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309 articles for “High-performance machining”
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AI-Powered ECG Prediction System for Detecting Cardiovascular Disease
Abstract: The proposed AI-powered CardioSmart Analyzer, an electrocardiogram (ECG) prediction system, presents an innovative and scientifically rigorous approach to the real-time automated analysis of ECG signals for diagnosing various heart conditions. This research focused on building a predictive model to identify cardiovascular diseases (CVD) using ECG data. A dataset comprising 2,840 12-lead ECG recordings was gathered from medical facilities in Gazipur, Bangladesh, over the period from June to August 2024. The …
Published in Research and Reviews: A Journal of Health Professions · Vol. 15, Issue 3, 2025 · pp. 51–85 Read article
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Interfacial and Tribo-Mechanical Performance of a TiO₂–Castor Oil Polymeric Nanofluid During Sustainable Machining of AISI 316L Stainless Steel Under MQL Conditions
Abstract: This research examines the tribo-mechanical performance and interfacial film characteristics of a TiO₂-reinforced castor-oil polymeric nanofluid during the turning of AISI 316L stainless steel under minimum-quantity lubrication (MQL). A Taguchi L9 orthogonal array was utilized to assess the synergistic effects of cutting speed, depth of cut, and coolant composition on surface integrity, while machining experiments were performed under dry, conventional, and TiO₂-nanofluid lubrication techniques. ANOVA and multiple-regression modeling were used …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 901–914 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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AI-Based Preventive Healthcare Using Quantum Computing
Abstract: With its improved performance and capabilities, quantum machine learning (QML) is becoming a promising field, especially in the healthcare industry for tasks like early heart disease prediction. In this work, a Quantum Support Vector Classifier (QSVC) is proposed as the basic classifier for a bagging ensemble learning model. Shapley Additive explanations (SHAP) are used to evaluate the significance of each attribute in the predictions in order to improve explainability. Using …
Published in Journal of Nanoscience, NanoEngineering & Applications · Vol. 15, Issue 2, 2025 Read article
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Hybrid Quantum–Machine Learning Framework for Nonlinear Rheological Modeling of Polymer and Composite Materials
Abstract: In polymer and composite materials, a major challenge lies in predicting their nonlinear rheological response, owing to complex multiscale interactions that are not captured by traditional constitutive laws or conventional machine learning approaches. In this study, a hybrid Quantum Machine Learning (QML) model comprising Quantum Support Vector Machine (QSVM) and Quantum Neural Network (QNN) architectures is proposed for viscosity prediction without requiring any specific rheological equation. To train and test …
Published in Journal of Polymer & Composites · Vol. 14, Issue 5, 2026 Read article
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Enhancing Credit Card Fraud Detection Using Device Fingerprinting and Behavioral Biometrics
Abstract: Credit card fraud is a growing global concern, with financial losses projected to reach $ 43.47 billion by 2028. Credit card fraud poses a major challenge in the financial industry, resulting in substantial financial losses and security risks. This research introduces a Machine Learning-based Credit Card Fraud Detection System designed to improve the accuracy of fraud identification. Due to the imbalanced nature of fraud datasets, SMOTE (Synthetic Minority Over-sampling Technique) …
Published in Journal Of Network security · Vol. 13, Issue 2, 2025 · pp. 40–50 Read article
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Machinability of Metal Matrix Composites using Wire Electric Discharge Machining: A Review
Abstract: Metal Matrix composites [MMCs] are increasingly being applied in the automotive and aerospace industries as high-performance substitutes for traditional materials. Due to these properties, namely, high strength-to-weight ratio, outstanding fracture toughness, and low density, a vast array of uses is feasible. We can control the properties of MMCs directionally as well, which makes them the perfect choice for military uses and marine and sports equipment. The machining of MMCs plays …
Published in Journal of Polymer & Composites · Vol. 13, Issue 1, 2025 · pp. 303–311 Read article
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Design for Additive Manufacturing (DFAM) Leveraging 3D Printing to Optimize Machine Components
