Composites Part C Open Access最新文献

Lightweight 3D-printed heaters: design and applicative versatility 轻质 3D 打印加热器：设计和应用多样性

IF 5.3

Composites Part C Open Access Pub Date : 2024-10-01 DOI: 10.1016/j.jcomc.2024.100527

{"title":"Lightweight 3D-printed heaters: design and applicative versatility","authors":"","doi":"10.1016/j.jcomc.2024.100527","DOIUrl":"10.1016/j.jcomc.2024.100527","url":null,"abstract":"<div><div>This paper proposes a new strategy for designing a 3D-printed heater that can overcome some criticalities of current commercial heater devices for application in the transport and energy sectors. A semiconductive nanocomposite material, acrylonitrile-butadiene-styrene filled with carbon nanotubes (ABS-CNT), was processed via Fused Filaments Fabrication (FFF). The printing was set to favor the current flow along the printing direction, consequently increasing the material's electrical conductivity. 3D-printed heater geometry, equivalent to several electrical resistances (resistive branches) connected in parallel, was optimized by varying the width, thickness, lengths, and number of branches. The adopted approach resulted in a flexible and scalable low-equivalent resistance value heater. Moreover, the optimized heater's flexibility allows it to be integrated into a curved fiberglass composite. Joule heating tests were experimentally performed and theoretically simulated by a multi-physics model. The numerical prediction resulted in good agreement with the experimental data. The results encourage the application of 3D-printed heaters as functional patches for the thermal management of different devices/components, including complex-shape composite structures.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142536090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Experimental investigation and modelling of the nonlinear creep behaviour of additive-manufactured carbon fibre-reinforced polyethylene terephthalate (CF-PET). 对添加剂制造的碳纤维增强聚对苯二甲酸乙二酯（CF-PET）的非线性蠕变行为进行实验研究和建模。

IF 5.3

Composites Part C Open Access Pub Date : 2024-10-01 DOI: 10.1016/j.jcomc.2024.100530

{"title":"Experimental investigation and modelling of the nonlinear creep behaviour of additive-manufactured carbon fibre-reinforced polyethylene terephthalate (CF-PET).","authors":"","doi":"10.1016/j.jcomc.2024.100530","DOIUrl":"10.1016/j.jcomc.2024.100530","url":null,"abstract":"<div><div>In this paper, the nonlinear creep behaviour of additive-manufactured carbon fibre-reinforced polyethylene terephthalate (CF-PET) is characterised using experimental, theoretical and computational methods. The experimental approach investigates the influence of infill orientations on the creep deformation of the material. For the study, samples at 0°, 45<sup>○</sup>, and 90° infill orientations are produced with 90% infill density using fused filament fabrication (FFF). The infill orientation parameter highly influences the creep behaviour. Increasing the infill orientation from 0° to 90° monotonically improves the creep resistance of the material, which can be explained by orientation of the fibre-matrix reinforcement towards the uniaxial stresses. Surface examinations of creep-ruptured samples via scanning electron microscopy (SEM) reveal that a combination of matrix failure, fibre pull-out, fibre-matrix debonding, inter-layer debonding, and the presence of voids cause the fractures. Based on the experimental data, the primary and secondary creep responses are modelled theoretically and computationally. The theoretical model is based on the dependence of the material's creep on stress and time parameters at the transient and steady state stages. Combined stress and time functions are used to model the creep of the material. Parallelly, two-dimensional (2D) finite element (FE) analyses are made on COMSOL Multiphysics to model the creep computationally. The approach is based on the superposition of Norton's and Garofalo's creep models with predefined time hardening property. The results of the modelling are in good agreement with the experimental findings, showing a maximum of 1.04 % for the theoretical, and 2.9 % for the computational approaches.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparative analysis of delamination resistance in CFRP laminates interleaved by thermoplastic nanoparticle: Evaluating toughening mechanisms in modes I and II 热塑性纳米粒子交错复合材料层压板抗分层能力的比较分析：评估模式 I 和模式 II 的增韧机制

IF 5.3

Composites Part C Open Access Pub Date : 2024-10-01 DOI: 10.1016/j.jcomc.2024.100518

引用次数: 0

Characteristics and evaluation of recycled waste PVCs as a filler in composite structures: Validation through simulation and experimental methods 作为复合材料结构填充物的回收废聚氯乙烯的特性和评估：通过模拟和实验方法进行验证

IF 5.3

Composites Part C Open Access Pub Date : 2024-10-01 DOI: 10.1016/j.jcomc.2024.100525

