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192 articles for “Machining parameters”
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Debris Flow Kinetics in Planetary Environments: A Systems Perspective
Abstract: Debris flow kinetics in planetary environments represent a critical intersection of geomorphology, fluid mechanics, and planetary science. These gravity-driven flow mixtures of solids, liquids, and gases play a key role in shaping planetary surfaces and recording environmental histories. This study adopts a systems perspective to analyze debris flow behavior across different planetary contexts, emphasizing the interconnected roles of material properties, energy transformations, and environmental forcing. By integrating rheological models with …
Published in International Journal of Universe · Vol. 1, Issue 2, 2025 · pp. 08–17 Read article
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Deploying Fuzzy Logic for Self-Tuning Regulator Design for Motion Control in Modern Electrical Machines
Abstract: Modern electrical machines require sophisticated motion control systems capable of adapting to varying operating conditions, load disturbances, and parameter uncertainties. Traditional self-tuning regulators (STR) based on classical control theory often struggle with nonlinearities, time-varying dynamics, and complex operational environments characteristic of contemporary electric drives. This article presents a comprehensive framework for deploying fuzzy logic in self-tuning regulator design to address these challenges in motion control applications. Fuzzy logic controllers leverage …
Published in International Journal of Electrical Machine Analysis and Design · Vol. 3, Issue 2, 2025 · pp. 11–21 Read article
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Heart Attack Prediction Using Machine Learning
Abstract: Heart attacks have become a prevalent and serious condition in recent years due to a variety of causes. Numerous variables, including age, sex, fat, and others, can be used to predict it. In the current study, it was found that a data set with 13 parameters and 302 distinct data values, collected from a Kaggle dataset to assess patient condition, was covered. This article delves into the application of machine …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 1, Issue 1, 2023 · pp. 8–13 Read article
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Greener 3D Printing: The Role of Artificial Intelligence in Sustainable Polymer and Composite Manufacturing
Abstract: The integration of sustainable materials with additive manufacturing (AM) technologies marks a significant step towards environmentally responsible production. Biodegradable polymers, recycled thermoplastics, and bio-based composites, when used in 3D printing, offer the potential to reduce the ecological footprint of manufacturing. However optimizing the interplay between material properties process parameters, and product performance remains a complex challenge. This review examines how artificial intelligence (AI) is being applied to address these challenges …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 288–300 Read article
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ML Analysis of Factors Affecting Vaccination in Rural Children: A Machine Learning Approach
Abstract: Vaccination remains one of the most effective public health interventions for preventing childhood diseases, yet rural regions in India continue to experience uneven immunization coverage due to multiple socioeconomic and geographic barriers. This research applies machine learning techniques to identify and analyze the major determinants influencing childhood vaccination uptake in rural communities. The study utilizes survey-based demographic, socioeconomic, and healthcare-related parameters to build predictive models that classify children as vaccinated …
Published in International Journal of Vaccines · Vol. 3, Issue 2, 2026 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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Mechanical Strength Prediction of Nano-Silica Concrete Composites Using Machine Learning Techniques
Abstract: Nano-silica, or nanosilica, refers to silicon dioxide nanoparticles, which are a kind of silica (SiO₂) with diameters that often fall below 100 nanometers. This nanomaterial has attracted considerable attention because of its distinctive characteristics and diverse array of uses, notably in augmenting the performance of materials such as concrete. The integration of nanoparticles with cementitious matrix in nano-silica concrete offers a viable approach to improving the mechanical characteristics and longevity …
Published in Journal of Polymer & Composites · Vol. 13, Issue 1, 2025 · pp. 963–973 Read article
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Multimodal Data Fusion with Hybrid Machine Learning for Enhanced Prediction of Li-Ion Battery Remaining Useful Life and State of Charge
