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192 articles for “Machining parameters”
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Improvement of Geometric Tolerances and Mechanical Properties of Aluminum Hybrid Metal Matrix Composites
Abstract: In the field of metal matrix composite materials, there has been a generous thrust towards the development of electrical discharge machining (EDM). In this study, stir casted aluminum hybrid metal matrix composites were successfully machined using EDM by analyzing the input process parameters namely, pulse-on time, peak current, and gap voltage using L27 orthogonal array. The ideal conditions for various output responses such as material removal rate, circularity, and radial …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 1583–1592 Read article
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Application of Grey Wolf Optimizer (GWO) strategy for Malware Analysis
Abstract: The ever-evolving landscape of cybersecurity necessitates continuous advancements in malware analysis techniques. This study explores the deployment of the Grey Wolf Optimizer (GWO) algorithm as a novel bio-inspired optimization mechanism to address the challenges posed by modern malware threats. The primary objective is to enhance various facets of malware analysis, including feature selection, parameter optimization, and the overall efficacy of malware detection models. The study begins by introducing the GWO …
Published in International Journal of Wireless Security and Networks · Vol. 1, Issue 2, 2023 · pp. 43–53 Read article
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Enhancing Surface Roughness in the Taguchi Method for Turning Alloy Steel in Wet and Dry Environments
Abstract: The present investigation focuses on evaluating the performance of turning operations in alloy steel with particular emphasis on the effect of cutting parameters on surface roughness. In the machining of alloy steel, tool life and surface integrity are significantly influenced by parameters such as spindle speed, depth of cut, and feed rate. Among these, feed rate has been observed to exert the most prominent effect on surface roughness. To systematically …
Published in International Journal of Robotics and Automation in Mechanics · Vol. 3, Issue 2, 2025 · pp. 1–6 Read article
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IoT Sensors to Monitor Pipeline Pressure and Flow Rate Combined with ML-Algorithms to Detect Leakages
Abstract: In the field of fluid mechanics, pipelines are the lifeblood of industries, transporting everything from natural gas and oil to water and chemicals. Maintaining their integrity is paramount for safety, economic efficiency, and environmental protection. Traditional leak detection methods explained in fluid mechanics can be slow, expensive, and sometimes fail to identify small leaks early enough to prevent significant damage. However, the convergence of Internet of Things (IoT) and Machine …
Published in Recent Trends in Fluid Mechanics · Vol. 12, Issue 2, 2025 · pp. 40–48 Read article
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An Experimental Study on Multi-Criteria Parameters Optimization of Process for Al6351/SiC/Gr Metal Matrix Composites Using AHP-TOPSIS Approach
Abstract: This research focuses on optimizing the process parameters of Wire Electrical Discharge Machining (WEDM) for a hybrid Metal Matrix Composite (MMC) comprising Al6351 aluminum alloy reinforced with 4% SiC and 6% graphite (Gr), fabricated via squeeze casting. This technique enables the formation of dense, defect-free composites with uniform reinforcement distribution, enhancing both mechanical properties and structural integrity. Microstructural characterization using Scanning Electron Microscopy (SEM) confirmed the even dispersion and bonding …
Published in Journal of Polymer & Composites · Vol. 13, Issue 5, 2025 · pp. 303–319 Read article
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A Review on Parametric Optimization of WEDM Technique for OHNS Steel
Abstract: In this study, the Wire Electrical Discharge Machining (WEDM) process for OHNS (Oil Hardened Non-Shrinking) steel, a high-performance material frequently used in the production of dies, punches, and precision tooling components, is optimized parametrically and validated experimentally. A continuously moving wire electrode and a sequence of electrical discharges are used in WEDM, a non-traditional machining method, to erode material and produce intricate and precise profiles, particularly in materials that are …
Published in Trends in Mechanical Engineering & Technology · Vol. 15, Issue 2, 2025 · pp. 29–35 Read article
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Optimizing Plasma Cutting Parameters for Thin Mild Steel Sheets of Various Thicknesses
