Near-infrared hyperspectral imaging of lamination and finishing processes in textile technology

NIR news ◽  
2017 ◽  
Vol 28 (1) ◽  
pp. 20-25 ◽  
Author(s):  
Gabriele Mirschel ◽  
Olesya Daikos ◽  
Tom Scherzer ◽  
Carsten Steckert
Keyword(s):  
Quality Control ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Chemical Imaging ◽  
Large Area ◽  
Adhesive Layers ◽  
Technical Textiles ◽  
Textile Technology ◽  
Finishing Processes

This paper highlights the potential of large-area hyperspectral imaging for process and quality control in established manufacturing and modification processes of technical textiles. In particular, it is focused on applications in lamination and impregnation of textiles. In case of lamination, NIR chemical imaging was applied to monitor the thickness of the adhesive layers inside the textile laminates and to detect lamination errors, whereas it was used to monitor the homogeneity of the functional finishes applied by impregnation. In both cases, low application weights ranging from only a few to several tens or hundreds grams per square meter have to be detected, which poses a challenge to the sensitivity of the method.

scholarly journals Chemometric Tools for NIRS and NIR Hyperspectral Imaging

2012 ◽  
Vol 69 (1) ◽  
Author(s):  
Laura M. DALE ◽  
André THEWIS ◽  
Ioan ROTAR ◽  
Juan A. FERNANDEZ PIERNA ◽  
Christelle BOUDRY ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Agricultural Sector ◽  
Spatial Information ◽  
Imaging System ◽  
Agricultural Products ◽  
Chemical Information ◽  
Special Focus ◽  
Large Area ◽  
Analytical Tools

Nowadays in agriculture, new analytical tools based on spectroscopic technologies are developed. Near Infrared Spectroscopy (NIRS) is a well known technology in the agricultural sector allowing the acquisition of chemical information from the samples with a large number of advantages, such as: easy to use tool, fast and simultaneous analysis of several components, non-polluting, noninvasive and non destructive technology, and possibility of online or field implementation. Recently, NIRS system was combined with imaging technologies creating the Near Infrared Hyperspectral Imaging system (NIR-HSI). This technology provides simultaneously spectral and spatial information from an object. The main differences between NIR-HSI and NIRS is that many spectra can be recorded simultaneously from a large area of an object with the former while with NIRS only one spectrum was recorded for analysis on a small area. In this work, both technologies are presented with special focus on the main spectrum and images analysis methods. Several qualitative and quantitative applications of NIRS and NIR-HSI in agricultural products are listed. Developments of NIRS and NIR-HSI will enhance progress in the field of agriculture by providing high quality and safe agricultural products, better plant and grain selection techniques or compound feed industry’s productivity among others.

scholarly journals Hyperspectral Imaging Using Laser Excitation for Fast Raman and Fluorescence Hyperspectral Imaging for Sorting and Quality Control Applications

2018 ◽  
Vol 4 (10) ◽  
pp. 110 ◽  
Author(s):  
Florian Gruber ◽  
Philipp Wollmann ◽  
Wulf Grählert ◽  
Stefan Kaskel
Keyword(s):  
Quality Control ◽  
Hyperspectral Imaging ◽  
Lead Zirconate Titanate ◽  
Imaging System ◽  
Measurement Data ◽  
Machine Learning Algorithms ◽  
Area Measurement ◽  
Large Area ◽  
Sorting Tasks

A hyperspectral measurement system for the fast and large area measurement of Raman and fluorescence signals was developed, characterized and tested. This laser hyperspectral imaging system (Laser-HSI) can be used for sorting tasks and for continuous quality monitoring. The system uses a 532 nm Nd:YAG laser and a standard pushbroom HSI camera. Depending on the lens selected, it is possible to cover large areas (e.g., field of view (FOV) = 386 mm) or to achieve high spatial resolutions (e.g., 0.02 mm). The developed Laser-HSI was used for four exemplary experiments: (a) the measurement and classification of a mixture of sulphur and naphthalene; (b) the measurement of carotenoid distribution in a carrot slice; (c) the classification of black polymer particles; and, (d) the localization of impurities on a lead zirconate titanate (PZT) piezoelectric actuator. It could be shown that the measurement data obtained were in good agreement with reference measurements taken with a high-resolution Raman microscope. Furthermore, the suitability of the measurements for classification using machine learning algorithms was also demonstrated. The developed Laser-HSI could be used in the future for complex quality control or sorting tasks where conventional HSI systems fail.

