Investigation of the effect of nanoparticles on wood-bioplastic composite behavior using the acoustic emission method. (January 2023)

• Record Type: Journal Article
• Authors: Karami, Samira; Khamedi, Ramin; Azizi, Hamed; Ahmadi Najafabadi, Mehdi
• Is Part Of: Journal of composite materials. Volume 57:Number 1(2023)
• Page Start: 23
• Page End: 34
• Publication Date: 2023-01
• Subjects: Acoustic emission -- wood-bioplastic nanocomposite -- failure micro-mechanisms -- wavelet packet transform; Composite materials -- Periodicals; Composites -- Périodiques; 620.118
• DOI: 10.1177/00219983221137663
• Languages: English
• ISSNs: 0021-9983

Abstract

Acoustic emission (AE) is a non-destructive method capable of detecting various fracture micromechanisms in composite materials. Discrimination of AE signals associated with various fracture modes is of interest when utilizing this technique. Thus, this paper investigates the identification of AE signal processing wavelet packet transform (WPT) failure mechanisms in wood-bioplastic nanocomposites. To this end, nanocomposites with different compositions were prepared and tested according to the ASTM D638 standard. AE signals were collected during the experiment, and analyzed by various methods. The fast Fourier transform method was used to analyze the fracture of polymers and wood and determine the frequency range of fracture. Additionally, a time-frequency analysis of the AE signal generated by the WPT was performed. The energy distribution criterion was employed to determine the main components of the various damage micromechanisms. The results reveal distinct types and percentages of failure micromechanisms in various composites. This method examined the effect of nanoparticles on the matrix's tensile strength. The scanning electron microscopy method was used to study the morphology of composites and mechanism of failure. The phase composition of the produced materials was examined with X-ray energy dispersive spectroscopy. The results demonstrated the proposed techniques show a good performance in computational damage discrimination.