A Rapid Quantitative Analysis of Bicomponent Fibers Based on Cross-Sectional In-Situ Observation

Abstract

To accelerate the industrialization of bicomponent fibers, fiber-based flexible devices, and other technical fibers and to protect the property rights of inventors, it is necessary to develop fast, economical, and easy-to-test methods to provide some guidance for formulating relevant testing standards. A quantitative method based on cross-sectional in-situ observation and image processing was developed in this study. First, the cross-sections of the fibers were rapidly prepared by the non-embedding method. Then, transmission and reflection metallographic microscopes were used for in-situ observation and to capture the cross-section images of fibers. This in-situ observation allows for the rapid identification of the type and spatial distribution structure of the bicomponent fiber. Finally, the mass percentage content of each component was calculated rapidly by AI software according to its density, cross-section area, and total test samples of each component. By comparing the ultra-depth of field microscope, differential scanning calorimetry (DSC), and chemical dissolution method, the quantitative analysis was fast, accurate, economical, simple to operate, energy-saving, and environmentally friendly. This method will be widely used in the intelligent qualitative identification and quantitative analysis of bicomponent fibers, fiber-based flexible devices, and blended textiles.

Color matching of bicomponent (PET/PTT) filaments with high performances using genetic algorithm

“…Among existent studies, several works have focused on the characterization and the physico-chemical properties of these bicomponent filaments (PET/PTT) 8 – 11 . Indeed, recent studies investigated the effect of spinning parameters on the final structural of bicomponent filaments (PET/PTT) and the interfacial structure and binding forces between polytrimethylene terephthalate (PTT) and polyethylene terephthalate (PET) filaments using sophisticated analytical techniques 9 , 10 . Obtained results showed that during the spinning process, a distinct interface layer formed between PTT and PET, crucial for their binding.…”

A weakly supervised learning pipeline for profiled fibre inspection

“…With the advent of computer vision technology, a variety of methods have been proposed to automate profiled fibre detection [4, 5], classification [6–8] and shape factor estimation [9, 10]. Recently, many works focus on the recognition task which integrates detection and classification in a network.…”