Volume 12 | Issue 5 |

 Abstract

Wire Electrical Discharge Machining (EDM) has emerged as a prominent machining process due to its capability to fabricate intricate shapes on conductive materials with high precision. This research focuses on investigating the machinability of Aluminum Alloy 6061 using Wire EDM and optimizing the process parameters to enhance machining efficiency and surface quality. The study begins with a comprehensive analysis of the machinability characteristics of Aluminum Alloy 6061, including its mechanical properties, thermal conductivity, and electrical conductivity. Experimental trials are conducted to evaluate the influence of key process parameters such as pulse-on time, pulse-off time, wire tension, and flushing conditions on material removal rate (MRR), tool wear rate (TWR), and surface roughness. Through systematic experimentation and statistical analysis, optimal process parameters are determined to maximize MRR while minimizing TWR and surface roughness. Advanced techniques such as response surface methodology (RSM) and Taguchi method are employed to establish mathematical models correlating process parameters with machining performance indicators. Furthermore, the study explores the effects of different dielectric fluids, electrode materials, and wire diameters on machining characteristics to identify the most suitable combinations for achieving superior machining outcomes. Additionally, the environmental impact and economic feasibility of the optimized Wire EDM process are assessed. The findings of this research contribute to enhancing the understanding of Wire EDM of Aluminum Alloy 6061 and provide practical insights for optimizing the machining process in industrial applications. By improving machining efficiency and surface quality, the proposed methodology can potentially lead to significant advancements in the manufacturing of precision components using Aluminum Alloy 6061.

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 Keywords

Wire Electrical Discharge Machining (EDM) ,Aluminum Alloy 6061 ,Optimization , Machining Parameters , Surface Quality

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 Abstract

Wire electric discharge machining (WEDM) is a crucial non-traditional method of machining. It is primarily utilized for the machining of hard metals and materials that prove challenging to machine using traditional processes. This makes WEDM a valuable tool in aerospace, automotive and tooling applications. The goal of this research paper is to identify the most suitable set of process parameters, including pulse on time (Ton), voltage, and current in the electric discharge machining (WEDM) process to achieve higher material removal rate, improved surface finish. Taguchi method is used to get the optimized result. The Taguchi Method offers a systematic approach to identify and optimize these parameters to enhance machining efficiency and quality. EN31 alloy steel material is used as a workpiece and the cutting wire is made up of Brass with 0.25 mm diameter.

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 Keywords

Wire Electric discharge machining, EN31 alloy steel, pulse on time (Ton), voltage, current, material removal rate, surface finish, Taguchi method

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 Abstract

This work proposes Automated Thread Error Detection, a system employing machine learning to examine program execution traces to discover threading issues such as data races and deadlocks. distinguishes between harmless and serious faults and has scalability and adaptability. Automated Thread Error Detection appears to be a promising solution to improve software quality and productivity through experiments and case studies, as it demonstrates superior performance in identifying threading errors the accuracy statistic shows the proportion of properly predicted labels. In this instance, the model's accuracy was 92.45%, meaning that 92.45% of the test dataset's samples were properly identified. High accuracy shows that the model can generalize well to new data and has picked up useful patterns from the training set.

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AUTOMATED THREAD ERROR DETECTION

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The growing need for food is causing a rise and expansion in the process of changing farming and agricultural production.Its dependability for farmers will be increased by using IOT in agricultural and crop production, which is an urgent need. Increasing crop productivity is necessary due to the rapid rise of the population. For crops to be more productive, the amount of N (nitrogen), P (phosphorus), and K (potassium) in the soil is essential. The minerals in the soil allow us to determine how quickly the crops absorb nutrients. The minerals in the soil allow us to determine how quickly the crops absorb nutrients. The lack of nutrients causes a decline in agricultural yield.The phrase "macro nutrients" describes the primary nutritional needs for necessary plant growth. But a proper fertilizer balance is required for higher development. The industry's current problems can be solved by smart farming, which places a focus on encouraging creativity and innovation in agricultural and agronomic production. IOT devices and sensors can also be used for that. Agricultural laborers could easily keep an eye on their harvests, crops, or fields and gather the relevant data and information.

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Automated Fertigation System

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In the pursuit of sustainable and intelligent building solutions, this abstract introduces an Automatic Light System (ALS) empowered by a microcontroller to revolutionize lighting control. The proposed system employs advanced sensors and microcontroller logic to create a responsive and energy-efficient lighting environment. The Automatic Light System utilizes ambient light sensors to constantly monitor the natural light levels in a given space. The smicrocontroller processes this real-time data and dynamically adjusts artificial lighting accordingly, ensuring optimal illumination while minimizing energy consumption. The system is designed to seamlessly transition between different lighting scenarios, such as daylight harvesting and task- specific lighting, providing a comfortable and visually optimized atmosphere for occupants. This research presents a holistic approach to intelligent lighting control, addressing both energy efficiency and user satisfaction. The microcontroller-based ALS offers a versatile solution applicable to various settings, including residential, commercial, and industrial spaces.

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Intelligent Lighting Control, Ambient Light Sensors, Energy Efficiency, Responsive Lighting, User Preferences, Building Automation, Energy Conservation

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In order to facilitate power transfer between the electric vehicle (EV) and the grid while the vehicle is operating, this paper suggests a dynamic wireless charging system for EVs. The system makes use of an RFID-based method as well as inductive power transfer (IPT) based on the resonance principle. The design and analysis of the wireless charging system, including the hardware prototype's implementation, the computation of the self and mutual inductances, and the modeling and simulation of the power transfer efficiency, are presented in this paper. The advantages, disadvantages, and potential applications of wireless charging technology for EVs are also covered in the paper. The suggested system is a workable, secure, and clever way to charge EVs.

