Tensile strength prediction of orthogonal CFRP under high intensity CW laser irradiation

Wang, Jiawei; Zhu, Yongxiang; Wei, Chenghua; Lv, Yuwei; Ma, Zhiliang; Wang, Lijun; Gao, Feng

doi:10.1063/1.5111548

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…In the process of laser irradiation, the pressure inside the corner edge increases significantly, and the carbon fibers at the corner edge can change into liquid, thus forming a molten section. Wei et al [ 66 ] irradiated CFRP with a high intensity CW laser and proposed a tensile strength prediction model of orthogonal CFRP under CW laser irradiation. It was found that the prediction model can reflect the decreasing process of tensile strength under laser irradiation, especially in the case of high intensity laser irradiation and high tensile load.…”

Section: Cfrp Laser Processing Optimizationmentioning

confidence: 99%

A Review of Research Progress on Machining Carbon Fiber-Reinforced Composites with Lasers

et al. 2022

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Carbon fiber-reinforced composites are widely used in automobile, aerospace and military lightweight manufacturing due to their excellent mechanical properties such as light weight, excellent fracture resistance, corrosion resistance and wear resistance, etc. However, because of their high hardness, anisotropy and low interlayer strength characteristics, there are many problems with machine carbon fiber-reinforced composites with traditional methods. As a non-contact processing technology, laser machining technology has lots of advantages in carbon fiber-reinforced composites processing. However, there are also some defects produced in laser machining process such the heat affected zone, delamination and fiber extraction due to the great difference of physical properties between the carbon fibers and the resin matrix. To improve the quality of carbon fiber-reinforced composites laser machining, lots of works have been carried out. In this paper, the research progress of carbon fiber-reinforced composites laser machining parameters optimization and numerical simulation was summarized, the characteristics of laser cutting carbon fiber-reinforced composites and cutting quality influence factors were discussed, and the developing trend of the carbon fiber-reinforced composites laser cutting was prospected.

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Section: Cfrp Laser Processing Optimizationmentioning

confidence: 99%

A Review of Research Progress on Machining Carbon Fiber-Reinforced Composites with Lasers

et al. 2022

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“…[1,2] The theoretical mechanical and physical properties of fiber-reinforced composite materials, such as CFRP, are estimated from the fiber orientation, fiber, matrix properties, and fiber volume fractions, assuming no defect. [3,4] However, it is well-known that the presence of defects can significantly decrease the mechanical and physical properties of CFRP, and some initial defects and the appearance of voids and interlaminar delamination by age deterioration in CFRP are common. [5][6][7][8][9] Therefore, the technique to detect the presence of voids and interlaminar delamination in CFRP and to instantaneously estimate CFRP's actual mechanical and physical properties is industrially applicable.Recently, data science methods, such as machine learning, have attracted attention for their potential to accelerate the design and development process of materials.…”

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confidence: 99%

k‐Means Clustering for Prediction of Tensile Properties in Carbon Fiber‐Reinforced Polymer Composites

et al. 2022

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The application of computer algorithms to identify patterns in data is referred to as machine learning. The algorithms are used to learn complex relationships and build models for various predictions. Herein, the k‐means method is used, one of the unsupervised learning methods in machine learning, to predict Young's modulus and ultimate tensile strength (UTS) of carbon‐fiber‐reinforced polymers (CFRPs), and their experimental Young's modulus and UTS values are compared. The k‐means method categorizes CFRP into four colors: carbon fiber, epoxy resin matrix, defects, and contamination. The prediction of Young's modulus and UTS of CFRP with different porosities and carbon fiber orientation demonstrates the effectiveness of the k‐means method. Furthermore, the experimental values of Young's modulus and UTS of commercial CFRP plate are closer to the predicted values than the catalog values. These results suggest that the k‐means method can predict Young's modulus and UTS of CFRP accurately, instantly, and automatically. The k‐means method is promising as a new technique to accurately and instantly understand the mechanical and physical properties of CFRPs without any material test.

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Fracture prediction of CFRP laminates subjected to CW laser heating and pre-tensile loads based on ANN

et al. 2022

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For the application of composite materials in a complex thermal and mechanical environment, we developed a standard artificial neural network (ANN) model for the fracture prediction of carbon fiber-reinforced polymer (CFRP) laminates under continuous wave laser heating and pre-tensile loads. A substantial amount of data was collected through experimentation and from published references, which were converted into 12 800 binary-classification-type input/output data pairs before being used for model training. Different numbers of hidden neurons were evaluated to determine the optimal architecture of the model, while the “early stopping” and “dropout” methods were used to improve its robustness. The trained ANN model functions as a binary classifier that can predict the fracture probability of CFRP laminates after a certain period of laser irradiation. Subsequently, another 14 sets of experimentally collected data were used for ANN model testing. The correct prediction rate of the model reached 86%, which was higher than two other machine learning models (k-nearest neighbors and random forest models) under the same conditions. As the failure behavior of CFRP laminates has a certain degree of randomness, the fracture probabilities predicted by the ANN model have more practical values than the specific fracture times predicted by existing theories. Results indicate that it is feasible to apply the ANN method to predict the failure behavior of composite materials with discrete mechanical properties in complex thermal environments, and this study provides useful guidance for the engineering applications of composite materials in complex thermal and mechanical environments.

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Tensile strength prediction of orthogonal CFRP under high intensity CW laser irradiation

Cited by 3 publications

References 17 publications

A Review of Research Progress on Machining Carbon Fiber-Reinforced Composites with Lasers

A Review of Research Progress on Machining Carbon Fiber-Reinforced Composites with Lasers

k‐Means Clustering for Prediction of Tensile Properties in Carbon Fiber‐Reinforced Polymer Composites

Fracture prediction of CFRP laminates subjected to CW laser heating and pre-tensile loads based on ANN

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