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Alpha-Bit: An Android App for Enhancing Pattern Recognition using CNN and Sequential Deep Learning
This research paper introduces Alpha-Bit, an Android application pioneering Optical Character Recognition (OCR) through cutting-edge deep learning models, including Convolutional Neural Networks (CNNs) and Sequential networks. With a core focus on enhancing educational accessibility and quality, Alpha-Bit specifically targets foundational elements of the English language - alphabets and numbers. Beyond conventional OCR applications, Alpha-Bit distinguishes itself by offering guided instruction and individual progress reports, providing a nuanced and tailored educational experience. Significantly, this work extends beyond technological innovation; Alpha-Bit's potential impact encompasses addressing educational inequalities, contributing to sustainability goals, and advancing the achievement of Sustainable Development Goal 4 (SDG 4). By democratizing education through innovative OCR technologies, Alpha-Bit emerges as a transformative force with the capacity to revolutionize learning experiences, making quality education universally accessible and empowering learners across diverse socio-economic backgrounds. 2024 ITU. -
Artificial Intelligence Application in Human Resources Information Systems for Enhancing Output in Agricultural Companies
Artificial intelligence (simulated intelligence) apparatuses like master systems, normal language handling, discourse acknowledgment, and machine vision have changed how much work in agribusiness, yet in addition its nature. This is on the grounds that the total populace and interest for food are developing, and the climate and water supply are evolving. Specialists and researchers are presently moving towards involving new IoT advances in shrewd cultivating to assist ranchers with utilizing manmade intelligence innovation to improve seeds, crop security, and composts. This will get ranchers more cash-flow and help the pay of the country in general. In agribusiness, computer-based intelligence is making its mark in three primary regions: checking soil and harvests, prescient examination, and cultivating robots. Along these lines, ranchers are utilizing sensors and soil tests increasingly more to accumulate information that can be utilized by ranch the board apparatuses for additional exploration and examination. This book adds to the field by giving an outline of how computer-based intelligence is utilized in agribusiness. It begins with a prologue to simulated intelligence, including a survey of all the computer-based intelligence techniques utilized in horticulture, similar to AI, the Web of Things (IoT), master systems, picture handling, and PC vision. 2024 IEEE. -
A Machine Learning- Based Driving Assistance System for Lane and Drowsiness Monitoring
Lane line detection is a vital component when driving heavy vehicles; this concept follows the path for driving a vehicle to prevent the risk of accidentally entering another lane without the drivers knowledge, which could result in an accident. To detect the lane, use frame masking and Hough line transformation with efficient machine learning algorithms, pre-processed and trained adequately for optimum accuracy as per the provided dataset to spot the white markings on both sides of the lane. Long-distance truck drivers suffer from sleep deprivation, making driving extremely dangerous while tired and they ignore the line markings and wander into the wrong lane. This chapter proposes a portable system that does not require any sensors or interference with the vehicles wiring system; instead, a system that fits on a windshield or any surface to monitor the actions of the driver, using computer vision and feature-extracted datasets within a trained neural network model using cameras. This driver-assisted system can detect drowsiness and give an alarm to wake up the driver by identifying the Region of Interest. These predictions are made based on eye movements, and the algorithm generates a score. The higher the score, the longer the time between alarms. 2024 Taylor & Francis Group, LLC. -
Augmented Reality-Enabled IoT Devices for Wireless Communication
[No abstract available] -
Data-Driven Strategies for Twitter Engagement: Hashtag Recommendations and API Insights