Abstract: Design for Additive Manufacturing (DFAM) is a new methodology that focuses on maximizing the special potential of 3D printing technologies to optimize machine components. Contrasting to traditional manufacturing processes, additive manufacturing (AM) makes it feasible to create complicated shapes that would be impossible or difficult to do using standard methods like casting or machining. This study explores the principles of DFAM, including the advantages it offers in terms of design …
Published in International Journal of Machine Systems and Manufacturing Technology · Vol. 2, Issue 2, 2024 · pp. 9–14 Read article
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Exploring the Application of 5-axis CNC Machining for the Fabrication of an Impeller for Single Suction Centrifugal Pump
Abstract: This study delves into the practical application of 5-axis machining for the fabrication of an impeller for a single suction centrifugal pump. It explores the integration of CNC machining and CAD as a crucial aspect, highlighting the synergy between design and manufacturing. The efficiency and performance of the pump are greatly influenced by the impeller, an important part of pump design. Impellers made using traditional manufacturing techniques frequently have intricate …
Published in International Journal of Manufacturing and Production Engineering · Vol. 1, Issue 2, 2023 · pp. 9–19 Read article
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Big Data in Chemistry: Problems and Answers
Abstract: The rapid growth of experimental and computational chemistry data, researchers now have access to vast datasets, presenting both significant opportunities and challenges. This paper explores the primary challenges associated with managing, processing, and utilizing big data in chemistry, including data heterogeneity, integration across various scales and systems, lack of standardized formats, and the need for advanced tools for data analysis. Additionally, the paper discusses the ethical concerns of data ownership, …
Published in International Journal of Cheminformatics · Vol. 2, Issue 1, 2024 · pp. 9–14 Read article
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Signal Feature Extraction and Machine Learning Techniques for Human Activity Recognition
Abstract: Human Activity Recognition (HAR) has emerged as a critical field of study with diverse applications in healthcare, fitness tracking, smart homes, and human-computer interaction. The aim of this research is to create an efficient HAR system through advanced techniques characterized by signal feature extraction and machine learning algorithms. The MEMS sensors are used appropriately during data mining to extract time-domain, frequency-domain, and statistical features, which are subsequently passed to the …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 12, Issue 1, 2025 · pp. 24–41 Read article
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The Convergence of AI and Composites - A Review Anchored in Patent Trends
Abstract: The integration of artificial intelligence (AI) and machine learning (ML) techniques is revolutionizing the design, analysis, and optimization of polymer (PC/FRP), metal (MC), and ceramic matrix composites (CC). Techniques such as artificial neural networks (ANN), deep learning (DL), genetic algorithms (GA), and physics-informed machine learning (PIML) are employed to enhance property estimation, process optimization, and predictive modeling. These AI-driven frameworks enable virtual testing, application-specific material design, and real-time decision-making, while …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 182–198 Read article
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DFT/Data Guided Predictive Modelling of Absorption Maxima in the OLED Rubrene Derivatives
Abstract: This study investigates the optical properties of rubrene derivatives to develop an accurate predictive model for absorption maxima using computational chemistry and chemoinformatic techniques. We benchmarked various quantum chemical methods, identifying that the M06-2X/aug-cc-pVDZ method in dichloromethane (DCM) provided the strongest correlation with experimental data. Key molecular descriptors such as band gap, ionization potential, and electrophilicity index were calculated and analyzed using principal component analysis (PCA) to identify significant factors …
Published in International Journal of Cheminformatics · Vol. 4, Issue 1, 2026 · pp. 41–56 Read article
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Laser Beam Machining Techniques and Applications: A Review
Abstract: Laser beam machining (LBM) is the most common thermal energy-based non-contact, non-conventional machining process. The non-conventional manufacturing processes are used to remove extra material using a variety of mechanical, thermal, electrical, chemical, or combinations of these energies without the use of sharp cutting tools as is required for conventional manufacturing. With innovative approaches to manufacturing processes, it has transformed a number of industries. It is frequently used to machine a …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 3, 2024 · pp. 28–35 Read article
