Search
251 articles for “Predictive Material Modeling”
-
Improved Prediction of Fluid Contacts using Calibrated Material Balance Models
Abstract: The demand for oil has been on the high in the recent past and will continue as search for sustainable alternative energy sources intensifies. The exploration and exploitation of oil from subsurface reservoirs have posed several environmental challenges which include flaring and improper water disposal to name a few, caused by excessive production of gas and water. Hence, it is important to establish a reservoir performance monitoring scheme that will …
Published in Journal of Petroleum Engineering & Technology · Vol. 4, Issue 2, 2014 · pp. 25–36 Read article
-
Optimizing Mechanical and Durability Properties of Eco-Friendly Composite Materials Using Recycled Fillers and ML Techniques
Abstract: The increasing demand for sustainable construction materials has intensified the exploration of recycled fillers as partial or full replacements for natural aggregates in composite materials. This study investigates the mechanical and durability performance of polymer matrix composites incorporating processed recycled fillers derived from construction and demolition (C&D) waste. Three distinct processing methods were employed to prepare the recycled fillers: untreated (URF), single processed (SPRF), and double processed (DPRF), with replacement …
Published in Journal of Polymer & Composites · Vol. 13, Issue 5, 2025 · pp. 269–309 Read article
-
Predictive Modeling and Optimization of Tensile and Flexural Strength in FDM 3D Printing Using Decision Trees and Bayesian Optimization.
Abstract: This research investigates predictive modelling and optimization technique for the tensile and flexural strength of PlA (Poly Lactic Acid) in Fused Deposition Modelling (FDM) 3D printing. Employing Decision Trees and Bayesian Optimization enhances comprehension and control of 3D printing process. Precise model predicts PLA material properties based on input parameters. Methodology involves rigorous data preprocessing, encompassing, cleaning, transformation, and normalization. Hyperparameter optimization via grid search systematically explores configurations, optimizing model …
Published in Journal of Polymer & Composites · Vol. 11, Issue 12, 2023 · pp. 203–214 Read article
-
AI-integrated Responsive Polymer Composites for Controlled Drug Delivery
Abstract: In the current biomedical engineering, it has been established that the development of smart drug delivery systems has become a paramount of relevance especially in ensuring precise, controlled and targeted therapeutic effects. This paper proposes a responsive polymer composite architecture with built-in AI, which is used to deliver drugs in a controlled manner and involves the development of advanced material design and predictive modeling based on data. Biocompatible materials and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
-
Revolutionizing Low Carbon Concrete: Innovations at the Nexus of Computational Science, Data Analytics, and Sustainable Technologies
Abstract: Concrete production accounts for 7% of global CO2 emissions necessitating low carbon innovations to curb exponential demand threatening climate commitments. This research reviews sustainable construction literature integrating computational simulations, big data infrastructure monitoring and alternative process redesign. Analysis reveals 30-50% reductions achievable through combined use of industrial ecologies, smarter sensing coordinated with ML optimization and novel binders like alkali-activated geopolymers. Rigorous LCA quantification verifies environmental superiority over conventional formulations. Case …
Published in Journal of Construction Engineering, Technology & Management · Vol. 13, Issue 3, 2023 · pp. 16–23 Read article
-
Implement Explainable Machine Learning to Improve Conductivity in Polymer-CNT Nanocomposites: Supporting Adaptive, Flexible, and Long-Lasting IoT Wrap-Around Electronics Applications
Abstract: The rapid growth of Internet of Things (IoT) technologies requires electronic components that are adaptable, lightweight, and durable, and that can continue to function well in diverse contexts and circumstances. Polymer–carbon nanotube (CNT) nanocomposites have become interesting choices for these kinds of uses because they are more flexible, conduct electricity better, and can be made to fit specific needs. However, improving conductivity in these heterogeneous systems remains a major challenge …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 238–254 Read article
-
Artificial Intelligence-Based Optimization of Mechanical and Biocompatible Properties in Polymer Composite Implants
Abstract: Artificial Intelligence (AI) has already become a ground-breaking tool of streamlining polymer composite implants to enhance both mechanical strength and biocompatibility simultaneously. This paper recommend an AI-based multi-objective optimization model, which integrates the selection of materials, structural modelling, and biological evaluation. The in vitro biocompatibility indicators, including cytotoxicity and cell adhesion, can be used to model mechanical behavior, e.g. stress-strain behavior and fatigue behavior. To arrive at an optimal material …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
-
Emerging Trends in Membrane-Based Gas Separation Technologies
Abstract: Membrane technology has emerged as a groundbreaking solution in various fields, revolutionizing industries such as water treatment, energy production, biomedicine, and environmental protection. Over the past few decades, significant advancements have been made in membrane materials, fabrication techniques, and performance optimization. With the growing global demand for efficient and sustainable separation processes, research has increasingly focused on enhancing membrane permeability, selectivity, and durability to improve performance across various industries, including …
Published in International Journal of Membranes · Vol. 2, Issue 1, 2025 · pp. 16–22 Read article
-
Corrosion of Reinforcement in Concrete and Steel: Review Paper
Abstract: Present paper review about all the aspects of corrosion of reinforcement. Like methods for controlling the corrosion, Alternatives materials, numerical modelling for predicting the corrosion rates. Different admixtures used for controlling corrosions. Epoxy materials compared to conventional cementite materials shows very small shrinkage defect. For damage prediction rate of corrosion is very crucial input parameters. Corrosioninfluencing factors such as cover cracking, resistivity, concrete quality and cover depth should be incorporated …
