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Thermal and mechanical properties of a high-density polyethylene (HDPE) composite reinforced with wood flour

materials Today: Proceedings 2023年 72卷 3602-3608页

作者： Fatima Ezzahrae, morchid Nacer, Akkouri Latifa, Elfarissi Abdellah, Zamma mohamed, idiri mustapha, Jammoukh Laboratory of Modeling and Simulation of Intelligent Industrial Systems M2S2I Higher Normal School of Technical Education Mohammedia University of Hassan II Casablanca Mohammedia Morocco Technical Center of Plastics and Rubber CTPC Casablanca Morocco Laboratory of Engineering and Materials LIMAT Faculty of Science Ben M'Sik University of Hassan II Casablanca Casablanca Morocco

To keep up with the rate of growth and global demand, it is necessary to manufacture products with high performance and improved durability. Wood-plastic composites (WPCs) are a rapidly growing class of construction materials formed by mixing wood fibers with a polymer matrix at a high temperature. However, significant technical issues remain, such as low thermal resistance, low water resistance, and limited interfacial contact or compatibility between wood fiber and the polymer matrix, which results in poor adhesion of the wood particles, etc. The structure and properties of the matrix/fiber interface can indeed have an impact on the final properties of the composites. To this end, good interfacial adhesion improves stress transmission from the matrix to the fiber, as well as composite performance. Therefore, chemical modification has been the most effective way to chemically bond the matrix to the filler. in this study, the modification is achieved by using maleic anhydride as a coupling agent which promotes the formation of chemical or material bonds between the wood flour (WF) and the polymer matrix. Three WF/high-density polyethylene (HDPE) composite blends with WF contents of 40%, 50%, and 60% were evaluated for density, flexural properties, hardness test, and thermal analysis. Physical tests showed that increasing the wood content of the composite from 40% to 60% increases the density and hardness of the WPC from 1,047 g/cm3 to 1,097 g/cm3 and from 64.5 to 67.7 respectively. On the other hand, increasing the wood flour content to 60% decreases the flexural strength from 37.524 mPa to 27.512 mPa, while the flexural modulus increases from 2176.125 mPa to 3568.284 mPa. in general, the results indicate that the developed WF/HDPE has suitable mechanical and thermal properties for outdoor applications. © 2022

关键词： High density polyethylenes

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impact of thermal-oxidative ageing on high-density polyethylene pipes: Analysis and prediction of service life

Impact of thermal-oxidative ageing on high-density polyethyl...

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innovative Research in Applied science, Engineering and Technology (iRAsET),Conference on

作者： ihssan srii Abdellah Zamma Naoual Belouaggadia Laboratory of Modeling and Simulation of Intelligent Industriel Systems M2S2I Higher Normal School of Technical Education Mohammedia (ENSETM) University of Hassan II Casablanca Mohammedia Morocco Laboratory of Modeling and Simulation of Intelligent Industriel Systems M2S2I Higher Normal School of Technical Education Mohammedia (ENSETM) University of Hassan II Casablanca Builders LA Builders Engineering School Comue University of Normandy Epron France

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

High-density polyethylene (HDPE) is increasingly used in the manufacture of culverts and drainage pipes because of its many advantages. The question of the longevity of HDPE pipes made from recycled resins compared with those made from virgin resins has aroused growing interest in the scientific community. several recent studies have confirmed the high durability of HDPE, estimating its lifespan at over 100 years. However, external factors, such as environmental conditions and operating constraints, can influence this longevity. However, its oxidative thermal ageing is crucial to its durability. This study investigates the effects of thermo-oxidative ageing on the mechanical and physico- chemical properties of PE, highlighting the degradation mechanisms induced by prolonged exposure to high temperatures and oxygen. A method combining experimental tests and predictive modelling was developed to assess the lifetime of the materials. The Arrhenius model was used to estimate degradation kinetics and predict PE lifetime as a function of ageing conditions.

关键词： Degradation Polyethylene Temperature Thermal engineering Predictive models mechanical factors Thermal analysis Resins Chemicals Thermal degradation

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smart materials for Energy Efficiency: A Decade of Bibliometric insights (2014-2024)

Smart Materials for Energy Efficiency: A Decade of Bibliomet...

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innovative Research in Applied science, Engineering and Technology (iRAsET),Conference on

作者： Hanane Boutraih Abdellah Zamma Naoual Belouaggadia Laboratory of Modeling and Simulation of Intelligent Industrial Systems (M2S2I) ENSET University Hassan II of Casablanca Morocco Laboratory of Modeling and Simulation of Intelligent Industrial Systems (M2S2I) ENSET Hassan II University Mohammedia Morocco Builders Lab Builders École d’ingénieurs COMUE Normandie Université Epron France

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

Nowadays, smart materials and Energy Efficiency are among the most current subjects. For this reason, this article seeks to study the relationship between these two topics through a bibliometric analysis using scopus and VOsviewer over the last decade (from 2014 to 2024). Based on these tools, 300 researchers wrote 370 published papers from 227 sources that were listed in scopus. This study presented cumulative data on the number of publications per year, their types, research areas, contributing countries, authors, and keywords. The results of this research are significant as they provide essential information that enhances researchers' understanding of the latest developments and trends in smart materials and energy efficiency.