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Comparative Analysis of Serial and Parallel Robot Mechanisms for Industrial Automation
Abstract: Serial and parallel manipulators represent two major mechanical architectures in industrial automation, each with distinct strengths and trade-offs. This study presents a detailed comparative analysis of serial-chain (open-kinematic) robots and parallel-kinematic manipulators (PKMs) with a focus on industrial automation tasks. It covers kinematics, static accuracy and stiffness, dynamics and actuation requirements, control and calibration burdens, workspace and singularity behaviour, and practical industrial considerations (cost, integration, safety, maintenance). Serial robots, exemplified …
Published in International Journal of Robotics and Automation in Mechanics · Vol. 3, Issue 2, 2025 · pp. 22–26 Read article
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A Review of Torque Ripple Reduction Techniques in Switched Reluctance Motors
Abstract: Switched Reluctance Motors (SRMs) have emerged as a promising alternative to conventional motor technologies due to their rugged structure, low manufacturing cost, high-temperature capability, and suitability for harsh environments. Despite these advantages, the widespread adoption of SRMs in applications such as electric vehicles, household appliances, industrial drives, and aerospace systems is significantly restricted by the issue of torque ripple. Torque ripple manifests as periodic fluctuations in the developed electromagnetic torque, …
Published in International Journal of Electrical Machine Analysis and Design · Vol. 3, Issue 2, 2025 · pp. 45–50 Read article
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Machine Learning Driven Mobile Price Prediction Using Feature Selection and Parameter Optimization
Abstract: Machine learning calculations are utilized in many fields like money, training, industry, medication, and online business. Machine learning calculations show execution contrasts relying upon the dataset and handling steps. Picking the right calculation, preprocessing and post-handling techniques have incredible significance in accomplishing great outcomes. The Random Forest classifier, K-nearest neighbor classifier, and support vector machine methods are evaluated to forecast mobile phone price categories. The “prediction” dataset which is taken …
Published in Current Trends in Information Technology · Vol. 14, Issue 3, 2024 · pp. 18–25 Read article
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Predictive Analytics and Adaptive Learning: A Machine Learning Framework for Reducing Learning Gaps
Abstract: Most contemporary digital learning environments encounter persistent challenges when it comes to accurately identifying students who are at-risk of academic underperformance. These challenges often arise due to limited visibility in learners’ engagement levels and gaps in conceptual understanding, particularly during the early stages of a course. To address this issue, the present study proposes an early prediction framework that leverages comprehensive student-related data through the application of machine learning techniques. …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 4, Issue 1, 2026 · pp. 16–21 Read article
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A Machine Learning Approach to Forecasting Outcomes in Limited Overs Cricket
Abstract: This study explores the application of machine learning techniques to forecasting outcomes in limited overs cricket matches, with a particular focus on One Day Internationals (ODIs). The research investigates how classification algorithms can be effectively utilized to analyze both contextual and dynamic factors that influence match results, including venue details, toss decisions, team strength, and historical performance records. By employing a structured methodology encompassing feature selection, data preprocessing, model training, …
Published in Recent Trends in Sports · Vol. 2, Issue 2, 2025 · pp. 09–19 Read article
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Experimental Investigation and Optimization of Machining Parameters for Al6351 Alloy Using a Modified Taguchi Approach
Abstract: Machining processes encompass both conventional and non-conventional techniques and optimizing machining parameters is crucial for achieving high-quality outcomes. However, simplifying these processes remains a significant challenge. This study focuses on determining the optimal machining parameters—cutting speed, feed rate, and depth-of-cut to enhance performance characteristics in Al6351 alloy plates. The parameters evaluated include surface roughness (Ra), material removal rate (MRR), resultant forces (RF), and temperature at the tool- workpiece interface (Temp). …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 1463–1481 Read article