Experimental study of a composite beam externally bonded with a carbon fiber-reinforced plastic plate

Zhang, Wei; Kang, Shuaiwen; Liu, Xiang; Lin, Benqing; Huang, Yiqun

doi:10.1016/j.jobe.2023.106522

Cited by 78 publications

(14 citation statements)

References 31 publications

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“…In this paper, various regression models have been tested to determine the best fit between the explanatory and response variables, and it is noticed that a second-order regression model would provide the most suitable resolution for the current problem. A second-order bivariate regression model can be represented using equation (1).…”

Section: Discussionmentioning

confidence: 99%

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Regression model-based parametric analysis of drilling of multi-walled carbon nanotubes-added glass fiber composite laminates

Panchagnula,

Sharma,

Cep

et al. 2024

Mater. Res. Express

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Multi-walled carbon nanotubes (MCNTs)-enhanced glass fiber composite (GFC) laminates are among the most promising materials for fulfilling various structural and non-structural requirements. They have also shown exceptional functional applications as excellent electrical and thermal conductors, as well as electromagnetic interference shielding materials. The present work primarily focuses on developing regression models for the drilling process of 0.3 wt% MCNTs-GFC laminates. For experimentation, three different coated drills—carbide, TiCN-coated, and TiAlN-coated—are used under both dry and chilled air cutting environments. The lowest thrust force, torque, and delamination factor were observed at a feed rate of 10 mm/min and a speed of 1500 RPM using a TiCN-coated drill in a chilled air environment. Regression analysis reveals that feed rate significantly influences thrust force, as justified by the R² value, which is above 90% for the selected cutting conditions. The corresponding t and F statistics values indicate the statistical significance of the relevant explanatory factors. The efficiency of the developed models is further validated by considering the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values, which are 136.9 and 144.7, respectively. These values indicate a good regression fit and likelihood of the models for data prediction. Additionally, there is a strong correlation (coefficient > 0.85) between thrust force and delamination factor under the selected cutting environments. Concurrently, the developed regression models are simulated and evaluated for random experiments (Nos. 87, 125, 187, 243, 244, and 399), and the predicted responses closely match the experimental values.

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Section: Discussionmentioning

confidence: 99%

“…In the realm of engineering, especially in industries like automobile, aerospace, construction, and sports, there is a constant demand for materials that blend low density with superior strength [1,2]. This quest has led to the rise of composite materials, particularly those enhanced by the integration of nano-fillers [3].…”

Section: Introductionmentioning

confidence: 99%

Regression model-based parametric analysis of drilling of multi-walled carbon nanotubes-added glass fiber composite laminates

Panchagnula,

Sharma,

Cep

et al. 2024

Mater. Res. Express

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“…Silk fibroin from Bombyx mori silk was combined with an epoxy resin polymer system to study molecular interactions at the silk fiber–matrix interface . Zhang et al investigated steel fiber-reinforced concrete (SFRC), ordinary concrete (OC), and carbon fiber-reinforced plastic (CFRP) plates to create a composite beam. Tests examined the flexural behavior of eight specimens comprising four CSOCBs and four SFRC-OC composite beams.…”

Section: Introductionmentioning

confidence: 99%

Plain-Woven Areca Sheath Fiber-Reinforced Epoxy Composites: The Influence of the Fiber Fraction on Physical and Mechanical Features and Responses of the Tribo System and Machine Learning Modeling

Subramanyam,

Kotikula,

Bennehalli

et al. 2024

ACS Omega

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Recent studies focus on enhancing the mechanical features of natural fiber composites to replace synthetic fibers that are highly useful in the building, automotive, and packing industries. The novelty of the work is that the woven areca sheath fiber (ASF) with different fiber fraction epoxy composites has been fabricated and tested for its tribological responses on three-body abrasion wear testing machines along with its mechanical features. The impact of the fiber fraction on various features is examined. The study also revolves around the development and validation of a machine learning predictive model using the random forest (RF) algorithm, aimed at forecasting two critical performance parameters: the specific wear rate (SWR) and the coefficient of friction (COF). The void fraction is observed to vary between 0.261 and 3.8% as the fiber fraction is incremented. The hardness of the mat rises progressively from 40.23 to 84.26 HRB. A fair ascent in the tensile strength and its modulus is also observed. Even though a short descent in flexural strength and its modulus is seen for 0 to 12 wt % composite specimens, they incrementally raised to the finest values of 52.84 and 2860 MPa, respectively, pertinent to the 48 wt % fiber-loaded specimen. A progressive rise in the ILSS and impact strength is perceptible. The wear behavior of the specimens is reported. The worn surface morphology is studied to understand the interface of the ASF with the epoxy matrix. The RF model exhibited outstanding predictive prowess, as evidenced by high R-squared values coupled with low mean-square error and mean absolute error metrics. Rigorous statistical validation employing paired t tests confirmed the model's suitability, revealing no significant disparities between predicted and actual values for both the SWR and COF.

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“…In the realm of aluminium matrix composites, stir casting stands out as the preferred manufacturing method. Renowned for its affordability, adaptability, and scalability, stir casting remains a top choice due to its economic viability and capacity for large-scale production [10][11][12][13]. Stir casting stands as a competitive option among various casting methods in terms of pricing.…”

Section: Introductionmentioning

confidence: 99%

Tribological impact on borosilicate glass powder reinforced Al-MMC by variation of sliding velocity and the study of its machinability

Bhowmik,

Bhattacharjee,

et al. 2024

Phys. Scr.

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Now-a-days, metal matrix composites based on pure aluminium are widely used as a replacement for a wide range of applications thanks to their high strength-to-weight ratio, ductility, and thermal conductivity. Micron-sized (10-3 mm) borosilicate (mixer of Silica (SiO2) and Boron Oxide (B2O3)) glass particles were used to create a dispersion in an aluminium matrix composite (Al7075- borosilicate glass MMC) utilizing a stir casting technique while in a liquid condition. The present study examines the effect of borosilicate glass particle content (0, 3, 6, and 9 wt. %) and changes in sliding velocity (0.5, 1, 1.5, and 2 m/s) on the wear behaviour of a composite rubbing against an EN31 steel disc at a constant load of 20N and a sliding distance of 1000 m. Analysis of wear showed that the presence of borosilicate glass increased the wear rate and countered the trend shown in the coefficient of friction. In a similar vein, the sliding velocity of the counter plate rotated faster resulted in a higher coefficient of friction and a higher wear rate. A reliable mathematical model is developed to find the best combination of machining parameters for Al7075-borosilicate glass metal matrix composite (MMC). This model will consider important performance measures like surface smoothness and material removal rate. The authors will also use a sophisticated statistical method called the Taguchi L9 orthogonal array design of experiments (DOE) to understand how different machining settings affect how easily the composite can be machined. By analyzing the results from the DOE, it can optimize the machining parameters for efficient and cost-effective processing. This work can lead to manufacturing innovation in the future.

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Experimental study of a composite beam externally bonded with a carbon fiber-reinforced plastic plate

Cited by 78 publications

References 31 publications

Regression model-based parametric analysis of drilling of multi-walled carbon nanotubes-added glass fiber composite laminates

Regression model-based parametric analysis of drilling of multi-walled carbon nanotubes-added glass fiber composite laminates

Plain-Woven Areca Sheath Fiber-Reinforced Epoxy Composites: The Influence of the Fiber Fraction on Physical and Mechanical Features and Responses of the Tribo System and Machine Learning Modeling

Tribological impact on borosilicate glass powder reinforced Al-MMC by variation of sliding velocity and the study of its machinability

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