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407 articles for “Dry machining”
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Analysing the Deep Hole Drilling Characteristics of AISI 316 Alloy Using Peck Drilling Approach
Abstract: This study aims at the deep hole drilling characteristics of AISI 316 alloy utilizing the Peck drilling procedure. It is well-established that hole is the most prevalent machining process, requiring precise techniques to achieve optimal cutting conditions. AISI 316 has high corrosion resistance and mechanical features. It is widely utilized in the aerospace, vehicle, aircraft, and other industries. Due to its high modulus of elasticity, reactivity at high cutting speeds, …
Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 14–28 Read article
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Designing of Shredder Machine for Recycling Post Consumer Denim Waste for Making Composite Denim Boards
Abstract: India has a population of about 1.35 billion in 2020 and as fabrics account for 4% of India's GDP, customers spend roughly 68 billion dollars on apparel and footwear each year. Currently, the short lifecycles of apparel products due to rapid fashion cycles and increased buying power of Indian consumers in urban as well as in rural areas are resulting in significant amounts of postconsumer textile waste in the form …
Published in Journal of Polymer & Composites · Vol. 13, Issue 1, 2025 · pp. 934–945 Read article
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Reviewing Threat Detection Methods in SaaS Platforms Through the Use of Adaptive Cloud Security Models
Abstract: Software as a Service (SaaS) solution has revolutionized the contemporary business processes as scalable and service-on-demand solution on cloud networks. Yet, this expansion has brought in sophisticated cybersecurity risks because of a multi-tenant environment facing the internet in the SaaS environment. The key to assure the service availability and protection of the data stored off-site is effective threat detection in such dynamic ecosystems. This review article seeks to discuss the …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 3, 2025 Read article
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A Review on Predicting Wear and Friction of PTFE Composites - Fillers to Machine Learning Models
Abstract: Polytetrafluoroethylene (PTFE) composites, a self-lubricating material with low friction, became an indispensable material in engineering applications where load carrying capacity and wear are crucial. The pure PTFE has poor mechanical strength and wear resistance which can be enhanced by the addition of fillers in appropriate volume fraction. The wear performance is dependent on various factors such as fillers, operating parameters, environmental conditions as well as manufacturing attributes. This makes the …
Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 114–128 Read article
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A Threshold-Weighted Mathematical Fusion Model for Epidemic Outbreak Prediction Using SEIR Residual Dynamics and Cloud-Based Machine Learning
Abstract: Accurate prediction of epidemic outbreaks is critical for effective public health management, resource planning, early warning generation, and timely intervention by municipal authorities. Traditional compartmental models such as Susceptible–Exposed–Infectious–Recovered (SEIR) offer valuable epidemiological insights and mathematical interpretability; however, they may not adequately capture the complex nonlinear relationships present in real-world urban health systems. Conversely, data-driven machine learning techniques can identify hidden patterns in large datasets but often lack epidemiological structure …
Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 2, 2026 · pp. 12–19 Read article
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Reinforcement Learning for Adaptive Sensing with Shape Memory Polymer-Based IoT Nodes
Abstract: The rapid expansion of intelligent sensing in the Internet of Things (IoT) has revealed the pressing need for materials and algorithms capable of self-adaptation in volatile environments. Conventional polymer-based sensors and static control strategies often fail to capture nonlinear thermo-mechanical dynamics, leaving them unsuitable for unpredictable operating conditions. Although prior studies have improved polymer composites or introduced algorithmic optimization independently, few attempts have coupled the adaptability of smart materials with …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 370–391 Read article
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Survey of Predictive Models for Safe Route Predicting Using Machine Learning Techniques
Abstract: Safe route prediction is essential for the well-being and security of individuals in urban and rural environments. Machine learning techniques leverage historical data, real-time information, and algorithms to estimate the safety levels of different routes. The objective of safe route planning is to minimize risks, including crime-prone areas and accidents, reducing potential harm, property damage, and emotional distress. However, challenges arise from the complex and dynamic nature of urban environments, …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 11, Issue 1, 2024 · pp. 13–22 Read article
