Fibre Orientation Distribution Assessment of Dynamically Sheet Formed Hemp Fibre Mats by Image Analysis

Abstract

Orientation of fibre preforms is an important factor that affects the properties of short natural plant fibre composites. In this paper, oriented short hemp fibre mats were produced using dynamic sheet forming and the fibre orientation distribution in the mats was analysed using ImageJ software as well as by a simple program developed on a MATLAB software package. The OrientationJ plug-in of ImageJ gave an orientation distribution curve with a peak at a predominant direction of 0° supporting alignment during dynamic sheet forming and from MATLAB software, a mean ratio of 0.64 was obtained for the oriented mats compared to 0.74 for an aligned bundles.

Alkali treatment of hemp fibres for the production of aligned hemp fibre mats for composite reinforcement

“…Availability and ease of use make ImageJ attractive (Abràmoff, Magalhães et al 2004, Schneider, Rasband et al 2012. The principles behind this analysis tool are available in the literature (Rezakhaniha, Agianniotis et al 2012, Gesellchen, Bernassau et al 2014, Shah, Vollrath et al 2015, Püspöki, Storath et al 2016, Sunny, Pickering et al 2018. OrientationJ can provide a fibre orientation distribution profile based on the orientation of fibres within a mat analysed.…”

Investigation on the microstructural characteristics and quasi-static puncture resistance of both domesticated and wild silkworm cocoons

“…The microstructural characteristics of cocoons in the inner and outer surface layers were obtained by Image J software (National Institutes of Health, USA), which was proven to be a reliable tool to perform image processing and characterize microstructures of fiber network scaffolds and biomaterials. 25 The microstructural characteristics of these cocoons were obtained as follows: (a) scanning electron microscopy images with a size of 512 Â 512 pixels were input into the software; (b) image processing was performed to enhance the contrast, reduce the noise and preserve the structure edges of the images; (c) binary images were generated based on image segmentation using statistical region merging algorithm, which was commonly recognized as a fast and robust method to divide an image into regions of similar intensity or color; and (d) a well-established algorithm (orientation J) was adopted to report microstructural parameters of the cocoon shells, involving fiber diameter, fiber intersectional length (i.e. the distance of the fiber between two adjacent intersections), fiber intersectional density, porosity and fiber orientation (i.e.…”

“…This means that an NOI close to 0% or 100% indicates highly aligned cocoon fibers, while an NOI near 50% indicates random fiber alignment. 25 In addition, fiber area fractal dimensions of the cocoons were also calculated by box-counting method. This method is based on the analysis of microscopic images of samples, which is easy to implement, and widely used in practice.…”