Abstract: Plasma machining was investigated for suitability to cut thin sheets and the quality obtained was assessed. The main objective of this investigation is to machine or cut required Mild steel sheet thicknesses and to determine the surface roughness and MRR values. Traditional cutting techniques have been discovered to be costly in terms of both time and resources. This study may be valuable to car converters, such as firms that make …
Published in International Journal of Electro-Mechanics and Material Behaviour · Vol. 1, Issue 1, 2023 · pp. 23–28 Read article
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Enhancing Performance of Hybrid Natural Fiber Reinforced Polymer: Insights into Processing, Characterization and Machining
Abstract: Natural fibre reinforced polymer composites (N-FRP) have gained popularity in recent years as an alternative to regular polymer composites, owing to growing environmental concerns. Natural fibers are becoming increasingly popular due to their outstanding properties such as flexibility, strength, compatibility with living creatures, and impact resistance. A notable application of natural fibers is in the medical industry, with the goal of producing cost-effective, sustainable, and long-lasting products. Combining different fibers …
Published in Journal of Polymer & Composites · Vol. 13, Issue 2, 2025 · pp. 139–153 Read article
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Utilizing Machine Learning to Evaluate the Connection between Poisson's Ratio and the Petrophysical Properties of Reservoir Rocks
Abstract: The Poisson's ratio is a crucial cornerstone, illuminating our understanding of geomechanical behaviour in wells during the dynamic drilling process and the inspiring recovery journey. This research rigorously employs machine learning methods to analyse the significant impact of geophysical parameters on the Poisson ratio in hydrocarbon reservoirs found in oil fields. The analysis utilized data from multiple oil and gas fields, highlighting the crucial relationships between the Poisson ratio, the …
Published in Journal of Petroleum Engineering & Technology · Vol. 15, Issue 2, 2025 · pp. 33–43 Read article
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An Experimental Investigation on Surface Roughness of Laser Beam Machining of Aluminium Alloy
Abstract: Laser beam machining (LBM) is a cutting-edge technique widely utilized for precision machining of advanced materials. This experimental investigation focuses on the surface roughness of aluminum alloy (Al 6061) during LBM. The study systematically examines the impact of process parameters such as laser power, cutting speed, and gas pressure on surface roughness (Ra). The significance of surface roughness in determining the quality and functionality of machined components is emphasized. The …
Published in Journal of Polymer & Composites · Vol. 12, Issue 6, 2024 · pp. 82–104 Read article
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Experimental Findings of the EDM Process Parameters for Metal Matrix Composites
Abstract: The process of electrical discharge machining (EDM) shapes hard metals and creates intricately formed, deep holes in a variety of electro-conductive materials by arc erosion. With regard to material removal rate (MRR) in metal matrix composite EDM, this study intends to examine the impacts of operational factors. The material removal rate generated is used to assess the efficacy of the metal matrix composite EDM process. It has been noted that …
Published in Journal of Polymer & Composites · Vol. 12, Issue 6, 2024 · pp. 14–18 Read article
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AI-Driven Framework for Accelerating Polymer Nanocomposite Commercialization in Computational Materials Engineering
Abstract: The remarkable mechanical strength increased functional qualities, lightweight structure, and thermal stability of polymer nanocomposites have prompted modern materials research to prioritize their rapid commercialization. Advanced materials can be created by adding nanoscale fillers such as carbon nanotubes, graphene, silica, and metal oxides to polymer matrices. These materials have applications in biomedical engineering, aerospace, electronics, packaging, and automobile manufacture. Research and development of polymer nanocomposites has traditionally relied on costly …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 1–19 Read article
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Machine Learning Assisted Timing Violation Prediction in Sub-7nm VLSI Physical Design
Abstract: The continuous scaling of semiconductor technology into the sub-7nm regime has introduced significant challenges in timing closure due to process variability, interconnect delay, power density, and manufacturing uncertainties. Conventional static timing analysis techniques often require extensive computational resources and iterative optimization cycles, resulting in increased design complexity and longer turnaround time. This research proposes a Machine Learning Assisted Timing Violation Prediction framework for sub-7nm VLSI physical design to improve early-stage …