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An Intelligent Modelling system for Automotive Vehicles
Abstract: In this paper it’s about the development of artificial intelligence that has fuelled technological advancements. Self-driving automobiles are an example of an innovative development. Nowadays, you may work or sleep in your car while driving to your destination without touching the steering wheel or accelerator. This project aims to create a workable model of a self-driving car capable of traveling on multiple tracks, including curved, straight, and straight followed by …
Published in Journal of Instrumentation Technology & Innovations · Vol. 15, Issue 1, 2025 · pp. 32–40 Read article
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Data-Driven Life Prediction of Fiber-Reinforced Polymer Composites Using IoT Sensing and Machine Learning Algorithms
Abstract: The accurate prediction of fatigue life in fiber-reinforced polymer (FRP) composites remains a major challenge due to their nonlinear, multi-mechanism degradation behavior under variable loading conditions. This study presents a data-driven framework, H-LiProNet, which combines real-time IoT sensing with hybrid machine learning to estimate remaining useful life (RUL) in FRP composites. The proposed system integrates embedded Fiber Bragg Grating (FBG) and acoustic emission (AE) sensors to capture strain and damage …
Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 116–130 Read article
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Develop a Data Science Approach for Optimizing Energy Consumption
Abstract: Optimizing energy consumption has become a critical challenge in the era of sustainability and increasing energy demand. Efficient energy management is essential to address environmental concerns, reduce costs, and ensure resource availability for future generations. This project leverages data science techniques to evaluate and improve energy consumption across diverse sectors, including residential, industrial, and commercial domains. By integrating advanced analytics, machine learning models, and real-time data processing, the project aims …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 1, 2025 · pp. 31–44 Read article
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Artificial Neural Network Modelling to Optimize Micro-Drilling Parameters of ECDM of Developed Novel Zn/(Ag+Fe)-MMC
Abstract: Several engineering fields have increased their use of metal matrix composites (MMCs) in the past few years. Due to the increase in composites, the demand for accurate machining has also become important. Specifically, pertaining to biomaterial applications, accuracy factor with desired surface finish is critical. While the near-net shape manufacturing process has advanced, MMCs frequently require post-mould machining to achieve surface quality, and dimensional tolerances. In the present study, a …
Published in Journal of Polymer & Composites · Vol. 11, Issue 1, 2023 · pp. 01–13 Read article
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Adaptive Drift Correction in Polymer-Based Wearable Biosensors via Data-Driven Signal Modeling
Abstract: Polymer-based wearable biosensors have emerged as a promising technology for continuous health monitoring due to their mechanical flexibility, biocompatibility, and suitability for long-term physiological interfacing. However, prolonged exposure to biofluids, environmental variability, and mechanical deformation introduces signal drift, which significantly degrades measurement accuracy and limits clinical reliability. This paper presents a data-driven methodology for compensating signal drift in polymer-based wearable biosensors using adaptive signal processing and machine learning techniques. The …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 131–139 Read article
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A Detailed Survey of Machine Learning Applications, Methods, and Future Prospects in Agriculture
Abstract: Agriculture is undergoing a digital transformation driven by machine learning (ML) and artificial intelligence. The integration of ML techniques with data from sensors, drones, satellites, and IoT devices has enabled precision agriculture, early disease detection, optimized resource use, and improved yield prediction. This paper presents a comprehensive review of machine learning applications in modern agriculture, covering key areas such as crop monitoring, soil analysis, irrigation scheduling, pest, and disease detection, …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 15, Issue 1, 2026 · pp. 39–45 Read article
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Advancements in Agricultural Forecasting: A Review of Machine Learning Based Crop Yield Prediction