{"title":"Characteristics and evaluation of recycled waste PVCs as a filler in composite structures: Validation through simulation and experimental methods","authors":"","doi":"10.1016/j.jcomc.2024.100525","DOIUrl":"10.1016/j.jcomc.2024.100525","url":null,"abstract":"<div><div>Solar Cell as a renewable energy utilization in today's era is considered a suitable choice due to encompass sustainability, environmental preservation, and energy processing efficiency. Solar cells have a finite lifespan that need replacement to maintain energy absorption efficiency. Unfortunately, discarded materials are often underutilized or improperly disposed of. In this study, used photovoltaic solar cell materials are explored as reinforcements in composites. The results showed that 4 % cell filler specimen exhibited highest ultimate tensile strength (UTS) with 51.43 MPa. Followed by Compression strength with 35.38 MPa and flexural strength with 45.54 MPa. SEM/EDS analysis of PV filler specimens revealed the dominance of Carbon (C) and Silica (Si) materials, comprising over 60 %. FT-IR analysis indicated varying compound bond intensities affecting polymerization and material strength under applied forces. Simulation results showed a difference of <2 % when compared to experimental testing outcomes. The current study benefited in environmental conservation efforts through waste reduction and the reuse of recycled materials and are listed in several applications such as in wind turbine, structures, lightweight laminates, automotive structures, and sport equipment.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142437707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A review of repairing heat-damaged RC beams using externally bonded- and near-surface mounted-CFRP composites 使用外部粘接和近表面安装的纤维增强塑料复合材料修复热损伤 RC 梁的综述

IF 5.3

Composites Part C Open Access Pub Date : 2024-10-01 DOI: 10.1016/j.jcomc.2024.100519

引用次数: 0

Hybrid lattice structure with micro graphite filler manufactured via additive manufacturing and growth foam polyurethane 通过增材制造和生长泡沫聚氨酯制造出带有微石墨填料的混合晶格结构

IF 5.3

Composites Part C Open Access Pub Date : 2024-10-01 DOI: 10.1016/j.jcomc.2024.100516

{"title":"Hybrid lattice structure with micro graphite filler manufactured via additive manufacturing and growth foam polyurethane","authors":"","doi":"10.1016/j.jcomc.2024.100516","DOIUrl":"10.1016/j.jcomc.2024.100516","url":null,"abstract":"<div><div>The utilisation of lightweight structures is a common practice across a range of disciplines, including the construction of light steel frames, sandwich panels, and transportation infrastructure, among others. The advantages of lightweight structures include design flexibility, weight reduction, and the sustainability of materials that can be easily recycled. However, these advantages also present significant weaknesses. Compared to solid materials with compact weight, lightweight structures do not have the same characteristics. With the reduction in material weight, the strength of the lightweight structure decreases significantly compared to solid materials. In this study, the lightweight structure was made using additive manufacturing and reinforced with solid Composite Polyurethane Foam reinforced with graphite filler expanded into the lightweight structure. The results showed that in the compression test, the mixture with 2 % graphite filler had the highest value of 2.5 kN. The highest hardness test on the specimen with a 2 % graphite mixture was 19.8 HA. FT-IR testing showed that the carbon bonds from graphite in the 2 % specimen had the highest intensity. The test results showed that the addition of Polyurethane Foam into the structure could enhance material strength effectively without adding significant material weight.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142357480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Experimental study to unraveling the seismic behavior of CFRP retrofitting composite coupled shear walls for enhanced resilience 揭示 CFRP 加固复合耦合剪力墙抗震行为的实验研究

IF 5.3

Composites Part C Open Access Pub Date : 2024-10-01 DOI: 10.1016/j.jcomc.2024.100523

{"title":"Experimental study to unraveling the seismic behavior of CFRP retrofitting composite coupled shear walls for enhanced resilience","authors":"","doi":"10.1016/j.jcomc.2024.100523","DOIUrl":"10.1016/j.jcomc.2024.100523","url":null,"abstract":"<div><div>This study focuses on the empirical examination of the nonlinear seismic performance of carbon fiber-reinforced polymer (CFRP)-strengthened composite coupled reinforced concrete (RC) shear walls. The experimental setup involves testing the structure in two distinct states, wherein CFRP sheets are utilized for retrofitting and reinforcement. In the initial phase, three samples undergo reinforcement utilizing distinct patterns of CFRP sheets. In the subsequent stage, an additional trio of specimens is fabricated and tested without the application of CFRP sheets. Subsequently, all structures are exposed to a load equivalent to 60 % of their flexural capacity. Following this, the tested specimens undergo retrofitting with CFRP sheets, utilizing the same patterns as in the initial phase. The retrofitted composite coupled shear walls are then subjected to retesting. The principal aim of CFRP retrofitting is to amplify the flexural and shear capacities of the specimens, empowering them to endure heightened seismic loads in comparison to their original configurations. This research contributes by evaluating ductility, ultimate strength, energy dissipation, and construction costs associated with composite coupled steel plate-concrete shear walls. All specimens underwent cyclic loading in accordance with the ATC-24 guidelines [1], which provide standard protocols for testing the cyclic performance of structural components. These guidelines, outline procedures for simulating seismic loading conditions in laboratory settings to evaluate the performance of structural systems under cyclic loading. Finally, a parametric study explores the impact of CFRP sheets and their adhesion patterns on the seismic behavior of composite coupled shear walls. The selection of the optimal retrofitting scheme considers the construction cost of each specimen based on the total area of CFRP sheets utilized.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A machine learning enhanced characteristic length method for failure prediction of open hole tension composites 用于开孔拉伸复合材料失效预测的机器学习增强特征长度法

IF 5.3

Composites Part C Open Access Pub Date : 2024-10-01 DOI: 10.1016/j.jcomc.2024.100524