Twitter is a great way to connect with people worldwide, and one of the best ways to do that is by using hashtags. A hashtag is a keyword or phrase attached to a particular topic, and users can use it to find related tweets. Using a hashtag relevant to the needs or for business can increase the tweets visibility and make it easier for people to see the content they want. It can hugely help content creators who want to increase engagement and influence their tweets. This research introduces TagMate, a hashtag recommender system for Twitter that offers significant benefits. By accessing the tweets using Twitter API and after analyzing and performing algorithms, recommendations for hashtags can be obtained. The Twitter API allows access to various information about the account, including followers, tweets, content, etc. This information can be used to generate recommendations for hashtags related to the business. The system will generate hashtags according to the tweet and recommend trending or popular hashtags to increase their reach or engagement on the Twitter platform. Using the API, a dashboard can be created showing which hashtags are being used most frequently and which are most popular. This information can help create more relevant and engaging tweets, attracting more followers and interest. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
DermAI: A Deep Learning-Based Mobile Application for Multi-type Skin Cancer Detection
The significance of early skin cancer detection for effective prevention and treatment is underscored by the limitations of traditional manual diagnostic methods used by dermatologists. Leveraging Convolutional Neural Networks (CNNs) and the HAM10000 dataset, this research aims to automate skin cancer classification through dermatoscopic image analysis. The primary objective of the research is an accurate classification system identifying seven specific skin cancer types. The novelty is the deployment of the classification system using a Mobile Application - DermAI. The trained CNN model, spanning 10 epochs, achieved remarkable precision, peaking at a 97.90 percentage test accuracy during the 7th epoch. Evaluation metrics like the confusion matrix confirm its reliability in categorizing lesions, minimizing misclassifications, and validating its efficiency as a diagnostic tool. Transforming the model into TensorFlow Lite format enables seamless integration into mobile platforms, optimizing computational resources. This allows users to access prompt skin cancer classification via an Android application, fostering accessibility to preliminary assessments. Early identification facilitates timely medical intervention, a crucial factor in enhancing prognosis. Through CNNs, TensorFlow Lite, and mobile deployment, this research strives to bridge technology and healthcare accessibility, empowering individuals to proactively manage their skin health based on classification results and initiate timely discussions with healthcare professionals. 2025 IEEE. -
Introduction to multimodal learning and heterogenous data
With the rising advancements in computation, technology, and many innovative evolutions coming into play, multimodal learning is one of the most rapidly growing fields within the domain of artificial intelligence and Machine Learning. It mainly focuses on integrating information from multiple sources called "modalities," allowing the systems to utilize the varieties of data to furtherenhance their understanding and performance. These so-called modalities make use of various types of data in the form of text, images, audio, and sensor readings. They are able to process complex information due to these modalities and thus provide more insightful results for the tasks that they are assigned. Another important aspect of multimodal learning is heterogeneous datadata that differs significantly in structure, format, and origin. This type of data falls mainly into three categories, comprising structured data, which is quite organized and therefore easy to locate or search, as in the case of the database records. Then comes unstructured data characterized by the free form, which comprises mainly social media posts, videos, and images. In addition, it has been possible to separate semistructured data. It incorporates some features of being ordered, like the metadata included in XML or JSON files; however, a fixed schema does not apply. The understanding of the kind is important because each type calls for a different problem, and each type poses new opportunities in analysis. Handling the heterogeneous data effectively is all the more important because the said system will be fed heterogeneous data, and if its combination and analysis go reasonably logically, it is expected to be a source for multimodal systems. The ability to merge structured, unstructured, and semistructured data improves performance across a wide range of tasks, including but not limited to common applications like image recognition, sentiment analysis, and decision-making processes in autonomous systems. For example, in the multimodal learning case, it would be beneficial for the system that learns customer feedback to merge textual reviews, audio recordings of customer interactions, and visual data from product images. It has been known to yield a much clearer picture of what customers really want and how they