Self-constructed strategy-based reinforcement LSTM approach for fiber-reinforced polymer non-linear degradation performance analysis

Song, Zhicen; Feng, Yunwen; Lu, Cheng; Liu, Jiaqi; Pan, Weihuang

doi:10.1016/j.compscitech.2023.110414

Composites Science and Technology

2024

DOI: 10.1016/j.compscitech.2023.110414

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Self-constructed strategy-based reinforcement LSTM approach for fiber-reinforced polymer non-linear degradation performance analysis

Zhicen Song,

Yunwen Feng,

Cheng Lu

et al.

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2024

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Cited by 3 publications

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Prediction of Ductile Damage in Composite Material Used in Type IV Hydrogen Tanks by Artificial Neural Network and Machine Learning with Finite Element Modeling Approach

Kadri,

Kallel,

Guerard

et al. 2024

Energy Tech

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This study investigates the degradation process of composite materials used in high‐pressure hydrogen storage vessels by employing advanced computational techniques. A recurrent neural network, specifically a bidirectional long short‐term memory (Bi‐LSTM) network, is utilized to predict the temporal evolution of ductile damage. The key degradation features are extracted from finite element modeling (FEM) computations using group method of data handling algorithms and treated as time‐series data. Results demonstrate that the Bi‐LSTM network can accurately undergo both elastic and plastic behaviors of the composite under tensile strength. Additionally, traditional machine learning (ML) algorithms such as extreme gradient boosting and random forest are employed to forecast strain degradation, showing promising results. This hybrid approach combining FEM, ML, and deep learning provides a comprehensive method for predicting the degradation of composite materials, offering significant potential for optimizing the design and durability of hydrogen storage vessels.

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Prediction of Ductile Damage in Composite Material Used in Type IV Hydrogen Tanks by Artificial Neural Network and Machine Learning with Finite Element Modeling Approach

Kadri,

Kallel,

Guerard

et al. 2024

Energy Tech

View full text Add to dashboard Cite

show abstract

Global sensitivity analysis for degraded braided composite with interval process

Sun,

Liang,

Ning

et al. 2024

Composites Science and Technology

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Applications of Long Short-Term Memory (LSTM) Networks in Polymeric Sciences: A Review

Malashin,

Tynchenko,

Gantimurov

et al. 2024

Polymers

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This review explores the application of Long Short-Term Memory (LSTM) networks, a specialized type of recurrent neural network (RNN), in the field of polymeric sciences. LSTM networks have shown notable effectiveness in modeling sequential data and predicting time-series outcomes, which are essential for understanding complex molecular structures and dynamic processes in polymers. This review delves into the use of LSTM models for predicting polymer properties, monitoring polymerization processes, and evaluating the degradation and mechanical performance of polymers. Additionally, it addresses the challenges related to data availability and interpretability. Through various case studies and comparative analyses, the review demonstrates the effectiveness of LSTM networks in different polymer science applications. Future directions are also discussed, with an emphasis on real-time applications and the need for interdisciplinary collaboration. The goal of this review is to connect advanced machine learning (ML) techniques with polymer science, thereby promoting innovation and improving predictive capabilities in the field.

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Self-constructed strategy-based reinforcement LSTM approach for fiber-reinforced polymer non-linear degradation performance analysis

Cited by 3 publications

References 36 publications

Prediction of Ductile Damage in Composite Material Used in Type IV Hydrogen Tanks by Artificial Neural Network and Machine Learning with Finite Element Modeling Approach

Prediction of Ductile Damage in Composite Material Used in Type IV Hydrogen Tanks by Artificial Neural Network and Machine Learning with Finite Element Modeling Approach

Global sensitivity analysis for degraded braided composite with interval process

Applications of Long Short-Term Memory (LSTM) Networks in Polymeric Sciences: A Review

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