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816 articles for “machining time”
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Industrial Prognostics via Ensemble Machine Learning: An Uncertainty Aware Framework for RUL Estimation on NASA FD004 Telemetry
Abstract: Estimating the Remaining Useful Life (RUL) of industrial machinery in real-time is now vital for both operational safety and smart resource management. In the aviation industry, turbofan engines deal with constantly shifting flight conditions, making traditional, scheduled maintenance both expensive and prone to error. This paper addresses the flaws in common “point-prediction” AI models, which offer a single failure date without any margin for error, by introducing a new, uncertainty-aware …
Published in Journal of Aerospace Engineering & Technology · Vol. 16, Issue 2, 2026 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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Advanced Water Purification Techniques for Sustainable Clean Water Management: A Comprehensive Review
Abstract: The growing global demand for safe drinking water, coupled with increasing pollution from industrialization, urbanization, and agricultural activities, has intensified the need for efficient and sustainable water purification technologies. Conventional water treatment processes often fail to remove emerging contaminants such as pharmaceuticals, microplastics, endocrine-disrupting compounds, and heavy metals. Advanced water purification technologies—including membrane filtration, advanced oxidation processes (AOPs), nanotechnology-based adsorbents, photocatalysis, electrochemical treatment, and bio-inspired purification systems—have emerged as promising …
Published in Journal of Water Pollution & Purification Research · Vol. 13, Issue 1, 2026 · pp. 47–52 Read article
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Minimization of Steady State Error of DC Motor Drive Using PID Controller
Abstract: As it is the time of advanced technology, machinery implementation is increasing very rapidly. Industries always work with the implemented technology which gives the fast and accurate response of the system. This paper includes the dc motor speed controlling using the PID controller. Close loop control system and electrical drives have the advantageous system for the technology. DC motor is used for getting best and easiest speed and torque control …
Published in Journal of Power Electronics and Power Systems · Vol. 8, Issue 2, 2018 · pp. 25–29 Read article
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A Review of Statistical Project Control Tool for Construction Industry
Abstract: AbstractProject management at construction industry is a complex task which involves qualitative and quantitative analysis of the activities. Various statistical models have been developed and suggested by the researchers which aim at making the process of managing construction projects more systematic and to access any shortcoming at the proper time, before it leads to the failure of the project. This paper presents and overview of such methods and discusses theirimportance …
Published in Journal of Structural Engineering and Management · Vol. 5, Issue 1, 2018 · pp. 18–21 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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AI-Powered Solutions for Sustainable Waste Management in Construction Projects
Abstract: The construction industry is a significant contributor to global waste, posing challenges to sustainability and environmental health. This research explores AI-powered solutions for sustainable waste management in construction projects, focusing on optimizing waste reduction, recycling, and resource efficiency. By integrating machine learning algorithms and IoT-enabled sensors, real-time monitoring of waste generation and segregation can be achieved. Predictive analytics and AI-driven decision-making tools are employed to enhance material reuse and minimize …
Published in Recent Trends in Civil Engineering & Technology · Vol. 15, Issue 2, 2025 · pp. 1–5 Read article
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Designing an AI-Based Platform for Stock Market Prediction
Abstract: The AI-Based Platform for Stock Market Prediction is an advanced tool designed to forecast stock prices and market trends using artificial intelligence. This platform combines machine learning algorithms, real-time financial data, and sentiment analysis to provide investors with actionable insights. The platform uses advanced predictive techniques like Long Short-Term Memory (LSTM) networks and Gradient Boosting Machines to generate precise and reliable forecasts. Additionally, it incorporates interactive visualizations and portfolio optimization …
Published in E-Commerce for Future & Trends · Vol. 12, Issue 3, 2025 · pp. 14–19 Read article
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IoT-Based Battery Health Monitoring for Electric Vehicles Using Machine Learning
Abstract: With increasing utilization of the Electric Vehicles (EV)s in global scale, battery health management becomes a critical factor which has great impact on vehicle performance, safety and longevity. Battery materials, such as NMC LFP lithium-ion batteries and lithium-ion batteries, degrade over time from charging behaviour, heat stress, discharging voltage profiles and environmental limits. Conventional BMS only offer threshold based health diagnostics and cannot perform accurate degradation prediction. This work presents …
Published in International Journal of Machine Systems and Manufacturing Technology · Vol. 4, Issue 1, 2026 · pp. 8–12 Read article
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Parametric Optimization of Aluminum Alloy 6061 Using Wire-EDM for Automotive Applications: A Taguchi-Based Approach
Abstract: Machining hard materials with complex geometries presents numerous challenges, often requiring the use of non-traditional methods such as wire Electric Discharge Machining (EDM). However, wire EDM machines operate at slow speeds, and increasing the speed can negatively impact surface finish, making it a difficult task. The ongoing research investigates the machinability study of Aluminum Alloy 6061 using wire EDM, emphasizing the optimization of process parameters to enhance machining performance and …
Published in Journal of Polymer & Composites · Vol. 13, Issue 5, 2025 · pp. 293–302 Read article
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Current Updates On Employability Of Artificial Intelligence In Healthcare Science & Research
Abstract: Over the centuries, tools have been developed to increase refinement to manipulate different tools in many ways to use human digital computers. It can perform the same types of numerical and symbolic operations that can be done by ordinary people, but faster and more reliable. Artificial intelligence algorithms applied to computer applications and software. Include knowledge-based systems. AI is the science that mimics the mental skills of humans in computers. …