actually behave. This chapter introduces notions of multimodal learning as well as heterogeneous datatheir characteristics, types, sources, and practical usage. It will attempt to establish a basic understanding of these two concepts in relation to each other in order to support more advanced applications through machine learning. In a review of the possible compositions between multimodal learning as well as heterogeneous data, the chapter will introduce their importance regarding the creation of intelligent systems that can address complex, intricate tasks across differing fields. As we enter the data age, with multiple sources churning out data at unprecedented rates that appear to have no bounds, integration of multimodal learning with heterogeneous data cannot be ignored. This will be vital for coming up with flexible yet useful applications to real-world problems. This region is promising for systems that perceive, interpret, and respond to the variability of information in a fashion similar to human reasoning and decision-making. Future application of artificial intelligence in the life of man will result from continuous research in the areas of multimodal learning and heterogeneous data. 2026 Elsevier Inc. All rights reserved. -
BAYESIAN SPATIAL TEMPORAL TREND ANALYSIS FOR DECISION MAKING AND RISK ASSESSMENT IN DENGUE INCIDENCE STUDIES: A CASE OF TAMILNADU
This study presents a Bayesian spatial-temporal analysis for studying Dengue incidence in Tamil Nadu, aiming to provide insights into decision-making and risk assessment strategies. Statistical models that allow a more accurate depiction of true disease rates by borrowing information from neighboring regions will help mitigate the effects of sparsely populated regions and deliver better inference. Perhaps the most conspicuous manner of modeling spatial dependence is to introduce spatially associated random effects within a Bayesian hierarchical setting. The Bayesian modeling and inferential framework are flexible and extremely rich in its capabilities to accumulate various scientific hypotheses and assumptions. The spatial and spatial temporal epidemiology is concerned with the description and analysis of spatial and spatial temporal variations in disease risk with respect to risk factors. As the primary aim of this work is to quantify the spatial disease pattern of dengue incidences apart from the mapping of disease modelling the disease and finding spatial clusters/hotpots is one important aspect in epidemiology is to find the temporal trends in or outside of clusters. In this study, a spatial-temporal trends model is fitted using the Leroux CAR priors set up for studying the spatial-temporal disease patterns with the estimation of the temporal trends with reference to dengue incidences in Tamil Nadu, India. 2025, Gnedenko Forum. All rights reserved. -
A Predictive Framework for Sustainable Human Resource Management Using tNPS-Driven Machine Learning Models
This study proposes a predictive framework that integrates machine learning techniques with Transactional Net Promoter Score (tNPS) data to enhance sustainable Human Resource management. A synthetically generated dataset, simulating real-world employee feedback across divisions and departments, was used to classify employee performance and engagement levels. Six machine learning models such as XGBoost, TabNet, Random Forest, Support Vector Machines, K-Nearest Neighbors, and Neural Architecture Search were applied to predict high-performing and at-risk employees. XGBoost achieved the highest accuracy and robustness across key performance metrics, including precision, recall, and F1-score. The findings demonstrate the potential of combining real-time sentiment data with predictive analytics to support proactive HR strategies. By enabling early intervention, data-driven workforce planning, and continuous performance monitoring, the proposed framework contributes to long-term employee satisfaction, talent retention, and organizational resilience, aligning with sustainable development goals in human capital management. 2025 by the authors. -
Impact of Endothelial Cell Repair Mechanisms on Doxorubicin-Induced Cardiomyopathy: Exploring Molecular Docking and Simulation studies of Angiogenic Factors
