Numerical Analisis of Thermal Stimulation Characteristics Influence on Detection of Defects in Multi-layer Composites by Infrared Thermography Method

2017 ◽  
pp. 7-20 ◽  
Author(s):  
Waldemar ŚWIDERSKI
Keyword(s):  
Composite Material ◽  
Carbon Fibre ◽  
Infrared Thermography ◽  
Computer Simulations ◽  
Thermal Stimulation ◽  
Numerical Calculations ◽  
Reflection And Transmission ◽  
Active Infrared Thermography ◽  
Layer Composite ◽  
Detection Of Defects

Some results of numerical calculations evaluating the influence of characteristic of thermal stimulation used in active infrared thermography on detection of thin defects in composites are presented in the paper. The calculations were carried out for multi-layer composite material strengthened by carbon fibre. Computer simulations were performed for typical thermal stimulations generated by heating lamps. The possibilities for detection of defects by using both reflection and transmission methods were analysed.

Investigation on the damage evolution in the impacted composite material based on active infrared thermography

2016 ◽  
Vol 83 ◽  
pp. 114-122 ◽  
Author(s):  
Yin Li ◽  
Zheng-wei Yang ◽  
Jie-tang Zhu ◽  
An-bo Ming ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Composite Material ◽  
Infrared Thermography ◽  
Damage Evolution ◽  
Active Infrared Thermography

scholarly journals Designing New Antibacterial Wound Dressings: Development of a Dual Layer Cotton Material Coated with Poly(Vinyl Alcohol)_Chitosan Nanofibers Incorporating Agrimonia eupatoria L. Extract

Molecules ◽  
2020 ◽  
Vol 26 (1) ◽  
pp. 83
Author(s):  
Cláudia Mouro ◽  
Colum P. Dunne ◽  
Isabel C. Gouveia
Keyword(s):  
Composite Material ◽  
Vinyl Alcohol ◽  
Pathogenic Bacteria ◽  
Antimicrobial Agents ◽  
Coating Layer ◽  
Wound Dressings ◽  
Poly Vinyl Alcohol ◽  
Microbial Invasion ◽  
Layer Composite ◽  
Medicinal Plant Extracts

Wounds display particular vulnerability to microbial invasion and infections by pathogenic bacteria. Therefore, to reduce the risk of wound infections, researchers have expended considerable energy on developing advanced therapeutic dressings, such as electrospun membranes containing antimicrobial agents. Among the most used antimicrobial agents, medicinal plant extracts demonstrate considerable potential for clinical use, due primarily to their efficacy allied to relatively low incidence of adverse side-effects. In this context, the present work aimed to develop a unique dual-layer composite material with enhanced antibacterial activity derived from a coating layer of Poly(vinyl alcohol) (PVA) and Chitosan (CS) containing Agrimonia eupatoria L. (AG). This novel material has properties that facilitate it being electrospun above a conventional cotton gauze bandage pre-treated with 2,2,6,6-tetramethylpiperidinyl-1-oxy free radical (TEMPO). The produced dual-layer composite material demonstrated features attractive in production of wound dressings, specifically, wettability, porosity, and swelling capacity. Moreover, antibacterial assays showed that AG-incorporated into PVA_CS’s coating layer could effectively inhibit Staphylococcus aureus (S. aureus) and Pseudomonas aeruginosa (P. aeruginosa) growth. Equally important, the cytotoxic profile of the dual-layer material in normal human dermal fibroblast (NHDF) cells demonstrated biocompatibility. In summary, these data provide initial confidence that the TEMPO-oxidized cotton/PVA_CS dressing material containing AG extract demonstrates adequate mechanical attributes for use as a wound dressing and represents a promising approach to prevention of bacterial wound contamination.

scholarly journals A Method of Defect Depth Recognition in Active Infrared Thermography Based on GRU Networks

2021 ◽  
Vol 11 (14) ◽  
pp. 6387
Author(s):  
Li Xu ◽  
Jianzhong Hu
Keyword(s):  
Infrared Thermography ◽  
Visual Information ◽  
Evaluation Method ◽  
Recognition Performance ◽  
Principal Component ◽  
Defect Depth ◽  
Model Learning ◽  
Flat Bottom ◽  
Bp Network ◽  
Active Infrared Thermography

Active infrared thermography (AIRT) is a significant defect detection and evaluation method in the field of non-destructive testing, on account of the fact that it promptly provides visual information and that the results could be used for quantitative research of defects. At present, the quantitative evaluation of defects is an urgent problem to be solved in this field. In this work, a defect depth recognition method based on gated recurrent unit (GRU) networks is proposed to solve the problem of insufficient accuracy in defect depth recognition. AIRT is applied to obtain the raw thermal sequences of the surface temperature field distribution of the defect specimen. Before training the GRU model, principal component analysis (PCA) is used to reduce the dimension and to eliminate the correlation of the raw datasets. Then, the GRU model is employed to automatically recognize the depth of the defect. The defect depth recognition performance of the proposed method is evaluated through an experiment on polymethyl methacrylate (PMMA) with flat bottom holes. The results indicate that the PCA-processed datasets outperform the raw temperature datasets in model learning when assessing defect depth characteristics. A comparison with the BP network shows that the proposed method has better performance in defect depth recognition.

Sign in / Sign up

Export Citation Format

Share Document