This paper presents a review and analysis of current non-destructive failure detection methods of composite materials and a brief outline of the build of a bamboo bicycle which has been used as a d...

https://tandfonline.com/doi/pdf/10.1080/21642583.2017.1311240

Comparative analysis of failure detection methods of composites materials’ systems

Machine learning (ML) has emerged as a useful predictive tool based on mathematical and statistical relationships for various engineering problems. The pairing of structural health monitoring (SHM) and nondestructive evaluation (NDE) methods with ML algorithms has yielded beneficial results in addressing the damage state of a material or system. Damage state descriptions addressed with ML include detecting a damage mechanism, locating a mechanism, identifying the type of mechanism, assessing the extent of the damage mechanism, and estimating the useful remaining life of a material or system. Damage evaluation research of composite materials has progressed with the increased usage of composite structural elements in the aerospace industry. NDE methods are a viable candidate for pairing with ML algorithms to improve damage state monitoring of composite materials due to the complexity associated with the structure of composites. Fiber-reinforced polymers (FRP), for example, contain at least two constituent materials a fiber and matrix material whose mechanical behavior and interactions contribute to the performance of an FRP. Unlike conventional composite analytical models that require explicit information about the constituents and microstructure of a laminate, an ML algorithm can construct damage evaluation predictions when employing exclusively past operational performance or data from an SHM or NDE method. A researcher determines the type of data selected when applying an ML model for trend analysis, anomaly detection, or prediction making. However, no one specific input feature is required for utilizing an ML model, and examples of possible data features include material properties, physical dimensions, and collected evaluation data. In the present review, applications of ML combined with the damage state evaluation of composite materials, particularly examining FRPs, are discussed to demonstrate the predictive capabilities of ML and its viability for future applications, especially in industrial environments, to minimize costs and improve damage detection rates.

The intersection of damage evaluation of fiber-reinforced composite materials with machine learning: A review

Glass fiber-reinforced polymer (GFRP) ship structures have hull plate thicknesses of 10 mm or more and are fabricated using a higher proportion of resin matrix systems than E-glass fiber reinforcements. Therefore, GFRP is classified as a highly attenuative material, and this characteristic is a major cause of large errors in ultrasonic nondestructive testing for quality inspections. In this study, considering the aforementioned design and fabrication characteristics of GFRP ship structures, hull plate prototypes with various glass fiber weight fractions, glass contents (Gc), and laminate thicknesses were fabricated. Then, a pulse-echo ultrasonic test was performed with the fabricated prototypes, and the attenuation characteristics of the GFRP hull plates were investigated by conducting statistical analyses. These results demonstrated that with a variation of 30–50% in the Gc used for GFRP structure design, the plate thickness variation had a greater impact than the Gc variation on the attenuation characteristics. The increase in Gc naturally increased the scattering of ultrasonic waves but did not significantly affect the attenuation coefficient. The effects of the inner voids on the ultrasonic waves were also investigated, and the results confirmed that the laminates in this Gc region did not significantly affect attenuation.

Ultrasonic Attenuation Characteristics of Glass-Fiber-Reinforced Polymer Hull Structure

An experimental investigation concerning the effect of AFP defects on progressive damage in CFRP coupons

Glass fiber-reinforced polymer (GFRP) ship structures are generally fabricated by hand lay-up; thus, the environmental factors and worker proficiency influence the fabrication process and presence of error in the non-destructive evaluation results. In this study, the ultrasonic testing of GFRP hull plate prototypes was conducted to investigate the statistical significance of the influences of the design parameters, e.g., the glass fiber weight fraction (Gc) and thickness variations, on the measurement error. The GFRP hull plate prototypes were fitted with E-glass fiber chopped strand mats (40 wt % content) with different thicknesses (7.72 mm, 14.63 mm, and 18.24 mm). The errors in the thickness measurements were investigated by conducting pulse-echo ultrasonic A-scan. The thickness variation resulted in increased error. Furthermore, hull plate burn-off tests were conducted to investigate the fabrication qualities. Defects such as voids did not have a significant influence on the results. The statistical analysis of the measurement errors confirmed that the thickness variations resulted in a strong ultrasonic interference between the hull plates, although the hull plates had similar specific gravity values. Therefore, the ultrasonic interference of the layer group interface should be considered to decrease the GFRP hull NDE errors with respect to an increase in the thickness and Gc.

Error Analysis of Non-Destructive Ultrasonic Testing of Glass Fiber-Reinforced Polymer Hull Plates

The general aim of this study is to evaluate the efficiency of the AE technique in monitoring the structural health of CFRP. To reliably establish the link between the AE responses with the evolution of structural damage tests were carried out initially on carbon fibre bundles alone before progressing with tests on plain carbon weave fabric composite specimens fabricated in the laboratory. In certain composite specimens artificial defects such as through-hole defect, deep scratch and notches were intentionally induced to study the effect of these defects on the mechanical properties and AE responses arising during testing. Tensile tests were carried out on carbon fibre bundle and composite specimens. Composite specimens were also tested in three-point bending. AE sensors were used to monitor the structural health condition of all specimens. Due to the complexity of the AE signals recorded from composite specimens, the AE results from the bundle specimens were initially evaluated, compared and then linked to the AE responses resulting from the composite specimen tests. The AE responses collected from tests of defect-free composite specimens were compared to those containing various types of artificially induced defects. From the results acquired it was established that the AE technique can monitor effectively the evolution of damage as load increases. Key parameter indicators based on the energy, counts and duration of the AE signals were determined to be more important than the amplitude of the hits recorded. From the results at hand it can be concluded that the AE technique can be a valuable tool in the evaluation of in-service critical structural CFRP components operating under cycling loading conditions.

Structural health condition monitoring of carbon-fibre based composite materials using acoustic emission techniques

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Structural health condition monitoring of carbon-fibre based composite materials using acoustic emission techniques