Doxorubicin (Dox), despite being an effective anti-cancer drug, also causes cytotoxicity to noncancerous tissues. ECs are highly abundant in the heart; thus, endothelial dysfunction is a major cause of doxorubicin-induced cardiomyopathy. The release of EPCs triggered by endothelial dysfunction, participates in the growth of new blood vessels and the repair of damaged endothelium to promote repair mechanism. The current study aimed to evaluate the effects of doxorubicin on SDF1/CXCR4 pathway via in silico molecular docking and simulation studies. Remarkably, heparin binding site of SDF1at LEU: 29 might be preoccupied by doxorubicin leads to poor expression because SDF1 activity heavily depends on its binding sites. On the other hand, active sites of CXCR4 at ASP: 171 and GLU: 288 also engaged by dox, leading to the assumption that doxorubicin restrict the receptor activity. Additionally, the interaction of doxorubicin at the proton acceptor site and ATP binding sites of VEGFR1, including ASP: 1022, GLY: 836, ALE: 837 and PHE: 838, suppresses the function of the receptor in the MAPK1/ERK2 and AKT1 signaling cascades. The co-expressing factor involved in SDF1/CXCR4 like VEGFR2, ANGPT1 and SHIP2 were also affected by specific amino acid interactions. This induces alterations in several vital biochemical pathways, leading to metabolic chaos. Taken together, it is hypothesize that doxorubicin-mediated functional inactivity of SDF1 via its receptor CXCR4 and VEGFR1 impaired the cardiac EPCs regulation on angiogenesis and vascularization. (2025), (DergiPark). All rights reserved. -
A continuous protocol for the epoxidation of olefins, monocyclic terpenes, and Alpha Beta Unsaturated Carbonyl Synthons using eco-friendly Flow Reactor Conditions
Herein, we report a simple synthetic protocol for selective epoxidation of olefins, monocyclic terpenes, and chalcones using a continuous semi-batch process in good to excellent yields. Mainly, industrial semi-batch epoxidation is an extremely risky process that includes very high safety measures to avoid the accumulation of peroxide species in the reactor during the process, which leads to accidents. To avoid the same, we have established a constant flow reactor protocol for the epoxidation of fore mentioned key synthons using a cyanamide-potassium carbonate catalytic system which helps to reduce the accumulation of the peroxide species, and also yields moderate to high yields of the desired products. The developed methodology was successfully utilized for the epoxidation of a range of aliphatic to aromatic olefins to generate corresponding epoxides. All the products and their structures were examined using 1HNMR, and 13NMR spectroscopy. More importantly, this proposed protocol is recyclable and reproducible where in using similar research conditions. 2022 The Authors -
Utilization of aluminum dross: Refractories from industrial waste
Aluminum oxide (Al2O3) and Magnesium-Aluminum oxides (MgAl2O4) are well known refractory materials used in engineering industries. They are built to withstand high temperatures and possess low thermal conductivities for greater energy efficiency. Dross, a product/byproduct of slag generated in aluminum metal production process is normally comprised of these two oxides in addition to aluminum nitride (AlN). Worldwide, thousands of tons of aluminum dross are generated as industrial wastes and are disposed of in landfills causing serious environmental hazard. This paper explores the potential to synergize the characteristics of the favourable contents of aluminum dross and its availability (in tons) via synthesis of refractories and thereby develop a value added product useful for the modern industries. In this work, Al-dross as-received from an aluminum industry which comprised of predominantly Al2O3, MgAl2O4 and AlN, was used to develop the refractories. AlN possesses high thermal conductivity values and therefore was leached out of the dross to protect the performance of the developed refractory. The washed dross was calcined at 700 and 1000C to facilitate gradual elimination of the undesired phases and finally sintered at 1500C. The dross refractory pellets were subjected to thermo-physical and structural properties analysis: XRD (structural phase), SEM (Microstructure), EDS (chemical constituents) and thermal shock cycling test by dipping in molten aluminum and exposing to ambient (laboratory). The findings include the favourable prospects of using aluminum dross as refractories in metal casting industries. Published under licence by IOP Publishing Ltd. -
Influence of nano ?-Al2O3 as sintering aid on the microstructure of spray dried and sintered ?-Al2O3 ceramics
Alpha Alumina (?-Al2O3) has traditionally been sintered to near theoretical density by employing variations in raw material properties, particle sizes, grinding methods, compaction pressures, sintering aids or minor quantities of additives and sintering temperatures. All these parameters directly influence the grain growth morphology and microstructure of the sintered alumina ceramic characteristics. Growth of large grained microstructure facilitated by fine grinding of raw material and coalescence of the grains enhanced by dopant additions are well researched. The maximum sintered density and strength of the fired body could be attained through large grained microstructure which include near spheroidal grains. Most of the final sintering is accomplished via additions of suitable aids which also may be promoted by liquid-phase sintering which is considered highly advantageous compared to solid-state sintering for products in many defense applications. In this paper the influence of nano ?