Published in Journal of Structural Engineering and Management · Vol. 3, Issue 2, 2016 · pp. 43–50 Read article
-
Implement Artificial Intelligence and Machine Learning for Engineering Design, Predictive Modeling, and Optimizing Polymer Nanocomposites
Abstract: Polymer nanocomposites are high performance engineered materials obtained by inclusion of nano-sized fillers into the polymer matrix to enhance mechanical, thermal, electrical, barrier and functional properties. However, the complex and non-linear interactions among polymer chemistry, nanofiller characteristics, filler concentration, dispersion, interfacial bonding and processing conditions make it challenging to anticipate and maximize their properties. Artificial intelligence (AI) and machine learning (ML) offer powerful data-driven solutions to these difficulties by establishing …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
-
Machine Learning-Based Quantification of Polymer Structure Property Relationships for Predictive Material Design
Abstract: Polymer structures exhibit complex, hierarchical arrangements that strongly influence macroscopic properties, yet consistent quantification remains challenging due to nonlinear interactions and limited unified modeling strategies. Existing approaches inadequately capture generalized structure–property mappings across diverse polymer systems. This research aims to establish a machine learning-based quantification model for polymer structure–property relationships to support predictive material design. A Polymer Structure Property Dataset of 5,000 polymer samples includes structural descriptors and experimentally measured …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 737–754 Read article
-
Sustainable Biomedical Polymer Composites Designed through Artificial Intelligence Approaches
Abstract: The development of sustainable biomedical polymer composites has become one solution that can be used to combat increasing environmental issues that have been presented by traditional medical materials without compromising functional performance. Implementation of the artificial intelligence (AI) in material design presents a paradigm shift of data-driven development, which improves the efficiency, accuracy, and scalability of composite development. The given work can serve as a universal guideline in developing biodegradable …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
-
Advanced Computational Models for Predicting Molecular Interactions
Abstract: Understanding molecular interactions is essential for a number of disciplines, including biochemistry, materials science, and medication development. Traditional experimental methods, while accurate, are often time-consuming and expensive. Advanced computational models have emerged as powerful tools to predict molecular interactions efficiently. In order to predict the behavior and interactions of molecules at the atomic and subatomic levels, this paper reviews the most recent developments in computational techniques, such as machine learning …
Published in International Journal of Advance in Molecular Engineering · Vol. 2, Issue 1, 2024 · pp. 8–13 Read article
-
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
-
Role of Artificial Intelligence in Structural Health Monitoring-A Brief Evaluation
Abstract: Artificial intelligence (AI) refers to the capacity of a machine or a computer to ‘think’ or reason in the way a human would, utilizing experience, learned facts, and flexible rules to solve problems that may not fit the standard outlines for a normal algorithm. From this follows the utilization of AI in various sectors, such as the information technology (IT) industry, media, healthcare and medicine, logistics, environmental sustainability, finance, business, …
Published in Journal of Structural Engineering and Management · Vol. 13, Issue 1, 2026 · pp. 34–39 Read article
-
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
-
Artificial Intelligence for Polymer and Nanocomposite Materials: Performance Prediction, Manufacturing Optimization, and Future Perspectives
Abstract: The exceptional mechanical properties, design flexibility, and lightweight nature of polymer composite and nanocomposite materials make them indispensable in a wide range of applications, including aerospace, automotive, construction, biomedical, and energy sectors. The optimization of the strength, durability, and manufacturing efficiency of polymer composite and nanocomposite materials is highly challenging because their performance depends on matrix composition, reinforcement type, fiber or nanoparticle distribution, interfacial interactions, processing conditions, and environmental factors. …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
-
Tribological Behaviour of PTFE based Composite Materials with Different Filler Materials: A Combine Numerical and Experimental Approach
Abstract: This study investigates the tribological behaviour of Polytetrafluoroethylene (PTFE) composites reinforced with various fillers, utilizing the Archard wear model to analyse wear mechanisms and predict wear rates. PTFE is widely recognized for its excellent chemical resistance and low friction, but its inherent wear resistance is relatively poor. To enhance its tribological properties, fillers such as glass fibres, carbon fibres, bronze, and graphite were incorporated into the PTFE matrix. In the …
Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 547–557 Read article
-
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
-
Data-Driven Digital Twin Model for Real-Time Strength Estimation in Polymeric Materials
Abstract: The real-time prediction of mechanical properties in polymeric materials is essential for ensuring quality, consistency, and operational efficiency in modern manufacturing systems. As industrial processes become increasingly complex, traditional trial-and-error approaches to material characterization are no longer sufficient to meet the demands of high-throughput production environments. This study introduces a digital twin-integrated machine learning approach for the real-time estimation of tensile strength in polymeric materials by combining simulation-driven insights with …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 246–257 Read article