Influence of curing processes on the development of fiber bridging during delamination in composite laminates

Hu, Ping; Pulungan, Ditho Ardiansyah; Tao, Ran; Lubineau, Gilles

doi:10.1016/j.compositesa.2021.106564

Cited by 15 publications

(6 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…0 and 45°CC mainly formed crack ports through local fracture of a certain CCF layer, resulting in interlayer delamination becoming fiber bundle debonding. Moreover, CCF bundles within the layer also acted as a bridge between the peeling interfaces during the process of debonding and splitting, thus strengthening the interactive connection between the adjacent layers 37 . The substrate failure even was observed at 45°CC, which indicated an unbreakable bond strength and adhesion 35 .…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, CCF bundles within the layer also acted as a bridge between the peeling interfaces during the process of debonding and splitting, thus strengthening the interactive connection between the adjacent layers. 37 The substrate failure even was observed at 45 CC, which indicated an unbreakable bond strength and adhesion. 35 However, the peeling process of 90 CC mainly manifested as interlayer peeling between CCF layers, with only a few fiber bundles splitting and attaching to the surface of the other layer (see Figure 5c).…”

Section: The Interlayer Properties Of Ccfrcsmentioning

confidence: 97%

“…The formation of internal pores in specimens was related to the retention of air and the absorption of water during the preparation and storage of specimens. 37 During the printing process, due to the temperature difference between the nozzle and platform, the printed line had residual stress, resulting in a rough surface of the printed line. 45 Although the surface of the previous deposition line could be reheated during the subsequent melting deposition of the deposition line in the layer, the heating range and time were limited.…”

Section: ①);mentioning

confidence: 99%

See 2 more Smart Citations

A multiscale study of heat treatment effects on the interlayer mechanical properties of 3D printed continuous carbon fiber‐reinforced composites

Zhu,

Li,

Long

et al. 2023

Polymer Composites

View full text Add to dashboard Cite

The main goal of this study was to investigate the effect of heat treatment on the microstructural and interlayer mechanical properties of 3D printed continuous carbon fiber‐reinforced composites (CCFRCs). The influence of raster orientations on the interlayer properties of composites was systematically studied by the floating roller peel test. The multiscale morphological investigation was applied to analyze the influence of heat treatment on the interlayer mechanical properties and change of pores microstructure for composites. The results showed that heat treatment improved the interlayer mechanical properties of CCFRCs. During heat treatment, the pores merged, migrated, and deformed as the softening and collapse of the matrix. The average peeling strength after heat treatment for specimens increased by 10.17%–51.30%. Besides, the increase in matrix fluidity and the change of pore shape in CCFRCs were more significant than that of matrix specimens after heat treatment, since more thermal channels in CCFRCs.Highlights The interlayer mechanical properties of 3D printed continuous carbon fiber‐reinforced composites (CCFRCs) were improved by heat treatment. The pores of composites merged, migrated, and deformed as the softening and collapse of the matrix. The addition of continuous carbon fiber (CCF) made the composites have more thermal channels than those with matrix specimens.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: The Interlayer Properties Of Ccfrcsmentioning

confidence: 97%

Section: ①);mentioning

confidence: 99%

See 1 more Smart Citation

A multiscale study of heat treatment effects on the interlayer mechanical properties of 3D printed continuous carbon fiber‐reinforced composites

Zhu,

Li,

Long

et al. 2023

Polymer Composites

View full text Add to dashboard Cite

show abstract

“…Zhang Y et al used the XGBoost model to screen out key features to predict the concentration of PM2.5 pollutants in Beijing in the next 24 h 19 . In addition, natural gas loads are susceptible to the influence of pipeline materials, and different materials may cause certain fluctuations in natural gas loads when external influences vary 20,21 . Bajcar et al studied the effects of traffic‐induced vibrations on natural gas pipelines.…”