Abstract: Agricultural productivity plays a critical role in global food security, and accurate crop yield prediction is essential for optimizing resource allocation and decision-making in farming. The rapid advancements in Machine Learning (ML) and Deep Learning(DL)have transformed agricultural forecasting, enabling data-driven approaches for crop prediction. This review paper provides a comprehensive analysis of various ML and DL techniques applied in crop yield forecast, highlighting the ineffectiveness, challenges, and future directions. The …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 14, Issue 3, 2025 · pp. 32–38 Read article
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Use of Artificial Intelligence to Access and Ensure Safe Drinking Water Supply: A Review
Abstract: Ensuring access to safe drinking water is a critical public health challenge. Traditional water quality assessment methods are often labor-intensive and time-consuming. Artificial intelligence offers a promising alternative, providing rapid, accurate, and scalable solutions for monitoring and predicting water quality. This systematic review examines the application of AI. The review highlights various AI models, including artificial neural networks, support vector machines, decision trees, and ensemble methods, in predicting water quality …
Published in Journal of Water Resource Engineering and Management · Vol. 11, Issue 2, 2024 · pp. 21–28 Read article
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An Analytical Study on Cybersecurity Threats and AI-Driven Mitigation Strategies in Next-Generation Smart Grids
Abstract: The increasing adoption of next-generation smart grids has introduced significant cybersecurity challenges due to their reliance on interconnected digital infrastructures and IoT-based control mechanisms. This study aims to analyze cybersecurity threats in smart grids and explore AI-driven mitigation strategies to enhance grid security and resilience. The research examines common cyber threats such as malware attacks, denial-of-service (DoS), data breaches, and insider threats while evaluating the effectiveness of AI-based solutions, including …
Published in Journal of Operating Systems Development & Trends · Vol. 12, Issue 3, 2025 · pp. 16–25 Read article
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Designing a Sustainable Pedal-Powered Washing Machine for Rural Areas
Abstract: The ingenuity can be found in its simple construction, sparse use of pricey components, extremely low upkeep and repair costs, accessibility to all segments of society, and lack of environmental impact. According to the environmental issue, it is essential to use energy in alternative ways in order to conserve it. The PPWM additionally conserves time, water in a container and power while being reasonably priced. The leg-powered washing machine (PPWM) …
Published in International Journal of Manufacturing and Production Engineering · Vol. 1, Issue 1, 2023 · pp. 20–25 Read article
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Transformer Health Monitoring System
Abstract: Rising demands for reliable and efficient power distribution in modern electric control grid increasingly call up for robust monitoring systems for critical substructure. Being a vital part of the power conduction system, transformer are subjected to mechanical, electrical, and environmental stresses, which, if not properly controlled, can cause failures. In this project, we propose a Transformer Health Monitoring System (THMS) using machine learning (ML) models and real-time monitoring method to …
Published in Journal of Power Electronics and Power Systems · Vol. 15, Issue 3, 2025 · pp. 1–9 Read article
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Drilling Parameters Optimization in LM6/B4C/Fly ash Hybrid Composites by Taguchi Technique
Abstract: Aluminium matrix composites (AMCs) are challenging to machine due to its abrasive characteristics. Because of the broad adoption of MMCs, it is vital to create sufficient equipment to facilitate efficient manufacturing. The current study utilizes signal-to-noise ratio (S/N) analysis to determine the ideal machining parameters for drilling AMC’s. The goal with this study is to investigate the effect of feed rate (FR), drill type (D), speed (SS), reinforcing material R …
Published in Journal of Polymer & Composites · Vol. 13, Issue 1, 2025 · pp. 890–897 Read article
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Intelligent Systems: A study on AI and Machine learning
Abstract: Artificial Intelligence (AI) and Machine Learning (ML) are dynamic branches of computer science that focus on developing systems capable of executing tasks commonly associated with human intelligence. These activities encompass making choices, resolving issues, understanding language, identifying patterns, and learning through experience. Artificial Intelligence refers to the broad area of designing systems and frameworks that enable machines to perform tasks resembling human thought and behavior. This field integrates diverse technologies …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 1, 2025 Read article