Process quality control of the manufacturing of Chinese Materia Medica by process analysis technology

NIR news ◽  
2019 ◽  
Vol 30 (7-8) ◽  
pp. 14-18
Author(s):  
Lijuan Ma ◽  
Yanjiang Qiao ◽  
Zhisheng Wu
Keyword(s):  
Quality Control ◽  
Near Infrared ◽  
Process Analysis ◽  
Chemical Imaging ◽  
Laser Induced Breakdown Spectroscopy ◽  
Chinese Materia Medica ◽  
Materia Medica ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Non Destructive

Process quality control is essential for the manufacturing of Chinese Materia Medica. Non-destructive monitoring is still a challenge for the quality control of Chinese Materia Medica manufacturing. As the commonly used non-destructive process analysis technology, near infrared spectroscopy, near infrared chemical imaging, and laser-induced breakdown spectroscopy have been applied to the quality control of Chinese Materia Medica manufacturing. The characteristic near infrared bands of 29 natural chemical components have been assigned. Ten spectral pre-processing methods and five variable selection methods have been optimized, respectively. Given the interrelationship among modeling parameters, a system modeling concept was put forward to establish a global model. Accuracy Profile was further proposed as the validation method of models. In addition, the homogeneity of chlorpheniramine maleate tablets from six brands was successfully visualized by near infrared-chemical imaging. As and Hg variation in An-Gong-Niu-Huang Wan have been rapidly monitored by laser-induced breakdown spectroscopy. A systematic modeling method (model establishment, evaluation, and validation) and the non-destructive technology of homogeneity visualization and toxic element detection have been developed for quality control of Chinese Materia Medica manufacturing, which is shown in Figure 1 of the article. These achievements have greatly promoted the research and application of process analysis technology in Chinese Materia Medica manufacturing, laying a solid foundation for the development of intelligent Chinese pharmaceutical industry.

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

2016 ◽  
Vol 932 ◽  
pp. 69-79 ◽  
Author(s):  
Gabriele Mirschel ◽  
Olesya Daikos ◽  
Tom Scherzer ◽  
Carsten Steckert
Keyword(s):  
Near Infrared ◽  
Chemical Imaging ◽  
Adhesive Layers ◽  
Line Analysis

scholarly journals Prediction of Coating Adhesion on Laser-Cleaned Metal Surfaces of Battery Cells Using Hyperspectral Imaging and Machine Learning

Coatings ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1388
Author(s):  
Johannes Maximilian Vater ◽  
Florian Gruber ◽  
Wulf Grählert ◽  
Sebastian Schneider ◽  
Alois Christian Knoll
Keyword(s):  
Machine Learning ◽  
Quality Control ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Support Vector ◽  
Electromagnetic Spectrum ◽  
Coating Adhesion ◽  
Cell Coating ◽  
The Cross ◽  
Battery Cells

Electric vehicles are shaping the future of the automotive industry. The traction battery is one of the most important components of electric cars. To ensure that the battery operates safely, it is essential to physically and electrically separate the cells facing each other. Coating a cell with varnish helps achieve this goal. Current studies use a destructive method on a sampling basis, the cross-cut test, to investigate the coating quality. In this paper, we present a fast, nondestructive and inline alternative based on hyperspectral imaging and artificial intelligence. Therefore, battery cells are measured with hyperspectral cameras in the visible and near-infrared (VNIR and NIR) parts of the electromagnetic spectrum before and after cleaning then coated and finally subjected to cross-cut test to estimate coating adhesion. During the cross-cut test, the cell coating is destroyed. This work aims to replace cross-cut tests with hyperspectral imaging (HSI) and machine learning to achieve continuous quality control, protect the environment, and save costs. Therefore, machine learning models (logistic regression, random forest, and support vector machines) are used to predict cross-cut test results based on hyperspectral data. We show that it is possible to predict with an accuracy of ~75% whether problems with coating adhesion will occur. Hyperspectral measurements in the near-infrared part of the spectrum yielded the best results. The results show that the method is suitable for automated quality control and process control in battery cell coating, but still needs to be improved to achieve higher accuracies.

Pork Tissue Recognition Using Visible and Near Infrared Hyperspectral Imaging

2020 ◽  
Author(s):  
Chih-Cheng Pai ◽  
Yang-Chu Chen ◽  
Keng-Hao Liu ◽  
Yuan-Hsun Tsai ◽  
Po-Chi Hu ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared
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