Published in International Journal of VLSI Circuit Design & Technology · Vol. 4, Issue 1, 2026 Read article
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Study of Contact Temperature During Polishing of Zinc Plate with Ultrasonic Vibration Using Single Pole Magnetic Abrasive Finishing
Abstract: The present work scrutinizes the impact of ultrasonic vibration on the contact temperature during polishing zinc plates with Single Pole Magnetic Abrasive Finishing (SPMAF). The efficacy and eminence of FMAB finishing are pointedly influenced by heat generation at the interaction point, as raised up temperatures can lead to the deprivation of abrasive elements or the bonding material inside the brush, which leads to the fading its overall effectiveness. The magnetic …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 1239–1247 Read article
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Multi-Objective Optimization of Carbon-Glass Fiber Polymer Drilling Process Based on Fuzzy Grey Entropy Weighing Method
Abstract: In recent years, the machining characteristics of hybrid fiber polymer composites have garnered significant research attention due to their growing industrial applications. This study specifically focuses on the drilling of hybrid carbon-glass fiber reinforced (CGFR) epoxy composites, fabricated using the hand layup technique. The key machining characteristics evaluated in this drilling process include surface roughness and circularity error. The influence of critical drilling process parameters, such as spindle speed, drill …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 100–112 Read article
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A Hybrid Algorithm for Processor Scheduling Using Game Theory Variants
Abstract: This study proposes a novel hybrid algorithm for processor scheduling in modern operating systems, integrating the strengths of traditional scheduling methods with game theory variants. Traditional schedulers often struggle to adapt to dynamic workload changes, leading to suboptimal performance. Our hybrid approach addresses this by treating processes as "players" in a game, where the "payoff" is CPU time. A base scheduler (e.g., Weighted Fair Queuing, Earliest Deadline First) provides a …
Published in Journal of Operating Systems Development & Trends · Vol. 12, Issue 1, 2025 · pp. 48–56 Read article
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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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Intelligent Design Approaches in Microwave Engineering Using Machine Learning Techniques
Abstract: In microwave engineering, machine learning (ML) has become a potent technology allowing quicker design cycles, improved modelling accuracy, and automatic optimisation of complicated systems. Recent developments in the use of ML methods to microwave components and systems, including antennas, filters, and high-frequency circuits, are summarised in this study. In the framework of electromagnetic simulation, surrogate modelling, and parameter extraction, supervised and unsupervised learning algorithms are addressed. Moreover, the study looked …
Published in Journal of Microwave Engineering and Technologies · Vol. 12, Issue 2, 2025 · pp. 31–38 Read article
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Design and Performance Analysis of Natural Rubber–Silica Nanocomposite Vibration Isolators for Household Appliances
Abstract: Natural rubber (NR) nanocomposites reinforced with surface-modified precipitated silica (SiO2) were synthesized through a two-roll mill compounding and compression-moulding process, then systematically evaluated as passive vibration isolator materials for domestic washing machine applications. Five silica loadings 0, 5, 10, 15, and 20 phr were investigated. A comprehensive characterization suite encompassing tensile testing, Shore A hardness, dynamic mechanical analysis (DMA), thermogravimetric analysis (TGA), and scanning electron microscopy (SEM) revealed clear structure–property …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 554–560 Read article
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Diabetes Risk Prediction from Survey Data Using Machine Learning Algorithms
Abstract: Diabetes mellitus represents one of the most significant global health challenges, affecting millions worldwide and leading to severe complications if left undiagnosed or poorly managed. Early detection and risk assessment are crucial for preventing the progression of this chronic condition. This research presents a comprehensive machine learning approach for predicting diabetes risk using survey-based health parameters. The study implements and compares four prominent classification algorithms: Logistic Regression, K-Nearest Neighbors (KNN), …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 Read article