster a health-conscious lifestyle and champion the creation of an unpolluted environment, effective WCO management is imperative. The repetitive utilization of WCO for cooking purposes yields detrimental effects on human health and diminishes overall productivity. This research delves into the fundamental characteristics of bio-based polyurethane (bio-PU), derived from discarded sunflower and palm oils. The findings are juxtaposed with those of non-biodegradable commercially available Polyurethane (PU). Through a process of addition polymerization conducted at room temperature, samples of PU foam are created. Specifically, 2.5 ml, 5 ml, and 7.5 ml of sunflower and palm oil are amalgamated with 5 ml of polyol and an equivalent amount of isocyanate. The vibrational attributes of amino acids and cofactors, which exhibit sensitivity to subtle structural alterations, are closely examined using Fourier transform infrared spectroscopy (FTIR). This technique, despite its lack of pinpoint precision, permits direct exploration of the vibrational properties of numerous cofactors, amino acid side chains, and water molecules. The presence of Polyurethane and its associated functional groups in the synthesized samples is verified through Fourier Transform Infrared Spectroscopy (FTIR) analyses. To ascertain Temperature ranges for primary phases of thermal degradation, discernible chemical bands within foams—comprising both recognized and unfamiliar compounds with distinct groupings—are evaluated. Emphasis is placed on identifying the peak release rates of particular chemical compounds (namely, CO2, -NCO, H2O, and C=O)
E-Coating Ultrafiltration System Maintenance using Machine Learning Techniques
Get PDFUltrafiltration process is one of the important processes in e-coating of metal parts. It is important to maintain and improve the performance of ultrafiltration membrane (UF membrane). This UF membrane should not be degraded under any situation, if this happens then it might lead to the flooding of coating fluid all over the metal parts. Hence it has to monitor properly. This will also lead to the wastage of coating fluid, wastage of materials and maintenance cost will also be high. So to avoid this, the workers have to monitor it on periodically basis. During e-coating in electrophoresis painting plant there may occur fault in filter and excess of fluid will flood in the material to be coated. The filter used in the ultrafiltration subsystem has to be monitored manually each time by the worker. Sometimes, if the worker did not monitor properly, it might lead coating to inappropriate places, it will cause wastage of material and fluid and also it takes time for clean-up and to change after fault occurs. Hence, the timely detection of faults is very important to prevent damage. In order to prevent these kinds of situations, a machine learning model is developed which predicts the flow meter readings beforehand. It uses an ensemble learning algorithm known as XGBoost which is one of the more powerful tools for prediction
Machine Learning based Early Stage Identification of Liver Tumor using Ultrasound Images
Get PDFLiver cancer is one of the most malignant diseases and its diagnosis requires more computational time. It can be minimized by applying a Machine learning algorithm for the diagnosis of cancer. The existing machine learning technique uses only the color-based methods to classify images which are not efficient. So, it is proposed to use texture-based classification for diagnosis. The input image is resized and pre-processed by Gaussian filters. The features are extracted by applying Gray level co-occurrence matrix (GLCM) and Local binary pattern (LBP in the preprocessed image. The Local Binary Pattern (LBP) is an efficient texture operator which labels the pixels of an image by thresholding the neighborhood of each pixel and considers the result as a binary number. The extracted features are classified by multi-support vector machine (Multi SVM) and K-Nearest Neighbor (K-NN) algorithms. The Advantage of combining SVM with KNN is that SVM measures a large number of values whereas KNN accurately measures point values. The results obtained from the proposed techniques achieved high precision, accuracy, sensitivity and specificity than the existing method
Impact of age hardening on the corrosion characteristics of dissimilar aluminum alloys welded using an innovative MIG welding approach
Get PDFAn innovative metal transfer technique in MIG welding was utilized to join aluminum-based alloys such as 6061 and 5083. Following welding, the specimens underwent heat treatment to explore their corrosion resistance by modifying their microstructure. Tafel polarization curve analysis was employed to evaluate the corrosion performance of the weld zone. The findings indicated that finer microstructures in the weldment led to a notable shift towards less negative corrosion potentials compared to coarser microstructures observed in the as-welded condition. Scanning electron microscopy and X-ray diffraction were used to investigate the microstructural morphology and phase identification of the weld zone. The findings of these tests revealed a link between microstructure and corrosion behaviours
