Composites Part C Open Access最新文献

{"title":"Advanced FEA simulation of GFRP and CFRP responses to low velocity impact: Exploring impactor diameter variations and damage mechanisms","authors":"Muhamad Luthfi Hakim ,&nbsp;Raihan Nafianto ,&nbsp;Ariayana Dwiputra Nugraha ,&nbsp;Ardi Wiranata ,&nbsp;Eko Supriyanto ,&nbsp;Gesang Nugroho ,&nbsp;Muhammad Akhsin Muflikhun","doi":"10.1016/j.jcomc.2024.100541","DOIUrl":"10.1016/j.jcomc.2024.100541","url":null,"abstract":"<div><div>In recent decades, the use of composite materials has experienced a significant increase in various fields. Fiber Reinforced Polymers Composite (FRPC) is one type of composite that is increasingly used due to its versatility and ability to improve product quality. However, FRPC materials have a high susceptibility to Low Velocity Impact (LVI) events, which can cause invisible internal damage such as delamination. LVI occurs when FRPC materials experience a sudden impact with a foreign object at a speed of 1–10 m/s, and can be identified through drop weight impact tests. This research addresses Finite Element Analysis (FEA) simulations to evaluate the mechanical properties of materials due to LVI, following the ASTM D7136 drop weight impact test standard. The variations studied include material types, namely Carbon Fiber Reinforced Polymers (CFRP) and Glass Fiber Reinforced Polymers (GFRP), as well as variations in the diameter of the impactor. The results showed that GFRP has more brittle properties than CFRP, which is indicated by the high absorption energy and larger maximum back surface displacement in CFRP. In addition, the damage in GFRP is more significant as CFRP requires a higher initiation force and energy to trigger and propagate the damage. The simulations also show that as the diameter of the impactor increases, the contact force increases, but the impact time is shorter. In contrast, a smaller diameter impactor penetrates the material more easily, with a smaller impact area and lower impact energy after contact occurs.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":"15 ","pages":"Article 100541"},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707079","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}

{"title":"A machine learning enhanced characteristic length method for failure prediction of open hole tension composites","authors":"Omar A.I. Azeem,&nbsp;Silvestre T. Pinho","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":"15 ","pages":"Article 100524"},"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}

{"title":"Experimental study to unraveling the seismic behavior of CFRP retrofitting composite coupled shear walls for enhanced resilience","authors":"Mohammad Meghdadian ,&nbsp;Amir R. Masoodi ,&nbsp;Mansour Ghalehnovi","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":"15 ","pages":"Article 100523"},"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}

{"title":"Damage detection in composite and plastic thin-wall beams by operational modal analysis: An experimental assessment","authors":"Josué Pacheco-Chérrez ,&nbsp;Manuel Aenlle ,&nbsp;Pelayo Fernández ,&nbsp;Carlos Colchero ,&nbsp;Oliver Probst","doi":"10.1016/j.jcomc.2024.100542","DOIUrl":"10.1016/j.jcomc.2024.100542","url":null,"abstract":"<div><div>The detection and localization of different damage features in thin-wall beam composite and plastic beams using Operational Modal Analysis (OMA) has been demonstrated experimentally. The detection of small damage features using modal analysis techniques is an emerging field, with few experimental OMA-based assessments having been reported so far. The proposed method is based on OMA combined with Stochastic Subspace Identification (SSI) and the enhancement of damage features by Continuous Wavelet Transforms (CWT). A composite thin-wall beam (CTWB) structure in two measurement configurations and a PVC tube in a free-free configuration have been tested. Damage features detected include extra masses attached to the beam, with a range from 9.5 % to 14.0 % of the beam mass, and small cracks perpendicular to the beam axis with lengths of about 4 % of the perimeter of the cross section. Calibration curves relating the strength of the damage signal with the weight of the attached masses have been constructed. Two simultaneous cracks or two masses could be detected as well. The quantification and localization of damage feature along the beam was possible through the use of Gaussian fit surface applied to damage maps obtained with the CWT technique. The width of the Gaussian fit curve was of the order of the distance between accelerometers, but the accuracy, estimated to be around 3 % of the beam length, was found to have sub-grid resolution. The proposed method was shown to work reliably with a relatively coarse measurement grid, potentially allowing for cost-effective Structural Health Monitoring (SHM) approaches.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":"15 ","pages":"Article 100542"},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722285","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}

