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686 articles for “machining efficiency”
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Machine Learning Pipelines: A Survey on Automation, Scalability, and Deployment Strategies
Abstract: Machine learning (ML) has become a critical enabler of intelligent applications across domains, requiring robust, efficient, and scalable deployment workflows. This review paper provides an in-depth overview of machine learning pipelines, emphasizing three key dimensions: automation, scalability, and deployment methodologies. It begins by exploring automation techniques that reduce manual effort in data ingestion, preprocessing, model selection, and hyperparameter tuning. Tools such as AutoML, TFX, and workflow orchestration platforms are examined …
Published in Journal of Advances in Shell Programming · Vol. 12, Issue 2, 2025 · pp. 17–28 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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Design, Development, and Optimization of Autonomous Robots for Enhanced Performance
Abstract: Autonomous robots are transforming industries by executing complex tasks with minimal human intervention, improving efficiency, precision, and adaptability across various domains such as manufacturing, healthcare, logistics, and exploration. Their performance relies on a synergy of robust hardware design, intelligent control mechanisms, and advanced optimization techniques. This paper explores the key components of autonomous robots, including sensor integration, locomotion systems, control architectures, and decision-making frameworks that enable autonomous operation in dynamic …
Published in Journal of Advancements in Robotics · Vol. 12, Issue 2, 2025 · pp. 22–30 Read article
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Artificial Intelligence in Microbiological Research: Methods, Applications and Implications
Abstract: Artificial Intelligence (AI) is revolutionising microbiological research by enabling the rapid analysis of complex biological data and improving the accuracy, efficiency, and reliability of scientific investigations. Recent advances in machine learning, deep learning, and bioinformatics have transformed AI into a powerful tool for studying microorganisms, their genetic composition, evolutionary patterns, and interactions with hosts and the environment. AI-driven computational models can process large and complex datasets far more efficiently than …
Published in Research and Reviews: A Journal of Microbiology and Virology · Vol. 16, Issue 2, 2026 · pp. 22–36 Read article
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Accurate & Efficient Plant Disease Detection using Transfer Learning with Edge Impulse
Abstract: Transfer learning is a powerful machine learning technique that allows optimization of pre-trained models for related tasks on small datasets. In this research paper, we explore the application of Edge Impulse & transfer learning for plant diseases and aim to detect on edge devices more effectively at low cost. We collected and preprocessed many plant images and used this data to fine-tune a neural network model pre-trained by Edge Impulse. …
Published in Recent Trends in Sensor Research & Technology · Vol. 10, Issue 1, 2023 · pp. 30–41 Read article
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Identification of Papaya Fruit Ripening Process Using AI
Abstract: Identifying the ripening process of papaya fruit using artificial intelligence involves employing machine learning algorithms to analyze various features such as color changes, texture alterations and chemical compositions. This model is capable of analyzing visual cues to determine the stage of ripeness. The dataset compares images of papaya at various ripening stages, and our AI model demonstrated high accuracy in classifying these stages. Employing machine learning algorithms and image processing …
Published in Research & Reviews : Journal of Food Science & Technology · Vol. 13, Issue 2, 2024 · pp. 23–30 Read article
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Predictive Maintenance Strategies for Safety-critical Mechanical Systems
Abstract: Ensuring the reliability and safety of industrial systems is essential, especially in high-risk sectors such as aerospace, manufacturing, and energy. Predictive maintenance (PdM) has become a crucial approach for minimizing operational failures and improving maintenance efficiency. This research introduces an advanced PdM framework that enhances industrial safety by integrating Internet of Things (IoT) technology, machine learning (ML), and big data analytics. By enabling real-time monitoring and predictive fault detection, this …
Published in Journal of Industrial Safety Engineering · Vol. 12, Issue 1, 2025 · pp. 12–17 Read article
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From Quantum Chemistry to Bioprocess Intensification: Advanced Computational Modeling and Enzyme-Based Catalytic Platforms for Green Chemical Transformations
Abstract: Green chemistry requires the development of sustainable catalytic systems that minimize waste generation, reduce energy consumption, and improve process efficiency. Computational chemistry and biocatalysis have emerged as complementary approaches for environmentally responsible chemical manufacturing. Computational techniques such as quantum chemistry, density functional theory (DFT), molecular dynamics, and machine learning provide mechanistic insights into catalytic reactions and support the rational design of efficient catalysts. These approaches enable the prediction of reaction …
Published in Journal of Catalyst & Catalysis · Vol. 13, Issue 2, 2026 · pp. 45–52 Read article
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Automatic Seed Planting Using a Controller
Abstract: Agriculture is a critical sector that requires continuous innovation to meet the increasing global demand for food. Traditional seed planting methods often involve labor-intensive processes that are time-consuming and prone to inefficiencies. This project presents an automatic seed planting machine that integrates ESP32 microcontroller technology with various sensors and actuators to enhance the precision, speed, and efficiency of seed planting. The system is designed to automate seed dispensing, soil digging, …
Published in Journal of Electronic Design Technology · Vol. 16, Issue 2, 2025 · pp. 1–8 Read article