关键词： Phase change materials Thermoelectric materials intelligent materials Bibliometrics market research Three-dimensional printing Energy efficiency Energy harvesting Waste heat smart materials

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smart materials Unveiled: From Fundamentals to Ai-Driven innovations

Smart Materials Unveiled: From Fundamentals to AI-Driven Inn...

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innovative Research in Applied science, Engineering and Technology (iRAsET),Conference on

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

The development of intelligent or smart materials is crucial to meet the demand for modern materials and to solve issues related to durability, performance, cost, resource availability, and environmental impact. Due to their great potential, they can be employed in many different sectors, such as biomedical, automotive, aerospace, civil industries, and energy. These materials are diverse, including piezoelectric materials, shape-memory alloys, magnetostrictive materials, electro- and magnetorheological fluids, chromic materials, and self-healing materials. in this paper, various kinds of intelligent materials are discussed, along with their benefits, limitations, potential uses, and the important role artificial intelligence has played in the progress of smart material research.

关键词： Technological innovation Electric potential magnetostriction intelligent materials Piezoelectric materials metals magnetic liquids Artificial intelligence sustainable development smart materials

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Assessing the performance of artificial intelligence regression models for predicting the fatigue of a bio-based composite

Assessing the performance of artificial intelligence regress...

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innovative Research in Applied science, Engineering and Technology (iRAsET),Conference on

作者： maryame Lakrade Zakaria mighouar Laidi Zahiri Khalifa mansouri Laboratory of Modelling and Simulation of Intelligent Industrial Systems(M2S2I) ENSET Hassan II University of Casablanca Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

The demand for natural fiber-reinforced composites in engineering design has increased, offering advantages like low density and environmental benefits. However, their long-term behavior, particularly under cyclic loading, remains limited. This study aims to predict the fatigue behavior of composites reinforced with natural fiber using several predictive models of regression trained on a database of studies. This model reduces experimental testing and improves prediction quality by avoiding computationally intensive data points.

关键词： Biological system modeling Natural fibers Computational modeling Predictive models Optical fiber networks Fatigue Data models Vectors Artificial intelligence Testing

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Thermal and mechanical properties of a high-density polyethylene (HDPE) composite reinforced with wood flour

Thermal and mechanical properties of a high-density polyethy...

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关键词： High density polyethylenes

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Biomaterials and 3D Printing: An innovative synergy for the manufacturing of the Future

Biomaterials and 3D Printing: An Innovative Synergy for the ...

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innovative Research in Applied science, Engineering and Technology (iRAsET),Conference on

作者： Abdelghani LAKHDAR Zineb LAABiD Hassan sAmRi Khalifa mANsOURi Ali siADAT M2S2I Modeling and Simulation of Intelligent Industrial Systems Laboratory ENSET Mohammedia Hassan II University (UH2) Casablanca Morocco Laboratoire LCFC at Arts et Métiers Institute of Technology France

ISBN: (数字)9798350309508

ISBN: (纸本)9798350309515

in The introduction of biomaterials into the realm of 3D printing represents a significant advancement with manifold implications in the fields of medicine, biomedical research, and customized manufacturing. 3D printing, also known as additive manufacturing, provides an innovative method for creating three-dimensional objects with precision and complexity, layer by layer. in this study, we conducted a numerical simulation of a biomaterial composed of a bioload in the form of chicken feathers randomly distributed in a matrix of PLA (polylactic acid), a material widely used in 3D printing. in this article, we numerically investigated 3D printing technology to create a biomaterial before advancing to a bio-printing stage. The results indicate an increase in stiffness accompanied by a reduction in density with an increase in the percentage of bioload, resulting in a decrease in density of approximately 0.1 g/cm3 and an increase in Young's modulus of 0.5 GPa. Aligned with the industrial trend focused on decreasing weight relative to volume, along with the potential utilization of chicken feather and PLA waste during recycling, explored in forthcoming articles.

关键词： Feathers Young's modulus Three-dimensional displays Programmable logic arrays Three-dimensional printing market research Recycling

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Deep Learning Based Techniques for Breast Cancer Classification: A systematic Review

Deep Learning Based Techniques for Breast Cancer Classificat...