-Al2O3 (<100 nm particle size) as sintering aid to obtain the desired microstructure in sintered micron sized (1 to 5 m) ?-Al2O3 is being reported. 1.0 and 1.5 wt% nano ?- Al2O3 powder were spray dried with 99.0 and 98.5% ?-Al2O3 powder respectively, with polyvinyl alcohol binder, compacted into 10 mm dia and 5 mm thick pellets and sintered at 1450 C with 3 h soak time. In addition to the two different sintering aid additive percentages, other variables included are spray dried powders removed from (i) chamber and (ii) cyclone. The sintered ceramics were characterized for bulk density and fracture surface microstructure via SEM analysis. Nano alumina as sintering aid exhibited significant influence that included generation of microstructure with porosity, precipitation or liquid phase sintering. The study was limited to establishing the definitive role played by nano alumina to influence the sintering of micron alumina. 2022 -
Synthesis of high temperature (1150 C) resistant materials after extraction of oxides of Al and Mg from Aluminum dross
Aluminum Dross (Al-dross) is a well-known Industrial waste generated in an Aluminium industry from the melting of the metal itself. It gets made yearly in hundreds of thousands of tons worldwide, due to the wide use and demand of Aluminum in almost every industry. However, Al-dross is not completely a waste as it contains two compounds of interest, namely Aluminum Oxide (Al2O3) and Magnesium Aluminate (MgAl2O4). They are the basic compounds present in any refractory which are products featuring low thermal conductivity and high temperature shock characteristics in the order of 1000 C+. Thus, Aluminum Dross becomes a vital candidate to be considered for the extraction of the two of the aforementioned compounds. Recent studies have shown that Al-dross indeed can be used to extract Al2O3 and MgAl2O4. However, Al-dross also contains Aluminum Nitride (AlN) a compound that exhibits the exact opposite properties demonstrated by refractories. In addition to being technically unsuitable for use as refractory material, AlN also possesses another huge issue. When Al-dross is dumped into landfills, the AlN present in the dross combines with the moisture in the soil and is energized by geothermal heat which leads into an exothermic reaction, thereby releases highly toxic and health hazardous gases. Keeping the above techno-environment challenges in mind, prior to utilizing the beneficiated Al-dross in any industrial application, it is important to leach out the AlN from the dross in an environment friendly manner. This paper deals with the successive leaching of AlN from the Al-dross using two laboratory procedures. Sintered (to be added) pellets made out of the processed powder in the lab were subjected to analysis of structural phases and chemical constituents by employing XRD and EDS. Cyclic thermal shock test cycles were also carried out by subjecting the pellets to 1150 C and quenching in air alternately, to study the refractory characteristics. 2019 Elsevier Ltd. All rights reserved. -
A Model to Predict the Influence of Inconsistencies in Thermal Barrier Coating (TBC) Thicknesses in Pistons of IC Engines
LHR (Low heat Rejection) engines comprise of components that are modified with ceramic Thermal Barrier Coatings (TBC) to derive improvements in performance, fuel efficiency, combustion characteristics and life. In addition to engine parameters, the ability of TBCs (250 - 300?m thick) to function favorably depends on materials technology related factors such as surface-connected porosity, coating surface roughness, uniformity and consistency in coating thickness [1]. Right since the nineties, emphasis has been placed on the complexity of piston contours from a coating processing standpoint because the piston bowl geometry although appears simple, is actually quite complex. Robotic plasma gun manipulation programs have been developed to obtain uniform coating properties and thicknesses which are highly classified information. Thicker coatings offer better thermal insulation characteristics but in thickness deficient regions, TBCs may be as thin as ?30 microns. Applied via the 'line of sight' process, in the Atmospheric Plasma Spray System the coating thickness does not get developed adequately if the components comprise of contours with shadow regions. Thus the coating quality of a LHR engine heavily depends upon the shape of the engine components. This affects the barrier effects offered by the TBC and is reflected via generation of unwanted thermal gradients in the combustion chamber and on the external piston walls that adversely influence