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Machine Learning Approach to Predict the Performability and Emissions of Diesel Engine Fueled with Doped Biodiesel Blend
Abstract: Enhancing the performability and emission characteristics of diesel engines has been a difficult task in light of growing concerns about global warming and other negative effects, as diesel accounts for 70% of global energy demand. In this study, engine performance and exhaust emissions for various fuel blends were thoroughly evaluated using machine learning techniques to predict engine emission and performance behavior. We focused on biodiesel blend and nanoparticle additive concentration …
Published in International Journal of Mechanical Dynamics and Systems Analysis · Vol. 3, Issue 1, 2025 · pp. 1–12 Read article
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Hybrid Machining Processes in Advanced Manufacturing: A Review of Mechanisms and Industrial Applications
Abstract: Hybrid machining processes (HMPs) have gained considerable attention in recent years as an effective approach to address the growing complexity and performance demands of modern manufacturing systems. These processes combine two or more machining techniques—such as mechanical, thermal, chemical, or electrical methods—into a single setup, enabling enhanced productivity, precision, and adaptability, particularly for hard-to-machine materials like ceramics, composites, and superalloys. The integration of distinct energy sources results in synergistic effects …
Published in Journal of Production Research & Management · Vol. 15, Issue 2, 2025 · pp. 19–24 Read article
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Integrated Qualitative Response Assessment System
Abstract: Examinations for universities and yearboards are traditionally administered offline, with a significant number of students opting for subjective exams. This preference stems from the labor-intensive nature of evaluating subjective responses, which requires considerable time and effort from educators. Additionally, subjective grading can be influenced by the evaluator’s mood, leading to inconsistencies. In contrast, multiple-choice and objective questions are prevalent in entrance and competitive exams due to their ease of automated …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 11, Issue 3, 2024 · pp. 18–24 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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Experimental Investigations of EDM Parameters on Machining Square Blind Holes in Maraging Steels
Abstract: Square blind holes have certain qualities that make them useful in fields where accuracy and efficiency are crucial, like aerospace, automotive, molding, and general manufacturing industries where structural integration is required in precise assembly. It is challenging to machine square blind holes with traditional machining due to geometrical complexity as precision is required for sharp corners which is difficult to get at the corners due to tool wear. Electrical discharge …
Published in Journal of Polymer & Composites · Vol. 13, Issue 3, 2025 · pp. 390–397 Read article
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Parametric Optimization of Tool Wear Rate in Side Flushing form of Die Sink EDM of EN-31 Die Steel
Abstract: Electrical discharge machining (EDM) is one of the most extensively used non-conventional material removal processes for manufacturing geometrically complex or hard material parts that are extremely difficult to machine by conventional machining process. EDM researchers have concentrated on achieving faster and more efficient metal removal rate coupled with a reduction in tool wear and improved surface characteristics. In this paper, the operating performance measures of side flushing type of electrical …
Published in Journal of Materials & Metallurgical Engineering · Vol. 1, Issue 1-3, 2011 · pp. 72–80 Read article
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A Machine Learning-based Analysis of Climate Change
Abstract: Climatic variations are a pressing global challenge that demands immediate and comprehensive attention. A wealth of articles has been published on climate change mitigation and adaptation, yet there remains a need for innovative methods to explore the complexities of climatic variations and to devise more efficient and effective strategies for adjustment and alleviation. With technological advancements, machine learning (ML) and deep learning (DL) approaches have derived significant popularity across various …
Published in Research & Reviews : Journal of Space Science & Technology · Vol. 13, Issue 2, 2024 · pp. 1–10 Read article
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Big Data Analytics for Effective Decision Making in Business Intelligence
Abstract: In this study, we explore the significance of Big Data Analytics (BDA) in enhancing decision-making processes within Business Intelligence (BI) frameworks. It involves processing vast volumes of data from various sources, enabling businesses to identify patterns, trends, and correlations that were previously unnoticed. This analytical power enhances strategic planning, customer understanding, operational efficiency, and competitive advantage. Through advanced algorithms and machine learning techniques, businesses can predict future trends, optimize operations, …
Published in Journal of Advanced Database Management & Systems · Vol. 11, Issue 1, 2024 · pp. 23–28 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 · pp. 1207–1231 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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Using Machine Learning for Key phrase Extraction in Digital Libraries
Abstract: Machine learning has revolutionized various aspects of information retrieval, including key phrase extraction in digital libraries. Key phrase extraction is crucial for summarizing and categorizing vast amounts of textual data, enabling efficient search and retrieval processes. This study explores the application of machine learning techniques for automatic key phrase extraction in digital libraries. We review various supervised and unsupervised learning algorithms, including deep learning models, that are employed to identify …
Published in International Journal of Cheminformatics · Vol. 1, Issue 2, 2023 · pp. 8–13 Read article