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Experimental study of response parameters during machining of Inconel 718 with cryogenically treated ceramic round tool using cutting fluid
Highly advanced superalloys are being rapidly spreading throughout the globe. It's in need of the hour to produce similar materials which are being used in several industries similar as petrochemical, biomechanical, aerospace and marine industries. Inconel 718 is one similar superalloy which is being used due to its better characteristic features like high attrition resistance, high temperature burden conditions, thermal fatigue resistance, and cryogenic temperatures. Owing to the hardness conditions, tools indicate the low tool life and high wear characteristics. Ceramic insert is one such tool that is being used to machine Inconel 718 which is cryogenically treated to improve tool life. The use of emulsified cutting fluid reduces tool wear and improve durability of the tool, thereby improving the efficiency of the machining of Inconel 718. In this paper, experimental investigation has been carried to find the use of emulsified cutting fluid that improves the machinability of Inconel 718 based on parameters such as surface roughness and tool wear under the effect of cutting parameters which are cutting speed, feed rate and depth of cut. 2021 Elsevier Ltd. All rights reserved. -
Parametric analysis of control parameters for investigating the machinability of inconel 718 using ceramic inserts of round type
Inconel 718 is a nickel-chromium based super alloy and has high corrosion and thermal resistance, high hardness, and high thermal strength at elevated temperatures which makes it difficult to cut. Due to these mechanical properties, it is being used in toughest conditions and hence the tool life is extremely short. This hard to cut metallic alloy has a wide scope in the field of bio medical industry, aerospace industry, bearing industry, steam turbine and nuclear applications and the demand has rapidly been increased in the recent years. Ceramic insert is one such cutting tool being used in the machining of this metal and study is still being conducted to increase the machinability. This paper investigates the machinability characteristics for determining the machinability of Inconel 718 using ceramic insert based on Grey Relation Analysis (GRA) and signal to noise (S/N) ratio. The input parameters such as feed rate, cutting speed and depth of cut are taken into consideration to obtain the suitable response parameters such as minimal surface roughness and low tool wear rate to improvise the machining characteristics of this superalloy. Ceramic inserts had even been cryogenic treated to provide better machining conditions on the Inconel 718. 2023 Author(s). -
The effect of cutting fluid in improving the machinability of Inconel 718 using ceramic AS20 tool
Industries demand a vast usage of superalloys in heat resistant and high temperature applications. These include nozzle of rocket fuel engines, throttle valve of turbojet engines, turbine blade discs of aerospace industries, rivets and fittings of chemical and production industries, biomedical applications in super strength resistive steels. These superalloys such as Inconel 718 finds its vast applications in all such industries. To machine such materials a lot of wear and tear occurs at the cutting tool. Hence, cutting fluid helps in reduction of tool wear and improving surface roughness. In this paper, two cutting fluids Koolkut 40 and Hicut 590 have been used in emulsified form during the machining of Inconel 718 with Ceramic cutting tool. Hicut 590 has been seen a better heat resistive cutting fluid in reducing the tool wear and thus improving the life of the tool. 2021 Elsevier Ltd. All rights reserved. Selection and Peer-review under responsibility of the scientific committee of the Global Conference on Recent Advances in Sustainable Materials 2021. -
An Investigation to the Hardness of the Cutting Tool During Machining Inconel 718 due to the Cryogenic Effect
The machining of superalloy Inconel 718 has seen a rapid demand in industries due to the superiority factor of its composition which makes it corrosion resistant, wear resistant and abrasive resistant. Due to these advanced features of this alloy, the cutting tool to be used to machine becomes a challenging one. There have been several cutting tools being used in the machine but wear of the tool and high surface roughness has been observed. Two cutting tools Tungsten Carbide RYMX 1004-ML TT3540 and Ceramic AS20 has been identified but the hardness on it is failed due to the machining conditions. The cryogenic treatment of these tools can see a remarkable change in machining and bring low surface roughness and reduce tool wear. 2023 American Institute of Physics Inc.. All rights reserved. -
Geometry of Variably Inclined Inviscid MHD Flows
A steady plane variably inclined magnetohydrodynamic flow of an inviscid incompressible fluid of infinite electrical conductivity studied. Introducing the vorticity, magnetic flux density, and energy functions along with the variable angle between magnetic field and velocity vector, governing equations are reformulated. The resulting equations are solved to analyze the geometry of the fluid flow. Considering streamlines to be parallel, stream function approach is applied to obtain the pattern for magnetic lines and the complete solution to the flow variables. Next considering parallel magnetic lines, magnetic flux function approach is applied to obtain streamlines and the complete solution of the flow. A graphical analysis of pressure variation is made in all the cases. 2020, Springer Nature Singapore Pte Ltd. -