the engine performance. Extensive diesel engine cycle simulation and finite-element analysis of the coatings have been conducted to understand their effects on (a) diesel engine performance and (b) stress state in the coating and underlying metal substructure. Research work presented here involves the need and developmental efforts made via Computational Fluid Dynamics (CFD) to generate a model via ANSYS - Fluent simulation software that predicts the temperature gradient across TBCs of various ceramics and coating thicknesses. The geometric model was developed using the dimensions obtained using a CMM (Coordinate Measuring Machine) in Solidworks and the mesh was developed in Altair Hypermesh. The generated mesh consists of 221938 elements. Interfaces were created between the piston-bond coat-top coat surfaces. The Ansys-FLUENT CFD code solves the energy equation to find out the temperature drop in the piston for different combustion temperatures. Although most of the cavities presented are not rectangular, incompressible and steady laminar flow was assumed. The Semi-Implicit Method for Pressure-linked Equations (SIMPLE) was used to model the interaction between pressure and velocity. The energy variables were solved using the second order upwind scheme. In addition, the CFD program uses the Standard scheme to find the pressure values at the cell faces. Convergence was determined by checking the scaled residuals and ensuring that they were less than 10-6 for all variables. Two cases with combustion temperatures varying between 700 and 800 K were developed in Ansys FLUENT, wherein the thickness was deficient in the 'shadow' region. The model was validated via experimentation involving thermal shock cycle tests in prototype burner rig facility and measuring the temperature drop across the TBC as well. Non uniform coatings, leading to non-uniform drop in temperature across the thickness are most likely to affect the lubrication system of the engine and therefore the performance. Substantial efforts must be directed towards development of consistent and uniformly thick coatings for optimum performance of the LHR engine. 2017 Elsevier Ltd. All rights reserved. -
Synthesis of Value Added Refractories from Aluminium Dross and Zirconia Composites
Results of a developmental study on the potential to synthesize industrial grade refractories from aluminum dross with un-stabilized zirconia are reported. The merit of the developed product to perform as refractories suitable for use at or above 1000C was assessed by studying the thermo-physical behavior as per guidelines of ASTM and IS. Aluminum dross, an industrial waste (slag) is generated in several millions of tons in the production of Aluminum and is dumped into landfills, which releases poisonous gases like methane and ammonia upon contact with moisture present in the land and the heat generated by the earth, warranting stringent mitigation efforts. Rich in aluminum metal (?15%), ?-Al2O3 (7-15%), MgAl2O4 (10-15%) and AlN (20-30%), the general prime dross composition draws interest due to its abundance and presence of ?-Al2O3 and MgAl2O4, for the production of refractories with insulating, shock resistance and stability at high temperature (?1000C and above) characteristics. Nevertheless, presence of AlN, a good thermal conductor acts as a deterrent in the production of refractories. Aluminium dross (after leaching out AlN) was processed with un-stabilized zirconia (monoclinic ZrO2) to synthesize the refractory composites. Conventional process (calcination, ball milling, compaction and sintering (1550C/6 hrs)) was employed. Characterization involved thermal shock cycling (air quench at furnace ambient of 1000C and room temperature) to determine the number of shock cycles endured before failure. Structural phase analyses at various stages of processing were carried out by via XRD. Magnesia present in dross did not appear to stabilize either the tetragonal or cubic ZrO2. Microstructural and chemical composition studies were carried out via SEM and EDS. The favourable results confirm the viability of the process methodology. 2019 Elsevier Ltd. -
Plasma Sprayed Refractory Coatings from Aluminium Dross