Section: Research Statusmentioning

confidence: 99%

“…18 Zhang Y et al used the XGBoost model to screen out key features to predict the concentration of PM2.5 pollutants in Beijing in the next 24 h. 19 In addition, natural gas loads are susceptible to the influence of pipeline materials, and different materials may cause certain fluctuations in natural gas loads when external influences vary. 20,21 Bajcar et al studied the effects of traffic-induced vibrations on natural gas pipelines. Dynamic stresses from traffic vibrations affect the loading of natural gas pipeline materials and may have some impact on natural gas loads and pose some safety threat to the surrounding environment.…”

Section: Research Statusmentioning

confidence: 99%

A hybrid deep learning approach by integrating extreme gradient boosting‐long short‐term memory with generalized autoregressive conditional heteroscedasticity family models for natural gas load volatility prediction

Zeng

Shao

Bian

et al. 2022

Energy Science & Engineering

View full text Add to dashboard Cite

Natural gas load forecasting provides decision-making support for natural gas dispatch and management, pipeline network construction, pricing, and sustainable energy development. To explain the uncertainty and volatility in natural gas load forecasting, this study predicts the natural gas load volatility. As the natural gas load volatility has the time-series features, along with long-term memory, volatility aggregation, asymmetry, and nonnormality, this study proposes a natural gas load volatility prediction model by combining generalized autoregressive conditional heteroscedasticity (GARCH) family models, XGBoost algorithm, and long short-term memory (LSTM) network. The model first takes the GARCH family models parameters of sliding estimation and meteorological factors as the influencing factors of volatility, and then it screens these influencing factors through the extreme gradient boosting (XGBoost) algorithm. Finally, the selected important features are input into the LSTM network to predict the volatility, and the 90% confidence interval of the volatility is calculated. Compared with a variety of single and combined models, the model proposed in this study has an average reduction of 45.404% in the evaluation index of mean squared error. The experimental results show that the model proposed in this study has a good performance and accuracy in predicting the volatility of natural gas load.

show abstract

Harnessing Extrinsic Dissipation to Enhance the Toughness of Composites and Composite Joints: A State‐of‐the‐Art Review of Recent Advances

Lubineau,

Alfano,

Tao

et al. 2024

Advanced Materials

View full text Add to dashboard Cite

Interfaces play a critical role in modern structures, where integrating multiple materials and components is essential to achieve specific functions. Enhancing the mechanical performance of these interfaces, particularly their resistance to delamination, is essential to enable extremely lightweight designs and improve energy efficiency. Improving toughness (or increasing energy dissipation during delamination) has traditionally involved modifying materials to navigate the well‐known strength‐toughness trade‐off. However, a more effective strategy involves promoting non‐local or extrinsic energy dissipation. This approach encompasses complex degradation phenomena that extend beyond the crack tip, such as long‐range bridging, crack fragmentation, and ligament formation. This work explores this innovative strategy within the arena of laminated structures, with a particular focus on fiber‐reinforced polymers. This review highlights the substantial potential for improvement by presenting various strategies, from basic principles to proof‐of‐concept applications. This approach represents a significant design direction for integrating materials and structures, especially relevant in the emerging era of additive manufacturing. However, it also comes with new challenges in predictive modeling of such mechanisms at the structural scale, and here the latest development in this direction is highlighted. Through this perspective, greater durability and performance in advanced structural applications can be achieved.

show abstract

Influence of curing processes on the development of fiber bridging during delamination in composite laminates

Cited by 15 publications

References 36 publications

A multiscale study of heat treatment effects on the interlayer mechanical properties of 3D printed continuous carbon fiber‐reinforced composites

A multiscale study of heat treatment effects on the interlayer mechanical properties of 3D printed continuous carbon fiber‐reinforced composites

A hybrid deep learning approach by integrating extreme gradient boosting‐long short‐term memory with generalized autoregressive conditional heteroscedasticity family models for natural gas load volatility prediction

Harnessing Extrinsic Dissipation to Enhance the Toughness of Composites and Composite Joints: A State‐of‐the‐Art Review of Recent Advances

Contact Info

Product

Resources

About