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Design and analysis, RFID technology, Power transfer, A dynamic wireless charging system, EVs.

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 Abstract

Driver weakness may be a noteworthy calculate contributing to street mischances around the world. To address this issue, we show the plan and execution of an Hostile to- Rest Alert Framework (ASAS) pointed at anticipating mishaps caused by tired driving. This extend presents a novel approach that combines real-time checking of driver's physiological signals with progressed flag preparing strategies to distinguish signs of weariness and trigger opportune cautions. The ASAS utilizes non-intrusive sensors, such as electroencephalography (EEG) and electrooculography (EOG), to persistently screen the driver's brainwave designs and eye developments. These physiological signals are prepared utilizing machine learning calculations to recognize designs related with laziness and microsleep scenes. The framework utilizes a wearable gadget coordinates into a driver's headset, making it comfortable and helpful for amplified utilize. Upon identifying signs of weariness, the ASAS enacts an caution component that incorporates sound-related cautions, situate vibrations, and visual notices on the vehicle's dashboard. This multi-modal alarm framework guarantees that the driver is instantly informed, permitting them to require fitting activities to remain alert and caution. Besides, the framework can be customized to adjust its affectability based on person driver characteristics and inclinations. The equipment engineering consolidates low-power microcontrollers and wireless communication modules, making the framework energy-efficient and competent of genuine- time information transmission to a companion versatile application. The versatile app gives extra highlights such as real-time checking of the driver's physiological state, authentic information examination, and personalized weakness chance appraisal.

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vehicles, from individual cars to commercial transport, contributing to more secure streets and more watchful drivers.

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The Data Sync Hub project explores the intricate landscape of data management, storage, and security. It begins with an overview of the historical significance of data centers, emphasizing their central role in modern technology infrastructure. The project underscores the importance of secure and efficient data management, highlighting technical components such as a comprehensive block diagram illustrating the network architecture. A central focus is on unified storage, which provides file-based and block-based storage services across various protocols and devices. Security and real-time data synchronization are key considerations, with robust measures including access controls, authentication, encryption, and user isolation ensuring data integrity and confidentiality. The integration of LDAP and authentication tokens underscores the commitment to safeguarding sensitive information. Additionally, the project emphasizes real-time synchronization for seamless data propagation and high availability, enhancing user experience and minimizing disruptions. Versioning and conflict resolution mechanisms further reinforce data consistency and integrity, promoting a reliable and resilient data ecosystem. In conclusion, the Data Sync Hub project offers a comprehensive approach to data management, integrating technical excellence with security measures to meet the evolving needs of modern enterprises. With a focus on scalability, user education, and technical support, it presents a comprehensive solution to optimize data operations and drive business success.

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NAS Storage, Storage Clusters, Replication, Cloud Storage, Redhat Linux

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Safety is a paramount concern across all sectors, with fire safety measures becoming essential in every environment. Particularly in industrial zones, fire incidents are prevalent and often result in significant harm to both human lives and property. This review examines the primary causes of these fire incidents and evaluates the technological solutions currently in place to mitigate them. Presently, many safety protocols, enhanced by the Internet of Things (IOT), are implemented post-incident initiation. However, the system proposed in this paper focuses on preemptive fire safety protocols specifically designed for the fireworks industry. By identifying and addressing the foundational causes of fires, the system aims to prevent fire incidents before they occur. Such proactive measures could significantly reduce hazardous incidents and save numerous lives.

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Internet of Things (IoT), fire incidents, preemptive safety.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Diabetic retinopathy (DR) is a significant consequence of diabetic condition (Hyperglycemia) and a primary factor in visual deficiency globally. Prompt identification and timely intervention play a pivotal role in averting vision impairment in individuals with diabetes. Lately, progress in deep learning techniques, notably Convolutional Neural Networks (CNNs), has showcased significant successes in various medical image analysis tasks, including the detection of diabetic retinopathy. This paper introduces an innovative method for automatically detecting diabetic retinopathy utilizing CNNs. Our proposed method involves preprocessing retinal fundus images to enhancing image contrast and reducing noise, followed by feature selection using a pretrained CNN architecture. The extracted features are then fed into a classification model for the recognition of Retinal disease in diabetes. We utilize a voluminous dataset of annotated retinal visuals to train & validate our CNN-based detection system, ensuring robust performance across diverse clinical scenarios. Experimenting the outcomes that showcase the efficiency of our approach in precisely achieving the desired results in detecting diabetic retinopathy, achieving cutting edge performance in sensitivity, specificity, and overall accuracy. Moreover, the proposed method exhibits robustness to variations in image quality and pathological characteristics, making it suitable for real-world clinical applications. In summary, our research underscores the promise of leveraging deep learning, particularly CNNs, as an invaluable resource in the prompt identification and treatment of diabetic retinopathy. Our proposed model offers potential for seamless integration into current healthcare infrastructure, enabling timely detection and intervention strategies to mitigate vision loss in individuals with diabetes.

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 Keywords

Diabetic Retinopathy, Convolutional Neural Network (CNN), Detection, Machine Learning

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Creative Commons Attribution 4.0 and The Open Definition