Abstract: Lithium-ion battery materials used in modern energy storage systems are required to exhibit high reliability, safety, and long lifecycle performance under varying operational and environmental conditions. Accurate prediction of Remaining Useful Life (RUL) and State of Charge (SoC) is therefore essential for understanding material degradation behavior, improving manufacturing quality, and enabling effective lifecycle management. However, nonlinear electrochemical aging, load variability, and thermal uncertainty significantly complicate accurate estimation of these parameters. …
Published in International Journal of Electro-Mechanics and Material Behaviour · Vol. 4, Issue 1, 2026 · pp. 1–5 Read article
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Integrate AI and IoT to Develop Sustainable Polymer Structural Materials Processing Optimization: Enabled Monitoring Strategies for Performance and Lifecycle Assessment
Abstract: The need for long-lasting structural polymer materials that are both environmentally friendly and highly mechanically effective is driving demand for these materials as the industrial sector continues to grow. Optimizing processes, saving energy, detecting faults, and monitoring structures are all hindered by conventional polymer manufacture. This study suggests an AI-IoT system for environmentally friendly production of structural polymer materials to get around these problems. Tools for evaluating system performance and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 · pp. 169–192 Read article
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Develop the Design of Sustainable Polymer Materials: Applying Reinforcement Learning, IoT-Enabled Monitoring, and Data-Driven Manufacturing Approaches
Abstract: Sustainable polymer materials development is a must due to resource constraints, environmental concerns, and the demand for designed materials with high performance. When it comes to material optimization, energy utilization, process unpredictability, and lifecycle sustainability, traditional polymer production methods have their challenges. Reinforcement Learning (RL), Internet of Things (IoT) monitoring, and data-driven production are utilized in the design and manufacturing of sustainable polymer materials. It is recommended to use Internet …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 Read article
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Mechanical Characterization and Machinability Optimization of Stir-Cast Al6061–SiC Metal Matrix Composites
Abstract: This study presents the fabrication, mechanical characterization, metallurgical analysis, and machinability optimization of Aluminum 6061 reinforced with Silicon Carbide (SiC) metal matrix composites (MMCs) at three weight fractions: 5%, 7.5%, and 10%. Composites were manufactured using the stir casting technique, followed by comprehensive mechanical testing (tensile, hardness, and impact), optical microscopy, and scanning electron microscopy (SEM). Machinability was assessed through turning experiments on a lathe using an L9 Taguchi orthogonal …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 Read article
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Artificial Intelligence and Machine Learning Applications in Optimizing Air Conditioning Systems
Abstract: The growing demand for air conditioning systems, especially in the wake of climate change and increasing global temperatures, has led to a significant increase in energy consumption. This, in turn, contributes to the growing concerns of environmental sustainability and operational costs. As a result, there is a pressing need for innovative solutions to optimize the performance and energy efficiency of air conditioning (AC) systems. Artificial Intelligence (AI) and Machine Learning …
Published in Journal of Refrigeration, Air conditioning, Heating and ventilation · Vol. 12, Issue 1, 2025 · pp. 38–43 Read article
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Aerodynamic Optimization of UAV Wings Using Machine Learning
Abstract: Unmanned Aerial Vehicles (UAVs) are increasingly deployed across defense, transportation, agriculture, and environmental monitoring, demanding improved aerodynamic efficiency to enhance endurance, stability, and payload capacity. Traditional aerodynamic optimization approaches, relying on computational fluid dynamics (CFD) simulations and wind tunnel experiments, are often time-consuming and computationally expensive. This study proposes a machine learning (ML)-driven framework for the aerodynamic optimization of UAV wing geometries, aiming to significantly reduce design cycles while improving …
Published in International Journal on Drones · Vol. 2, Issue 1, 2026 · pp. 1–7 Read article
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Enhancing Chatter Resistance in Deep Hole Boring Through Modified Tool Design: A Study on Impact of Length-To-Diameter (L/D) Ratio