{"title":"A machine learning enhanced characteristic length method for failure prediction of open hole tension composites","authors":"","doi":"10.1016/j.jcomc.2024.100524","DOIUrl":"10.1016/j.jcomc.2024.100524","url":null,"abstract":"<div><div>The characteristic length method is a non-local approach to predicting the failure of open and closed-hole composite features. This method requires the determination of the linear elastic stress field of the composite laminate at its failure load. Typically, this requires computationally expensive progressive damage and linear elastic modelling and simulation with finite element analysis (FEA). In this study, we demonstrate the benefit of machine learning methods to efficiently and accurately predict characteristic lengths of composite laminates with open holes. We find that the prediction of the load-displacement profile usefully informs ultimate failure load prediction. We also find that linear elastic stress fields are more accurately predicted using a long-short term memory neural network rather than a convolutional decoder neural network. We show indirect prediction of characteristic length, via prediction of failure loads and linear elastic stress fields independently, results in more flexible, interpretable and accurate results than direct prediction of characteristic length, given sufficient training data. Our machine learning-assisted characteristic length method shows over five orders of magnitude of time-saving benefit compared to FEA-based methods.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142437706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A review of AI for optimization of 3D printing of sustainable polymers and composites 人工智能在优化可持续聚合物和复合材料 3D 打印方面的应用综述

IF 5.3

Composites Part C Open Access Pub Date : 2024-10-01 DOI: 10.1016/j.jcomc.2024.100513

{"title":"A review of AI for optimization of 3D printing of sustainable polymers and composites","authors":"","doi":"10.1016/j.jcomc.2024.100513","DOIUrl":"10.1016/j.jcomc.2024.100513","url":null,"abstract":"<div><div>In recent years, 3D printing has experienced significant growth in the manufacturing sector due to its ability to produce intricate and customized components. The advent of Industry 4.0 further boosted this progress by seamlessly incorporating artificial intelligence (AI) in 3D printing processes. As a result, design precision and production efficiency have significantly improved. Although numerous studies have explored the integration of AI and 3D printing, the literature still lacks a comprehensive overview that emphasizes material selection and formulation, predictive modeling, design optimization, and quality control. To fully understand the impacts of these emerging technologies on advanced manufacturing, a thorough assessment is required. This review aims to examine the intersection of AI and 3D printing to create a technologically advanced and environment-friendly manufacturing environment. It examines factors such as material, process efficiency, and design enhancements to highlight the benefits of combining these technologies. By focusing on predictive modeling, material selection and quality control, this analysis aims to unlock the potential for a sustainable and efficient 3D printing process. This review provided a thorough analysis of the challenges and potential benefits, proving valuable for academics and practitioners alike. It presents solutions that may establish a foundation for sustained growth and outlines a strategy for leveraging 3D printing and AI capabilities in the manufacturing sector.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Genetic programming-based algorithms application in modeling the compressive strength of steel fiber-reinforced concrete exposed to elevated temperatures 基于遗传编程算法的钢纤维加固混凝土高温抗压强度建模应用

IF 5.3

Composites Part C Open Access Pub Date : 2024-10-01 DOI: 10.1016/j.jcomc.2024.100529

{"title":"Genetic programming-based algorithms application in modeling the compressive strength of steel fiber-reinforced concrete exposed to elevated temperatures","authors":"","doi":"10.1016/j.jcomc.2024.100529","DOIUrl":"10.1016/j.jcomc.2024.100529","url":null,"abstract":"<div><div>Steel-fiber-reinforced concrete (SFRC) has replaced traditional concrete in the construction sector, improving fracture resistance and post-cracking performance. However, extreme temperatures degrade concrete's material characteristics including stiffness and strength. The construction industry increasingly embraces machine learning (ML) to estimate concrete properties and optimize cost and time accurately. This study employs independent ML methods, gene expression programming (GEP), multi-expression programming (MEP), XGBoost, and Bayesian estimation model (BES) to predict SFRC compressive strength (CS) at high temperatures. 307 experimental data points from published studies were utilized to develop the models. The models were trained using 70 % of the dataset, with 15 % for validation and 15 % for testing. Iterative hyperparameter adjustment and trial-and-error refining achieved optimum predictions. All the models were evaluated using correlation (R) values for training, validation, and testing datasets. MEP showed slightly lower R-values of 0.923, 0.904, and 0.949 than GEP, which performed consistently with 0.963, 0.967, and 0.961. XGBoost had the greatest training R-value of 0.997 but dropped in validation (0.918) and testing (0.896). BES model exhibited commendable performance with scores of 0.986, 0.944, and 0.897. GEP and XGBoost exhibited great accuracy, with GEP sustaining constant accuracy across all datasets, highlighting its potency in predicting CS. Interpreting model predictions using SHapley Additive exPlanation (SHAP) highlighted temperature over heating rate. CS improved significantly as the steel fiber volume fraction (Vf) reached 1.5 %, plateauing thereafter. The proposed models are valid and accurate, providing designers and builders with a practical and adaptable method for estimating strength in SFRC structural applications, particularly under high-temperature conditions.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142536089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0