{"title":"Multi-scale fatigue damage analysis in filament-wound carbon fiber reinforced epoxy composites for hydrogen storage tanks","authors":"Imen Feki ,&nbsp;Mohammadali Shirinbayan ,&nbsp;Samia Nouira ,&nbsp;Robert Tie Bi ,&nbsp;Jean-Baptiste Maeso ,&nbsp;Cedric Thomas ,&nbsp;Joseph Fitoussi","doi":"10.1016/j.jcomc.2024.100537","DOIUrl":"10.1016/j.jcomc.2024.100537","url":null,"abstract":"<div><div>This article presents the findings of a multi-scale experimental study on carbon fiber-reinforced epoxy composites (CFRP) used in lightweight hydrogen storage pressure vessels produced via filament winding. The research employs a combination of tension-tension load-controlled fatigue tests and high-resolution physical-chemical characterization and porosity quantification to assess the impact of porosity on mechanical performance. The findings demonstrate that porosity has a detrimental impact on mechanical properties, acting as nucleation sites for damage mechanisms such as crack initiation, fiber-matrix separation and fiber breakage. At the mesoscopic level, microdefects coalesce into transverse cracks and delamination, resulting in complex failure modes under cyclic loading. The results of the tensile tests demonstrated that the orientation of the fibers has a significant impact on the mechanical behavior of the material. The ±15° configuration demonstrated superior tensile strength and modulus, while the ±30° and multilayer configurations exhibited higher ductility. The results of the fatigue testing confirmed that fiber orientation has a significant impact on fatigue life, with the ±15° configuration proving to be the most resistant. Microscopic analysis indicated that pores act as damage initiation points, accelerating failure through matrix cracking, fiber-matrix debonding, and delamination. This study highlights the need for improved porosity control during manufacturing to enhance the durability of hydrogen storage systems. Additionally, it provides valuable insights for optimizing fiber orientation to improve fatigue performance in practical applications.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":"15 ","pages":"Article 100537"},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662425","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}

{"title":"High-throughput in-situ mechanical evaluation and parameter optimization for 3D printing of continuous carbon fiber composites","authors":"Yuichiro Yuge,&nbsp;Ryosuke Matsuzaki","doi":"10.1016/j.jcomc.2024.100536","DOIUrl":"10.1016/j.jcomc.2024.100536","url":null,"abstract":"<div><div>The mechanical properties of carbon fiber reinforced thermoplastic (CFRTP) molded parts produced by thermal fusion lamination 3D printing vary with printing conditions. This study assesses the influence of the 3D printing parameters on the mechanical properties of resulting CFRTP products through parameter evaluation testing. An in-situ three-point bending test mechanism was developed to enhance the efficiency of these tests, allowing the same 3D printer to handle all processes from printing multiple CFRTP specimens simultaneously to conducting a bending test, reducing manual handling time to about one minute. Using this modified 3D printer, 700 specimens with varying printing conditions were produced, and their flexural strength was measured semi-automatically. Results revealed that the flexural strength of the 3D-printed CFRTP object varied with nozzle temperature, printing pitch, and stacking pitch, but not with printing speed. Machine learning was then employed to predict the maximum flexural strength and determine optimal printing parameters using the collected data as training data.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":"15 ","pages":"Article 100536"},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662422","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}