Abstract: Deep hole boring is a specialized machining process crucial for creating precise bores with high length-to-diameter (L/D) ratios, particularly vital in aerospace, automotive, and oil and gas industries. The L/D ratio is pivotal for stability and performance. Chatter, a detrimental vibration phenomenon, is a significant concern in deep hole boring, influenced by the L/D ratio. Higher L/D ratios increase chatter, leading to poor surface finish and reduced tool life. Longer …
Published in International Journal of Machine Systems and Manufacturing Technology · Vol. 2, Issue 2, 2024 · pp. 24–33 Read article
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An Innovative AI-Integrated Approach for Identifying the Tensile Robustness of Polymeric Materials
Abstract: Polymeric materials have so many applications and character similar to flexibility, robustness and lightweight nature they are essential to a large variety of industries. Though, it is difficult to establish their tensile robustness appropriately, particularly in a variety of environmental situation. Provide a recommended Artificial Intelligence (AI)-integrated method to decide the issues of rapidly ascertaining the tensile robustness of the polymeric material. Using machine learning (ML), this study, predicted and …
Published in Journal of Polymer & Composites · Vol. 13, Issue 1, 2025 · pp. 90–97 Read article
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Numerical Studies on Deep Drawability of the Aluminium Alloy, AA6082 and Parameters Affecting It
Abstract: Deep drawing is a metal forming operation used for manufacturing sheet-metal components for application in the automobile, aerospace, and packaging industries. The objective of the present work was to study the various parameters influencing the drawability of AA6082. The deep-drawing process was modeled and simulated in Ls-Dyna Pre-Post(R) V4.6.17 software. The tensile test was performed according to the ASTM-E8M standard on AA6082-T6 material and subsequently annealed to attain higher ductility. …
Published in Journal of Polymer & Composites Read article
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A Machine learning approach to asses carbon emission, utility to produce biomaterial
Abstract: The transportation sector, and the automotive industry in particular, has grown significantly over the last ten years. However, these quick advancements have also brought about important problems that require workable answers. The problem of carbon emission is responsible for global warming due to greenhouse gas emission. The present work is focused on CO2 emission from public and personal transport within Agra city on a real-time basis. In the context of …
Published in International Journal of Advanced Control and System Engineering · Vol. 3, Issue 1, 2025 · pp. 22–30 Read article
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Predictive Maintenance in Semiconductor Systems: Insights from Machine Intelligence and Data-Driven Methods
Abstract: With the fast-paced development of semiconductor technology comes the need to focus on device reliability, or how long devices will function and the likelihood of devices having operational issues. Predicting failures and avoiding downtime with the implementation of timely, actionable, and data-driven maintenance strategies are essential to insure devices function sustainably within predetermined performance levels. The implementation of predictive maintenance within artificial intelligence and machine learning technologies will provide the …
Published in Journal of Semiconductor Devices and Circuits · Vol. 13, Issue 1, 2026 Read article
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A Review on Lung Cancer Prediction Using Machine Learning
Abstract: Lung cancer continues to be a major contributor to cancer-related mortality across the globe. Timely diagnosis and reliable prediction models play a crucial role in enhancing treatment outcomes and survival rates for patients. The present study focuses on the utilization of machine learning (ML) methods for the prediction of lung cancer. Using datasets that incorporate clinical records, imaging modalities, and genetic profiles, the research assesses the predictive capabilities of multiple …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 14, Issue 3, 2025 · pp. 1–11 Read article
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Optimization of Tensile and Flexural Properties of PETG Filament in FDM 3D Printing Using Response Surface Methodology
Abstract: Now a days manufacturing trend has been changed from subtractive to additive. In additive manufacturing (3D Printing), layer by layer deposition occurs and final product can be obtained. The main advantages of additive manufacturing is to obtain customized product, complex geometry product. This research investigates the optimization of tensile and flexural Strength of PETG filament in Fused Deposition Modeling (FDM) 3-d printing by using of Response Surface Methodology (RSM). Specimens …
Published in Journal of Polymer & Composites · Vol. 13, Issue 1, 2025 · pp. 39–58 Read article