Marketing Research and Market-Focused Production as an Effective Business Tool in Power Sector
Businesses must devote part of their resources to conducting market and marketing research to make good decisions, which will help expand any business and utilize resources effectively. Understanding the intended clients is essential to successfully operating and expanding a firm. For marketers to comprehend consumer value about the product being supplied and therefore add value to their consumers, it is crucial to have this understanding. Organizations can better influence customers to buy niche goods or corporate services after thoroughly understanding their objectives, requirements, and values. In this situation, it is required to restructure the physical system and the related control and planning systems to provide production the tools it needs to become more competitive and customer-focused, acting as a positive and active production process instead of a reactive one. One of the finest techniques for understanding consumers is market research. It provides basic information that a company may utilize to inform its marketing strategy, facilitating and enhancing sales and marketing. This paper reviews the impact of effective market and marketing research and market-focused manufacturing in the power sector. 2023 IEEE. -
Determining the Antecedents and Consequences of Brand Experience: A Study to develop a Conceptual Framework
In the marketing literature, one of the most talked- about subjects is brand experience (BE). Through an examination of the numerous studies conducted by BE researchers, this report attempted to determine the significance of BE in the body of recent literature. This paper culminates in the creation of a conceptual framework that prospective investigators might utilize to discern the diverse pathways inside BE. 2024 IEEE. -
Impact of Digital Media Marketing on Consumer Buying Decisions
Digital Marketing has become one of the most discussed topics in the field of management in the recent past. With the advent of social media, digital marketing has even garnered more attention. It has directly or indirectly influenced the buying behaviour of the customers also. This paper has tried to understand the impact of digital marketing in influencing the impulsive buying behaviour of the customers. 2024 IEEE. -
Role of Artificial Intelligence in Influencing Impulsive Buying Behaviour
This research paper investigates the influence of Artificial Intelligence (AI) on impulsive buying behaviour in the digital commerce domain. The study explores how AI algorithms, data analysis, and customized marketing approaches influence impulsive buying decisions, reshaping traditional understandings of this phenomenon. The analysis draws from a confluence of psychological principles, technological advancements, and marketing strategies, aiming to shed light on how AI not only forecasts but also incites impulsive buying behaviours. The study identifies research gaps, such as the integration of AI with emotional triggers, the comparative effectiveness of AI vs. human influence, and cross-cultural and demographic variability. The research methodology involves a descriptive study with a questionnaire-based survey, and data analysis tools such as ANOVA and paired t-tests. This research contributes to the broader discussion on digital-age consumer behaviors, underscoring the revolutionary role of AI in transforming retail experiences and beyond. 2024 IEEE. -
Understanding the use of Regression Analysis in Business Analytics to understand the perceptions of Students about Quality in Higher Education
For a very long time, researchers in a variety of fields have utilized regression analysis as a crucial tool for data analysis and result interpretation. Regression analysis has also been widely employed in the business world to determine what factors influence consumers' decisions to purchase any of the company's products. Comprehending the interplay of these variables will enable the business to conduct a more thorough consumer analysis and boost sales. This essay is an attempt to comprehend students' perceptions on the qualities they consider important while applying to universities. Regression analysis is another approach used in this article to determine how the quality criteria affect the respondents' overall happiness. 2024 IEEE. -
An Empirical and Statistical Analysis of Classification Algorithms Used in Heart Attack Forecasting
The risk of dying from a heart attack is high everywhere in the world. This is based on the fact that every forty seconds, someone dies from a myocardial infarction. In this paper, heart attack is predicted with the help of dataset sourced from UCI Machine Learning Repository. The dataset analyses 13 attributes of 303 patients. The categorization method of Data Mining helps predict if a person will have a heart attack based on how they live their lives. An empirical and statistical analysis of different classification methods like the Support Vector Machine (SVM) Algorithm, Random Forest (RF) Algorithm, K-Nearest Neighbour (KNN) Algorithm, Logistic Regression (LR) Algorithm, and Decision Tree (DT) Algorithm is used as classifiers for effective prediction of the disease. The research study showed classification accuracy of 90% using KNN Algorithm. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Automated Verification of Open/Closed Principle: A Code Analysis Approach