Harnessing Effectiveness of ResNet-50 and EfficientNet for Few-Shot Learning
Get PDFInspired by the concept of human intelligence- learning and expanded upon with several examples – several- step learning focused on computers that can classify images in a comparable way. This article covers the interesting field of sparse learning, focusing on comparing its implementation using two popular deep learning networks: ResNet and EfficientNet. Little learning has the potential to be effective on tasks where obtaining large data sets is expensive or impossible. This allows machines to mimic humans’ ability to learn and expand from small samples, thus opening possibilities in the field of several types of diagnostics, personalized recommendations, systems, and robotics. Our main goal is to measure and compare the accuracy achieved by these models when learning on limited datasets and to show that EfficientNet achieves better accuracy when it requires fewer parameters and computational resources compared to ResNet-We considered VGG-flowers dataset for comparison. Our results show that Narrow EfficientNet outperforms ResNet-50 in terms of overall accuracy (85.20% vs. 84.30%), precision (85.60% vs. 85.40%), recall (85.30% vs. 84.50%) and F1 acquisition (85.45% vs. 84.95%). This suggests that EfficientNet’s emphasis on computational efficiency and parallelism may provide a slight advantage on limited data
Unlocking the Potential of Sodium Ion Batteries: A Comprehensive Review
Get PDFSodium ion batteries (SIBs) have recently emerged as a promising alternative to lithium-ion batteries (LIBs) due to their abundance, cost-effectiveness, and similar electrochemical properties. This review provides an in-depth analysis of the science behind it and the technology to explore, the scope for commercialization, prospects, advantages, and disadvantages of SIBs in the realm of energy storage technology. Through a critical examination of current research and developments, this article elucidates the potential of SIBs in revolutionizing the energy storage landscape
Comparative Studies on Application of Various Adsorbents in Textile Waste Water
Get PDFThis thesis aims to explore the potential of employing natural adsorbents, such as cashew nut shells, date seeds, orange peels, and coir pith, to mitigate COD levels in textile wastewater. The wastewater used for the study was sourced from a textile industry located in Salem. The investigation involved batch studies, wherein the effectiveness of each selected absorbent in reducing COD was assessed to determine the most efficient among the four sorbents. The initial concentration from the batch research served as a basis for identifying the optimal adsorbent, with the COD of the textile wastewater maintained consistently along with the initial dye concentration. To conduct the study, the adsorbent was incrementally introduced in 10 g portions into conical flasks. Over a10-minute period following a 20-minute contact time, the supernatant liquid from each conical flask was collected using syringes. The COD concentration in the obtained samples was determined using a standard methodology. Results revealed that date seeds exhibited the highest percentage of COD removal at 67%, followed by cashew nut shells at 45%, coir pith at 33%, and orange peels at 23%. The data obtained indicated that cashew nut shells and date seeds achieved the highest percentages of COD reduction, respectively. On the other hand, the Orange Peel Adsorbent displayed the least reduction in COD. Based on the collected findings, date seeds emerge as a promising adsorbent for effectively lowering COD in the treatment of textile wastewater
Transverse Fluctuations and Their Effects on the Stable Functioning of Semiconductor Devices
Get PDFSemiconductor plasma is often found in chaotic unpredictable motion which shows some anomalous behaviors providing multiple challenges to work with the instabilities in a semiconductor device. Experimental studies have shown that these instabilities give rise to fluctuations and azimuthal non-uniformities, which are usually present in the semiconductor. The energy fluctuations have also been observed in some of the cases. In this paper, we have obtained the fluctuations in velocity field by integrating the linearized governing hydrodynamic equations with RungeKutta method of order four (RK4). Then, we have come up with a mathematical formulation, where these fluctuations can be obtained from a KdV family equation with homotopy-assisted symbolic simulation. We have also obtained the relative velocity between the solitary structures for different parameters. Finally, by giving a detailed explanation of the behavior of semiconductor devices, we can study the usefulness of formulating the plasma waves in the various regime, and predict their characteristics theoretically