Refractory coatings on metals offer a unique blend of chemical inertness, stability and mechanical properties at temperatures higher than what the metal can normally withstand. However, a balance must be struck with many factors: Thickness, adhesion, performance, durability, economy and suitability for specific end use requirements. The present-day technology requires the coating to give effective service under extreme temperatures while being environmentally friendly and be easily available. One application of refractory coating is in steel industry-pipe linings. This research works highlights the potential to use aluminum dross, an industrial waste material to generate refractory coatings, comprised of Al2O3 and MgAl2O4 after suitable processing. Al dross is a byproduct of the Aluminium smelting process which can be recycled mechanically to separate the residual Aluminium metal from the Aluminium oxide. These are usually produced in tones every year and are found to be dumped in landfills and other empty spaces which generate toxic fumes like methane and other gases when reacted with moisture. The Aluminium dross used in this work was analyzed and found to comprise of its usual constituents such as metallic Al, MgAl2O4, Al2O3, AlN and other oxides and nitrides in minute quantities. Manual procedures were conducted to synthesize plasma spray-able dross which was further introduced to standard laboratory tests for the removal of undesirable constituents like AlN and other nitrides which led to the optimization of quality of powders. Atmospheric plasma spray (APS) coating methodology was used to deposit 250?m thick coatings of re-processed Al dross, involving the spraying of the processed powder onto a bond coated (NiCrAlY) steel substrate. The raw, reprocessed and the plasma sprayed coated Al dross were evaluated for their material characteristics by employing X-ray Diffractometry (XRD) for crystal structural phases, microstructure and chemical composition by employing sophisticated microscopy (SEM) technique and EDS associated with the SEM. The paper is presented keeping in in view the aptness of reprocessed Al dross, an industrial waste material to be utilized as refractories for use in engineering industries. 2019 Elsevier Ltd. -
Residual stress analysis on functionally graded 8% Y2O3-ZrO2 and NiCrAlY thermal barrier coatings
Thermal Barrier Coatings (TBCs) protect metallic components that operate in high temperature environments and enhance their service life. The conventional two-layered TBC system consists of a duplex ceramic top coat (TC) fabricated from 8 wt% yttria stabilized zirconia (8-YSZ) and an underlying bond coat (BC) comprised of intermetallic layers such as NiAl or MCrAlY (M = Co, Ni) etc. In the present study, functionally graded material (FGM) TBCs were fabricated by introducing a third blend layer of 8-YSZ and NiCrAlY, in between the BC and TC in order to enhance the thermal fatigue life of the TBC. The blend layer in FGM TBCs provides a smoother transition in thermal expansion properties between the metallic substrate and the top ceramic coat (8YSZ) which have widely different thermal expansion characteristics compared with each other. In service, thermal fatigue introduces severe tensile stresses between the coated layers and the substrates leading to ultimate detachment of the coatings from the substrates. In this work, residual stress analysis by Cos ? method was carried out as a non-destructive assessment tool to foresee the likelihood of onset of failure in the TBCs, well before the damage was visible. The two-layered (conventional) and three-layered (FGM) TBCs were synthesized on Inconel 718 substrates by atmospheric plasma spray (APS) technique. The TBCs were subjected to thermal fatigue tests between 1200? (by using gas flame) and ambient temperature and evaluated for residual stress analysis at different stages of thermal fatigue testing. The goal was to assess if residual stress analysis could be used to determine if the TBC was about to fail well before the delamination occurred and the catastrophic failure could be avoided. The tests conducted and results obtained are presented. 2022 -
Nano A-AL203 particles agglomerated by spray dying to produce free following powders suitable for /
Patent Number: 202141035836, Applicant: Gowtham Sanjai.
This invention discloses a method to produce free flowing plasma sprayable powders comprised of nano a-Alumina grains, suitable for injection into the high temperature plasma stream of an atmospheric plasma spray system. The plasma sprayable powder particles will be in the range of 30-90 microns, but the grains within the powder particles will only be about 50 nanometers. This micron sized powder, when flowing through the high temperature plasma will dissociate to release the nano grains, resulting in 12 to 15 microns thick coatings, deposited on the substrate per individual pass. -
A process to beneficiate A-Alumina and magnesium aluminate composite powder /
Patent Number: 202141035837, Applicant: Parvati Ramaswany.
An environmentally friendly process to beneficiate a-alumina (AI2O3 - corundum) and magnesium aluminate (MgAbOj - spinel, linear formula: MgOAbOj) ceramic composite powder, from black aluminum dross (an industrial waste), has been disclosed. The process involves grinding of the Al-Dross, leaching of the undesirable compounds (A1N) by using hot carbonated water, dehydration and calcination, wherein the ammonia gas (whenever it was evolved) was scrubbed through dilute H2SO4.