The SOLID principles are foundational to software engineering, focusing on the maintainability, scalability, and extensibility of software systems. The Open/Closed Principle (OCP), a pivotal element among these principles, underscores the need to design software modules that are open for extension yet closed for modification. This research explores automated verification techniques for OCP, addressing the validation of software modules through extensibility and adaptability assessments. The principal objectives involve the development of a code analysis approach and a methodology capable of automating the verification of adherence to OCP in developed codes, providing actionable insights to software developers. The system focuses on specific aspects of OCP, including inheritance, abstraction, and polymorphism, and aims to provide clear indications of where violations occur within a codebase. The implementation uses the Abstract Syntax Tree (AST) analysis to examine class definitions. The automated analysis of Python code using the defined rules offers a clear understanding of OCP adherence. Results are presented in Pandas DataFrames, indicating potential violations and providing developers with actionable insights to enhance code quality and maintainability. Overall, the automated code verification system aims to enhance code quality and adherence to fundamental design principles, paving the way for advancements in automated code analysis and software engineering practices. 2024 IEEE. -
Performance Evaluation and Comparison of Various Personal Cloud Storage Services for Healthcare Images
In recent times, usage of personal cloud storage services for storing e-health records in on a rise. This is due to the constant accessibility, easy sharing, and safe storage of the data at a nominal cost. In this paper, we have analyzed the performance of four personal cloud storage services: Google Drive, Dropbox, Sync.com, and Icedrive using medical image data files of various sizes. The parameters checked were number of packets transmitted during file upload and duration of time to upload, download, and delete the files. The results show us a comparative analysis of the personal cloud storage services based on the parameters and also help us identify certain gaps for the future. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Blockchain Scalability: Solutions, Challenges and Future Possibilities
In recent years, blockchain has received a lot of interest and has also been widely adopted. Yet, blockchain scalability is proving to be a difficult problem. To create a new node in platforms like Bitcoin takes few days of time. This scalability problem has few proposed solutions. The present alternatives to blockchain scalability are divided into two groups in this paper: first layer and second layer techniques. Second layer solutions suggest procedures that are deployed outside of the blockchain, while first layer methods propose adjustments to the blockchain (i.e., altering the blockchain design, such as block size). We concentrate on sharding as a viable first-layer solution to the scalability problem. The thought behind sharding is to split the blockchain network into numerous groups, each processing a different set of transactions. Furthermore, we compare few of the already available sharding-based blockchain solutions and present a performance-based comparative analysis in form of the benefits and drawbacks of the existing solutions. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Electric Vehicle Traction Motor Hardware in Loop (HIL) Regulation for Adaptive Cruise Control Scenario
This paper aims at developing a adaptive cruise control system using model predictive algorithm which operates on a Software-in- loop system. The vehicle modelling performed in IPG Car Maker operates with a Matlab based Model Predictive Controller at the back end. The Model Predictive Controller works on the relative distance between the leader vehicle and the ego vehicle. The primary focus is on optimizing the ACC performance to enhance energy efficiency, taking into account the specific dynamics of electric power trains. The study places particular emphasis on the integration of IPG Car Maker software to provide a realistic and dynamic simulation environment, enabling the evaluation of the proposed ACC-MPC system under an urban driving scenario and environmental conditions. 2024 IEEE. -
Machine Learning Techniques in Predicting Heart Disease a Survey
The heart serves an important role in living creatures. Diagnosis and forecast of cardiac illnesses demand greater precision, perfection, and accuracy because such tiny mistakes can lead to weariness and death. Numerous heart-related deaths have occurred, and the incidence rates have been rising over time. Predicting the development of heart disorders is important to work in the medical industry. Every month, many databases related to the patient are kept. The information gathered can be used to predict the occurrence of future diseases. This article gives an outline of cardiovascular diseases and modern treatments. Also, the focus of this research is to outline some current research on applying machine learning techniques to predict heart disease, analyze the many machine learning algorithms employed, and determine which technique(s) are useful and efficient. Artificial neural network (ANN), decision tree (DT), fuzzy logic, K-nearest neighbor (KNN), Naive bayes (NB), and support vector machine (SVM) are data mining and machine learning approaches used to predict cardiac disease. This paper includes an overview of the present method based on features, the algorithms are compared, and the most accurate algorithm is analyzed. 2022 IEEE. -