Published in Research and Reviews: A Journal of Pharmaceutical Science · Vol. 16, Issue 2, 2025 · pp. 72–77 Read article
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Optimization of Robotic Path Planning Algorithms for Autonomous Material Handling Systems
Abstract: For autonomous systems for handling materials (AMHS) to operate as efficiently as possible in industrial and logistical settings, robotic route planning is essential. This study examines many robotic route planning algorithms, emphasizing their use, ways of optimization, and difficulties in material handling systems. To improve the effectiveness, precision, and computational viability of these algorithms, the study also examines a number of optimization strategies, including machine learning, parallelization, heuristic search, and …
Published in International Journal of Robotics and Automation in Mechanics · Vol. 2, Issue 2, 2024 · pp. 15–20 Read article
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Improving Plastic Bottle Waste Management System of India using RVMs
Abstract: India is ranked first as the most populous country in the world, and plastic waste management has been a major ongoing concern for India. With the growing economy and population, the growth of plastic waste generation has been exponential but plastic waste management has been underachieved. The excess utilization and mishandling of single-use plastic have depreciated the performance of the plastic waste management system of India. This study emphasizes on …
Published in Journal of Energy, Environment & Carbon Credits · Vol. 14, Issue 3, 2024 · pp. 31–41 Read article
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Design of Gear Tooth Rounding and Chamfering Machine
Abstract: All movable gears which mesh with another gear require either rounding or chamfering. The operation is generally applied to the teeth of spur gears for use in sliding gear transmission of automotive vehicles and machine tool Gear boxes where the gears are moved on their longitudinal axes meshing with another spur gear. In current scenario the mechanically operated machines require lot of settings, which is time consuming and requires one …
Published in Trends in Machine design · Vol. 4, Issue 3, 2017 · pp. 38–44 Read article
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Disease Prediction Using Machine Learning (ML)
Abstract: A technique called Machine Learning Disease Prediction uses symptoms reported by users or patients to forecast disease. The user-provided symptoms are entered into the system, and it outputs the disease probability. In disease forecasting, various popular supervised machine learning methods are known to be utilized. These algorithm estimates the likelihood of a disease occurrence. Precise analysis of medical information will support timely disease detection and management of patients based on …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 12, Issue 2, 2024 · pp. 40–48 Read article
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Crop Disease Prediction by Machine Learning
Abstract: The classification of Crop can be classified into several methods. The data set of crop leaf illnesses, notably Bacterial Leaf Blight disease (BLB), a crop leaf disease with significant outbreaks throughout Thailand, and Brown Spot Crop disease (BSR), is classified employing image classification in this study. Additionally, image processing technology is used for identifying different types of crop leaf disease. These algorithms include the Random Forest, Decision Tree, Gradient Boost, …
Published in Trends in Machine design · Vol. 11, Issue 2, 2024 · pp. 21–25 Read article
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Energy-efficient HVAC System with Decision Tree Classifier and Real-time SMS Notification
Abstract: This research paper explores the design and implementation of an energy-efficient heating, ventilation, and air conditioning (HVAC) system aimed at optimizing energy consumption and enhancing operational efficiency. The system incorporates high-efficiency components, including axial flow fans, motors, and intelligent variable frequency drives, achieving an overall system efficiency of up to 85%. By utilizing both static and dynamic pressures, the HVAC system operates more effectively under varying conditions compared to traditional …
Published in International Journal of Advanced Robotics and Automation Technology · Vol. 2, Issue 2, 2024 · pp. 29–34 Read article
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Early Autism Diagnosis: Machine Learning Models and Their Effectiveness
Abstract: Diagnosis is of utmost importance for timely intervention and support. However, traditional diagnosis methods, which are based on subjective assessment, are delayed. This project explores the role that machine learning techniques might play in enhancing the accuracy and effectiveness of ASD detection. Several state-of-the-art classification algorithms were benchmarked using a dataset from Kaggle. Logistic Regression, XG Boost, Random Forest, Decision Tree, and Gradient Boosting were taken into consideration. Other performance …
Published in Journal of Instrumentation Technology & Innovations · Vol. 14, Issue 2, 2024 Read article
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Review article on Quality Control in Clinical Trials
Abstract: Quality control (QC) is a critical component in the conduct of clinical trials, ensuring the accuracy, reliability, and credibility of data collected throughout the study. It encompasses a systematic set of procedures designed to monitor trial conduct and data integrity, thus safeguarding the rights, safety, and well-being of participants. This review explores the principles, implementation, and evolving practices of quality control in clinical trials, highlighting its importance across all phases …
Published in Research & Reviews: A Journal of Drug Design & Discovery · Vol. 12, Issue 3, 2025 · pp. 01–07 Read article
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Defect Diagnosis and Modelling for A Rotating Machine Running at A Steady Pace
Abstract: Rotary equipment, such as gears, shafts, pumps, and bearings, are widely used across various rotary machine in industries, often operating under different loading conditions. The fluctuations in loading can lead to fatigue failures in rotating components, significantly affecting machinery performance. To investigate the behavior of such rotating equipment under diverse operational scenarios, a numerical model has been created. This approach can replace costly and often challenging experimental methods. However, it …
Published in Journal of Polymer & Composites · Vol. 13, Issue 5, 2025 · pp. 154–164 Read article