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innovative Research in Applied science, Engineering and Technology (iRAsET),Conference on

作者： Chaima Elmejgari Younes NADiR mohammed Qbadou Modeling and Simulation of Intelligent Industrial Systems Laboratory (M2S2I) Ecole Normale Supérieure de l'Enseignement Technique (ENSET) Hassan II University of Casablanca (UH2C) Mohammedia Morocco Modeling and Simulation of Intelligent Industrial Systems Laboratory (M2S2I) Ecole Normale Supérieure de l'Art et de Design (ENSAD) Hassan II University of Casablanca (UH2C) Mohammedia Morocco

ISBN: (数字)9798350309508

ISBN: (纸本)9798350309515

Cancer represents a disease characterised by the uncontrolled growth of abnormal cells, often due to genetic mutations and environmental factors. it has the potential to infiltrate surrounding tissue and metastasize to other areas of the body. There are various types of cancer with distinct characteristics, risk factors, and treatment options. Early detection improves survival rates and reduces treatment costs. Advances in imaging technology have facilitated diagnosis, Computer-assisted systems utilize various modalities such as CT scans, mRi, ultrasound, mammography, X-rays, and histopathology to identify *** learning has shown impressive results in processing large data sets within biomedicine over the past decade. The purpose of this review is to analyze and evaluate a range of pertinent research papers focusing on the application of deep learning methods for cancer detection, following the Prisma methodology. Additionally, we aim to assess current approaches to cancer diagnosis with a particular focus on techniques developed for breast cancer detection.

关键词： Deep learning Ultrasonic imaging systematics Reviews Histopathology magnetic resonance imaging Genetic mutations

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Classification of indoor CO2 Levels: Exploring the impact of Humidity, Temperature, and Occupancy on Air Quality Using machine Learning model

Classification of Indoor CO2 Levels: Exploring the Impact of...

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mediterranean smart Cities Conference (msCC)

作者： Dani Yassine Belouaggadia Naoua Jammoukh mustapha Ennadafy Hamza Laboratory: Modeling and Simulation of Intelligent Industrial Systems. (M2S2I) University Hassan II Casablanca Mohammedia Morocco Builders Lab Builders Ecole d'ingénieur 14600 Epron Laboratory: Modeling and Simulation of Intelligent Industrial Systems. (M2S2I) University Hassan II Casablanca Mohammedia Morocco Centre Technique de Plasturgie et de Caoutchouc (CTPC) University of Hassan II Casablanca Casablanca Morocco Signals Distributed Systems and Artificial Intelligence Laboratory (SSDIA) Hassan II University Casablanca Casablanca Morrocco

ISBN: (数字)9798350374001

ISBN: (纸本)9798350374018

This study investigates the influence of humidity, temperature, and occupancy on indoor CO2 levels using machine learning models. Utilizing a dataset sourced from open-access data, the analysis encompasses correlation matrix exploration, logistic regression modeling, and statistical comparisons between CO2 emissions and other variables. Results reveal strong correlations between humidity, temperature, and CO2 levels, with light and occupancy emerging as significant contributor. Logistic regression modeling achieves high accuracy (97.39%) in predicting CO2 emission classes. Descriptive statistics highlight the pronounced impact of variables on CO2 levels, with humidity and occupancy exhibiting notable influence. This research provides insights into factors influencing indoor CO2 levels and suggests practical implications for iAQ improvement strategies.

关键词： Productivity Logistic regression Temperature Correlation smart cities Humidity machine learning Ventilation indoor environment Carbon emissions

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Artificial intelligence in solar stills: A Bibliometric Analysis of Trends, Applications, and Future Prospects

Artificial Intelligence in Solar Stills: A Bibliometric Anal...

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innovative Research in Applied science, Engineering and Technology (iRAsET),Conference on

作者： Ait Baha Asmaa Omar Bouattane Naoual Belouaggadia Abdellah Zamma Laboratory of Modeling and Simulation of Intelligent Industrial Systems (M2S2I) ENSET Hassan II University of Casablanca Morocco Laboratory of Electrical Engineering and Intelligent Systems (IESI) ENSET Hassan II University of Casablanca Morocco Builders Lab Builders École D’ingénieurs COMUE Normandie Université Epron France

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

solar stills are relatively affordable, renewable energy distillers with low maintenance, which can help solve water problems in remote areas. Artificial intelligence (Ai) has enhanced efficiency through adaptive control, the adjustment of operational parameters, and the development of forecasting methods and tools. Ai solutions, such as hybrid models and machine learning, enhance design, automate tasks, and reduce costs by optimizing insulation and tilt angle, among other factors. There are also additional complementary technologies, such as nanotechnology and phase change materials, that improve sustainability and performance. in this paper, we review the adoption of artificial intelligence technology in solar still research, utilizing a bibliometric technique to analyze 2216 articles published between 2019 and 2024. The results show that the development of Ai-integrated solar still technology experienced significant growth between 2019 and 2024, demonstrating how artificial intelligence enhances the efficiency, design, and performance of solar desalination systems. The findings also reveal that The Journal of Cleaner Production and Desalination are the most prominent sources in this field, driving knowledge dissemination and innovation. Additionally, the results highlight that China, india, and Egypt are the most active contributors to this research domain, reflecting their strong commitment to tackling water scarcity and promoting sustainable desalination.

关键词： Phase change materials Renewable energy sources Technological innovation Bibliometrics Desalination Production maintenance Artificial intelligence Optimization Nanotechnology

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