Mobile Freeze-Net with Attention-based Loss Function for Covid-19 Detection from an Imbalanced CXR Dataset
In this paper, we present a novel framework, that is, Mobile Freeze-Net along with Attention-based Loss Function, for Covid-19 detection from a Chest X-Ray (CXR) dataset. First, we have observed that by freezing 50% of a Mobile Net-V2 model (means fine-tuning 50% layers from ImageNet dataset) has automatically removed the class imbalance problem from the CXR dataset considerably. We call this 50% frozen Mobile Net-V2 model as Mobile Freeze-Net. Secondly, we have proposed an Attention-based Loss function, which provides more attention to the class, having higher inter-class similarity. We have computed attention weights for each class from the statistical inference of the dataset itself, by employing a Monte-Carlo method and thereafter, we have incorporated those weights into WCCE loss function of Mobile Freeze-Net model. By utilizing Mobile freeze-Net, we have achieved testing accuracy, F1 score, precision and recall of 93%, 94%, 93% and 94% respectively. This is approximately 3-4% improvement compared to 100% fine tuning of Mobile-Net V2. Furthermore, we have achieved approximate 1-2% improvement of Mobile Freeze-Net, after incorporating Attention-based Loss function. For the validity of the proposed framework, we have conducted experiments with 10-fold cross validation. All these experimental results suggest that our proposed framework has outperformed other existing models considerably. 2023 Owner/Author(s). -
Log-Base2 of Gaussian Kernel for Nuclei Segmentation from Colorectal Cancer H and E-Stained Histopathology Images
Nuclei Segmentation is a very essential and intermediate step for automatic cancer detection from H and E stained histopathology images. In the recent advent, the rise of Convolutional Neural Network (CNN), has enabled researchers to detect nuclei automatically from histopathology images with higher accuracy. However, the performance of automatic nuclei segmentation by CNN is fraught with overfitting, due to very less number of annotated segmented images available. Indeed, we find that the problem of nuclei segmentation is an unsupervised problem, because still now there is no automatic tool available which can make annotated images (nuclei segmented images) accurately, to the best of our knowledge. In this research article, we present a Logarithmic-Base2 of Gaussian (Log-Base2-G) Kernel which has the ability to track only the nuclei portions automatically from Colorectal Cancer H and E stained histopathology images. First, Log-Base2-G Kernel is applied to the input images. Thereafter, we apply an adaptive Canny Edge detector, in order to segment only the nuclei edges from H and E stained histopathology images. Experimental results revealed that our proposed method achieved higher accuracy and F1 score, without the help of any annotated data which is a significant improvement. We have used two different datasets (Con-SeP dataset, and Glass-contest dataset, both contains Colorectal Cancer histopathology images) to check the effectiveness and validity of our proposed method. These results have shown that our proposed method outperformed other image processing or unsupervised methods both qualitatively and quantitatively. 2023 SPIE. -
Performance Improvement in E-Gun Deposited SiOx- Based RRAM Device by Switching Material Thickness Reduction
A performance improvement by reduction in switching material thickness in a e-gun deposited SiOx based resistive switching memory device was investigated. Reduction in thickness cause thinner filamentary path formation during ON-state by controlling the vacancydefects. Thinner filament cause lowering of operation current from 500 ?A to 100 ?A and also improves the reset current (from >400 ?A to <100 ?A). Switching material thickness reductionalso cause the forming free ability in the device. All these electrical parametric improvements enhance the device reliability performances. The device show >200 dc endurance, >3-hour dataretention and >1000 P/E endurance with 100 ns pulses. 2022 Institute of Physics Publishing. All rights reserved. -
Leveraging Deep Autoencoders for Security in Big Data Framework: An Unsupervised Cloud Computing Approach
Abnormalities recognition in bank transaction big data is the number one issue for stability of financial security system. Due to the rate digital transactions are increasing it is vital to have effective ways. Encryption with deep autoencoder model should be explored as it involves trained neural networks that learn such patterns from the complex transaction data. The following paper demonstrates application of anomaly detection using deep autoencoders in the banking big data transactions. It focuses on the theoretical bases, network design, preparedness and the testing measures for deep autoencoders. On the other hand, it solves problems such as high dimensionality and imbalanced dataset. This research paper shows deep autoencoders effectiveness in deep learning and how the network identifies different fraudulent big data transactions, money laundry and unauthorized access. It also encompasses recent developments of cloud environments and future methods using deep autoencoders including the fact that constant search for new possible solutions is a must. The insights delivered contribute to the discourse in financial security community, which incorporates researchers, practitioners, and policymakers involved in anomaly detection in cloud. 2024 IEEE.