Joining processes of <scp>CFRP‐Al</scp> sheets in automobile lightweighting technologies: A review

Han, Shanling; Guang, Xinjie; Li, Zhiyong; Li, Yong

doi:10.1002/pc.27088

Cited by 40 publications

(28 citation statements)

References 48 publications

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“…Carbon fiber reinforced polymer (CFRP) is a composite material with resin as matrix and carbon fiber bundle as reinforcement, which is widely used in aerospace, automotive and wind power blades. [1][2][3][4] Due to its excellent properties such as high strength, high-specific elastic modulus, light weight, fatigue resistance and good corrosion resistance, more and more attention has been focused on application of CFRP in recent years. It improves the energy efficiency while ensuring the strength of the structure.…”

Section: Introductionmentioning

confidence: 99%

Damage mode identification in carbon/epoxy composite via machine learning and acoustic emission

et al. 2023

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Combining acoustic emission (AE) and machine learning algorithms to understand damage and failure of carbon fiber reinforced polymer (CFRP) under bending loads is a challenging task in their practical applications. This work aims to identify different characteristics of the signal induced by damage patterns of carbon/epoxy composites using machine learning model. By carrying out wavelet packet analysis of the AE signals allowing the identification of four macroscopic damage modes. Four types of signals corresponding to matrix cracking, delamination, fiber/matrix debonding and fiber breakage are characterized by the frequency bands of the main energy distribution in the original waveform. The frequency bands are (62.5–125 kHz) or (125–187.5 kHz), (187.5–250 kHz), (250–312.5 kHz) and (312.5–375 kHz) and above, respectively. To test the actual performance of the established model, called support vector machine (SVM) classifier, several precracked and untreated specimens have been fabricated and subjected to three‐point bending test. The classification result of constructed classifier was compared with the k‐means algorithm, which is widely accepted for classifying AE signals, and the similarity of the two results is analyzed. The results indicated that the similarity of different clusters exceeded 92%, 84%, 83% and 77%, respectively. It can be seen that the SVM classifier was considered promising to provide new ideas for the health monitoring of composite structures.

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Section: Introductionmentioning

confidence: 99%

Damage mode identification in carbon/epoxy composite via machine learning and acoustic emission

et al. 2023

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“…In recent years, carbon fiber composite materials have undergone rapid development. In particular, carbon fiber reinforced polymer (CFRP) consisting of carbon fiber and resin matrix is now widely implemented in aerospace and automotive 1–6 . It offers numerous advantages, such as low thermal expansion, high fatigue resistance, high strength‐to‐weight ratio, biocompatibility, and dimensional stability.…”

Section: Introductionmentioning

confidence: 99%

Experimental probe into the femtosecond laser ablation thresholds in carbon fiber reinforced polymer and assessment of the measurement theories

Zhao

Lin

et al. 2023

Polymer Composites

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Focusing on the femtosecond laser processing of carbon fiber reinforced polymer (CFRP), the ablation threshold is of great concern to fulfill the manufacturing of specified functional surfaces and devices. The ablation thresholds and incubation coefficients of resin matrix and carbon fiber in CFRP subject to femtosecond laser irradiation are investigated from the measurement theories of ablation area diameter and ablation rate. The ablation thresholds

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“…With the increased production of automobiles, the corresponding problems of environmental pollution and collisions have been brought about. The lightweighting of automobiles and the improvement of structural crashworthiness are of great significance for energy reduction, emission reduction, and crash safety 1–3 . Carbon fiber reinforced plastics (CFRP), which meet both structural and performance requirements, are gradually replacing traditional metallic materials and are widely used in thin‐walled energy‐absorbing structures for vehicles 4–7 …”

Section: Introductionmentioning

confidence: 99%

Crashworthiness analysis and multi‐objective optimization of Al/CFRP hybrid tube with initial damage under transverse impact

Cai,

Ma,

Wang

et al. 2023

Polymer Composites

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Damage can cause uncontrollable changes during the service of a structure, leading to a reduction in mechanical properties and ultimately to structural failure. This paper presents an experimental and numerical study of the crashworthiness of thin‐walled Al/CFRP hybrid tubes with pre‐existing transverse compression damage under transverse impact loading. Experiments shows that initial damage has a more limited effect on the energy absorption capacity of hybrid tubes under impact loading, but has a greater effect on their deformation pattern and the evolution of damage. After impact, cracks on the surface of the damaged tube propagate into multiple, discontinuous cracks with a total length similar to the length of the tube. Conversely, only short cracks are generated on the intact tube. Subsequent finite element simulations demonstrate the validity and accuracy of a coupled multi‐loads model and explore the effect of different structural parameters (winding angle and number of CFRP layers, and thickness of Al tube) on the crashworthiness of the hybrid tubes. Finally, a multi‐objective snake optimizer algorithm (MOSO) was used to obtain an optimal hybrid tube structure for multiple loading conditions in order to minimize the effect of initial damage on the crashworthiness of the hybrid tube. In comparison to the simulation outcomes for the original structure, the peak crushing force (PCF) decreased by 28.46%, whereas the specific energy absorption (SEA) increased by 44.59%.Highlight The effect of pre‐existing damage on the crashworthiness and deformation pattern of hybrid tube structures is investigated by means of experimental and numerical simulations. A multi‐step finite element model was developed using the restart method to realize the crashworthiness analysis of the hybrid tube under multiple load coupling conditions. The prediction accuracy of four surrogate models for the crashworthiness of hybrid tubes is compared and analyzed. The multi‐objective snake optimizer algorithm (MOSO) is proposed to obtain the optimum hybrid tube structure for multiple loading conditions.

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Joining processes of CFRP‐Al sheets in automobile lightweighting technologies: A review

Cited by 40 publications

References 48 publications

Damage mode identification in carbon/epoxy composite via machine learning and acoustic emission

Damage mode identification in carbon/epoxy composite via machine learning and acoustic emission

Experimental probe into the femtosecond laser ablation thresholds in carbon fiber reinforced polymer and assessment of the measurement theories

Crashworthiness analysis and multi‐objective optimization of Al/CFRP hybrid tube with initial damage under transverse impact

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