4.7 Article

Acoustic emission-based failure load prediction for plain woven laminates under quasi-static indentation

Related references

Note: Only part of the references are listed.
Article Mechanics

Investigation on damage evolution of open-hole plain woven composites under tensile load by acoustic emission signal analysis

Yuhang Liu et al.

Summary: Acoustic emission (AE) signal analysis was used to investigate the damage mode identification, damage initiation detection, and damage evolution analysis of open-hole plain woven composites (OHPWCs) under tensile load. The effect of the open-hole diameter on mechanical properties and failure mechanisms was assessed. The peak frequency ranges of typical failure modes were identified, and a novel concept of damage participation rate (DPR) was proposed. The results showed that matrix cracking was the major proportion of damage modes in the damage evolution process of OHPWCs under tensile load.

COMPOSITE STRUCTURES (2023)

Article Engineering, Manufacturing

A 3D objective material model for elastic-plastic damage behavior of fiber reinforced polymer composites

Junfeng Ding et al.

Summary: A new elastic-plastic damage model is proposed in this paper to simulate the plastic deformation and progressive failure process of fiber reinforced polymer composites under 3D stress conditions. The effectiveness and objectivity of the proposed material model are demonstrated by a series of test cases.

COMPOSITES PART A-APPLIED SCIENCE AND MANUFACTURING (2023)

Article Mechanics

A deep learning method for damage prognostics of fiber-reinforced composite laminates using acoustic emission

D. Xu et al.

Summary: This paper uses the acoustic emission technique to monitor the performance degradation process of composite materials, developing a prognostic model by combining feature evaluation and deep learning methods. The model effectively filters out key features and trains a neural network for degradation estimation. Results show that the prognostic model helps quantify the degradation process and damage tolerance of composite materials.

ENGINEERING FRACTURE MECHANICS (2022)

Article Mechanics

Damage monitoring of carbon fibre reinforced polymer composites using acoustic emission technique and deep learning

Claudia Barile et al.

Summary: In this research, a deep Convolutional Neural Network (CNN) was trained to classify image-based Acoustic Emission (AE) waveforms for online damage monitoring. The CNN achieved high accuracy in classifying different damage modes of Carbon Fibre Reinforced Polymer (CFRP) composites. The classified AE descriptors were able to accurately identify the occurrences of different damage modes, validating the accuracy of the CNN.

COMPOSITE STRUCTURES (2022)

Article Engineering, Manufacturing

Quasi-static indentation and acoustic emission to analyze failure and damage of bio-composites subjected to low-velocity impact

Mohamed Habibi et al.

COMPOSITES PART A-APPLIED SCIENCE AND MANUFACTURING (2022)

Article Engineering, Mechanical

Machine learning based anomaly detection and classification of acoustic emission events for wear monitoring in sliding bearing systems

F. Koenig et al.

Summary: The study aims to monitor and classify the multi-variant wear behavior of sliding bearings using acoustic emission (AE) technique and deep learning based on convolutional neural networks. It successfully achieved high accuracy and sensitivity in detecting three-body abrasion due to particle contamination.

TRIBOLOGY INTERNATIONAL (2021)

Review Chemistry, Physical

Damage mechanism identification in composites via machine learning and acoustic emission

C. Muir et al.

Summary: Identification of damage mechanisms is crucial for structural health monitoring, design, and composite systems. Recent advancements in machine learning have uncovered new pathways for understanding the relationship between waveforms and damage mechanisms in higher-dimensional spaces. This review evaluates the current state of the field, discussing the extraction of waveform features, clustering, labeling, and error analysis strategies in detail. Fundamental requirements for damage mechanism identification in machine learning frameworks are also explored, including those currently used, under development, and yet to be explored.

NPJ COMPUTATIONAL MATERIALS (2021)

Review Engineering, Multidisciplinary

Damage characterization of laminated composites using acoustic emission: A review

Milad Saeedifar et al.

COMPOSITES PART B-ENGINEERING (2020)

Article Engineering, Multidisciplinary

Quasi-static three-point bending and fatigue behavior of 3-D orthogonal woven composites

Xiaoping Gao et al.

COMPOSITES PART B-ENGINEERING (2019)

Article Engineering, Multidisciplinary

Damage characterization in composite materials using acoustic emission signal-based and parameter-based data

Claudia Barile et al.

COMPOSITES PART B-ENGINEERING (2019)

Article Engineering, Multidisciplinary

Clustering of interlaminar and intralaminar damages in laminated composites under indentation loading using Acoustic Emission

Milad Saeedifar et al.

COMPOSITES PART B-ENGINEERING (2018)

Article Materials Science, Composites

Failure load prediction for fiber-reinforced composites based on acoustic emission

Markus G. R. Sause et al.

COMPOSITES SCIENCE AND TECHNOLOGY (2018)

Article Engineering, Multidisciplinary

Characterization of indentation damage resistance of hybrid composite laminates using acoustic emission monitoring

C. Suresh Kumar et al.

COMPOSITES PART B-ENGINEERING (2017)

Article Materials Science, Composites

Prediction of delamination growth in carbon/epoxy composites using a novel acoustic emission-based approach

Reza Mohammadi et al.

JOURNAL OF REINFORCED PLASTICS AND COMPOSITES (2015)