Literature DB >> 27286771

Near-infrared chemical imaging used for in-line analysis of inside adhesive layers in textile laminates.

Gabriele Mirschel1, Olesya Daikos1, Tom Scherzer2, Carsten Steckert3.   

Abstract

This paper demonstrates for the first time that near-infrared (NIR) chemical imaging can be used for in-line analysis of textile lamination processes. In particular, it was applied for the quantitative determination of the applied coating weight and for monitoring of the spatial distribution of hot melt adhesive layers using chemometric approaches for spectra evaluation. Layers with coating weights between about 25 and 130 g m(-2) were used for the lamination of polyester fabrics and nonwovens as well as for polyurethane foam. It was shown that quantitative data with adequate precision can be actually obtained for layers applied to materials with significantly heterogeneous surface structure such as foam or for hidden layers inside fabric laminates. Even the coating weight and the homogeneity of adhesive layers in composites consisting of black textiles only could be quantitatively analyzed. The prediction errors (RMSEP) determined in an external validation of each calibration model were found to range from about 2 g m(-2) to 6 g m(-2) depending on the specific system under investigation. All calibration models were applied for chemical imaging in order to prove their performance for monitoring the thickness and the homogeneity of adhesive layers in the various textile systems. Moreover, they were used for the detection of irregularities and coating defects. Investigations were carried out with a large hyperspectral camera mounted above a conveyor. Therefore, this method allows large-area monitoring of the properties of laminar materials. Consequently, it is potentially suited for process and quality control during the lamination of fabrics, foams and other materials in field-scale.
Copyright © 2016 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Chemometrics; Coating weight; Homogeneity; Hyperspectral imaging; In-line monitoring; Process control

Year:  2016        PMID: 27286771     DOI: 10.1016/j.aca.2016.05.015

Source DB:  PubMed          Journal:  Anal Chim Acta        ISSN: 0003-2670            Impact factor:   6.558


  1 in total

1.  Hyperspectral near infrared imaging quantifies the heterogeneity of carbon materials.

Authors:  Mikko Mäkelä; Paul Geladi
Journal:  Sci Rep       Date:  2018-07-11       Impact factor: 4.379

  1 in total

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