{"title":"Genetic programming-based algorithms application in modeling the compressive strength of steel fiber-reinforced concrete exposed to elevated temperatures","authors":"Mohsin Ali ,&nbsp;Li Chen ,&nbsp;Qadir Bux Alias Imran Latif Qureshi ,&nbsp;Deema Mohammed Alsekait ,&nbsp;Adil Khan ,&nbsp;Kiran Arif ,&nbsp;Muhammad Luqman ,&nbsp;Diaa Salama Abd Elminaam ,&nbsp;Amir Hamza ,&nbsp;Majid Khan","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":"15 ","pages":"Article 100529"},"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}

{"title":"Assessment of chitosan-PVA hydrogels infused with marine collagen peptides for potential wound healing applications","authors":"Farhana Islam,&nbsp;Ehsanur Rahman,&nbsp;Tanjina Tarannum,&nbsp;Nafisa Islam","doi":"10.1016/j.jcomc.2024.100528","DOIUrl":"10.1016/j.jcomc.2024.100528","url":null,"abstract":"<div><div>Ideal wound dressings should show enhanced moisture management at the wound site, antibacterial and physical barrier, and mechanical robustness. Additionally, it should be easy to apply to the wound and be biocompatible and non-toxic. In this study, a linker-free freeze-thaw procedure was used to create an array of chitosan/PVA hydrogels blended with commercially available marine collagen peptides. Marine collagen peptides (CP) are easily available as by-products of the marine food industry and are an inexpensive and novel source of biomaterial in this field. The different weight ratios of chitosan, PVA, and CP influenced the hydrogel properties such as swelling, gel content, evaporation, and mechanical properties. Furthermore, SEM and ATR-FTIR were used to characterize the hydrogels generated under ideal conditions. After 24 h, the optimum hydrogel (chitosan:PVA:CP ratio of 1:5:1) showed a water absorption capacity of up to 900 %, a gel content of 80 %, and a 40 % evaporation rate. The physical interactions between marine collagen peptide and gel-forming components were validated by ATR-FTIR spectra, and the hydrogel kept a sufficient porous structure for potential wound dressing application. To test the mechanical integrity of the hydrogels, compression testing was carried out showing a compressive modulus of up to ∼40 kPa. The addition of marine collagen peptide in the chitosan/PVA hydrogel increased its wettability, antimicrobial capabilities, and hemostatic properties. Furthermore, the hydrogel preparation procedure is simple and does not use toxic chemicals, serving as a model for developing safe and effective hydrogel wound dressing.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":"15 ","pages":"Article 100528"},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142536091","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}

{"title":"A review of AI for optimization of 3D printing of sustainable polymers and composites","authors":"Malik Hassan ,&nbsp;Manjusri Misra ,&nbsp;Graham W. Taylor ,&nbsp;Amar K. Mohanty","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":"15 ","pages":"Article 100513"},"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}

{"title":"Cure-induced residual stresses and viscoelastic effects in repaired wind turbine blades: Analytical-numerical investigation","authors":"Ayush Varshney ,&nbsp;Daniel Paul ,&nbsp;Puneet Mahajan ,&nbsp;Leon Mishnaevsky Jr.","doi":"10.1016/j.jcomc.2024.100521","DOIUrl":"10.1016/j.jcomc.2024.100521","url":null,"abstract":"<div><div>During scarf repair of wind turbine blades, the difference in coefficients of thermal expansion and chemical shrinkage between the original part and the repair patch leads to the development of residual stresses. These residual stresses are detrimental when the repaired composite structures are subjected to operational cyclic loads and affect their post-repair lifetime. This paper uses a hybrid analytical-numerical model to evaluate the residual stresses in a scarf-repaired composite panel. A Prony series-based viscoelastic model is used to describe the material behaviour of the composite undergoing cure to replicate real-life effects more closely. Experiments on the repaired composite samples and numerical simulations on a model of the same are performed to study the post-repair mechanical behaviour. It is found that the damage initiates at the adhesive interface between the scarf patch and the base composite. The resulting debonding and damage to the base composite leads to the failure of the repaired section.</div></div>","PeriodicalId":34525,"journal":{"name":"Composites Part C Open Access","volume":"15 ","pages":"Article 100521"},"PeriodicalIF":5.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418544","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}