Portable instruments based on NIR sensors and multivariate statistical methods for a semiautomatic quality control of textiles

Near-infrared (NIR) spectroscopy is a widely used technique for determining the composition of textile fibers. This paper analyzes the possibility of using low-cost portable NIR sensors based on InGaAs PIN photodiode array detectors to acquire the NIR spectra of textile samples. The NIR spectra are then processed by applying a sequential application of multivariate statistical methods (principal component analysis, canonical variate analysis, and the k-nearest neighbor classifier) to classify the textile samples based on their composition. This paper tries to solve a real problem faced by a knitwear manufacturer, which arose because different pieces of the same garment were made with “identical” acrylic yarns from two suppliers. The sweaters had a composition of 50% acrylic, 45% wool, and 5% viscose. The problem occurred after the garments were dyed, where different shades were observed due to the different origins of the acrylic yarns. This is a challenging real-world problem for two reasons. First, there is the need to differentiate between acrylic yarns of different origins, which experts say cannot be visually distinguished before garments are dyed. Second, measurements are made in the field using portable NIR sensors rather than in a controlled laboratory using sophisticated and expensive benchtop NIR spectrometers. The experimental results obtained with the portable sensors achieved a classification accuracy of 95%, slightly lower than the 100% obtained with the high-performance laboratory benchtop NIR spectrometer. The results presented in this paper show that portable NIR sensors combined with appropriate multivariate statistical classification methods can be effectively used for on-site textile quality control.This research was partially funded by Generalitat de Catalunya under grant numbers ACE033/21/000028, 2021 SGR 00392, and 2021 SGR 01501.Peer ReviewedPostprint (published version

Application of machine learning algorithm in the sheet metal industry : an exploratory case study

This study solved a practical problem in a case in the sheet metal industry using machine learning and deep learning algorithms. The problem in the case company was related to detecting the minimum gaps between components, which were produced after the punching operation of a metal sheet. Due to the narrow gaps between the components, an automated sheer machine could not grip the rest of the sheet skeleton properly after the punching operation. This resulted in some of the scraped sheet on the worktable being left behind, which needed a human operator to intervene. This caused an extra trigger to the production line that resulted in a break in production. To solve this critical problem, the relevant images of the components and the gaps between them were analyzed using machine learning and deep learning techniques. The outcome of this study contributed to eliminating the production bottleneck by optimizing the gaps between the punched components. This optimization process facilitated the easy and safe movement of the gripper machine and contributed to minimizing the sheet waste.© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.fi=vertaisarvioitu|en=peerReviewed

Structural textile pattern recognition and processing based on hypergraphs

The humanities, like many other areas of society, are currently undergoing major changes in the wake of digital transformation. However, in order to make collection of digitised material in this area easily accessible, we often still lack adequate search functionality. For instance, digital archives for textiles offer keyword search, which is fairly well understood, and arrange their content following a certain taxonomy, but search functionality at the level of thread structure is still missing. To facilitate the clustering and search, we introduce an approach for recognising similar weaving patterns based on their structures for textile archives. We first represent textile structures using hypergraphs and extract multisets of k-neighbourhoods describing weaving patterns from these graphs. Then, the resulting multisets are clustered using various distance measures and various clustering algorithms (K-Means for simplicity and hierarchical agglomerative algorithms for precision). We evaluate the different variants of our approach experimentally, showing that this can be implemented efficiently (meaning it has linear complexity), and demonstrate its quality to query and cluster datasets containing large textile samples. As, to the best of our knowledge, this is the first practical approach for explicitly modelling complex and irregular weaving patterns usable for retrieval, we aim at establishing a solid baseline

Developing of Ultrasound Experimental Methods using Machine Learning Algorithms for Application of Temperature Monitoring of Nano-Bio-Composites Extrusion

In industry fiber degradation during processing of biocomposite in the extruder is a problem that requires a reliable solution to save time and money wasted on producing damaged material. In this thesis, We try to focus on a practical solution that can monitor the change in temperature that causes fiber degradation and material damage to stop it when it occurs. Ultrasound can be used to detect the temperature change inside the material during the process of material extrusion. A monitoring approach for the extruder process has been developed using ultrasound system and the techniques of machine learning algorithms. A measurement cell was built to form a dataset of ultrasound signals at different temperatures for analysis. Machine learning algorithms were applied through machine-learning algorithm’s platform to classify the dataset based on the temperature. The dataset was classified with accuracy 97% into two categories representing over and below damage temperature (190oc) ultrasound signal. This approach could be used in industry to send an alarm or a temperature control signal when material damage is detected. Biocomposite is at the core of automotive industry material research and development concentration. Melt mixing process was used to mix biocomposite material with multi-walled carbon nanotubes (MWCNTs) for the purpose of enhancing mechanical and thermal properties of biocomposite. The resulting composite nano-bio- composite was tested via different types of thermal and mechanical tests to evaluate its performance relative to biocomposite. The developed material showed enhancement in mechanical and thermal properties that considered a high potential for applications in the future

​​Reducción de la subjetividad en el proceso de clasificación de color de teñido de lotes de producción textil mediante machine learning​

Uno de los principales retos en la producción textil es reproducir lo mejor posible la tonalidad del color en la tela, tonalidad que se obtiene a partir de una muestra dada por el cliente. El procedimiento de evaluación del color normalmente tiene mucha subjetividad debido a la apreciación visual que hace el analista humano de calidad al evaluar un lote de teñido. Cuando se rechaza un lote de teñido y la diferencia de color no es muy evidente, se producen demoras en la decisión final, esta es la problemática en la empresa EcoTextil. Se investigó flujos de trabajo incluyendo algoritmos de aprendizaje supervisado para la clasificación de las partidas de teñido según tonalidad, reduciendo la subjetividad humana en la evaluación de la tonalidad del color. Para el diseño de los flujos de trabajo de clasificación se utilizó la Metodología Fundamental para la Ciencia de Datos de IBM (Rollins, 2015). Los flujos de trabajo automatizados propuestos fueron clasificados en flujos de alto, regular y bajo rendimiento, los flujos de alto rendimiento tienen en promedio un valor F1 de 0.92 que es mayor al valor F1 del flujo de trabajo actual, evaluación humana, que es de 0.82. La utilización de los flujos de trabajo automatizados propuestos significa un ahorro de 30,000 soles al año por reducción de horas hombre, unos 60,000 soles al año por reducción de reprocesos innecesarios, 144,000 soles al año por reducción de tiempos muertos y unos 30,000 soles al año por reducción de saldos de producción.One of the main challenges in textile production is to reproduce as best as possible the color tone in the fabric, a tone that is obtained from a sample given by the client. The color evaluation procedure normally has a lot of subjectivity due to the visual appreciation that the human quality analyst makes when evaluating a dye lot. When a batch of dyeing is rejected and the color difference is not very evident, there are delays in the final decision, this is the problem at the EcoTextil company. Workflows including supervised learning algorithms for the classification of dye batches according to hue were investigated, reducing human subjectivity in the evaluation of color hue. For the design of the classification workflows, the Fundamental Methodology for Data Science was used. The proposed automated workflows were classified into high, regular and low performance flows, high performance flows have an average F1 value of 0.92, which is higher than the F1 value of the current workflow, human evaluation, which is 0.82. The use of the proposed automated workflows means a saving of 30,000 soles per year due to the reduction in man hours, about 60,000 soles per year due to the reduction of unnecessary reprocessing, 144,000 soles per year due to the reduction of downtime and about 30,000 soles per year for reduction of production balances.Tesi

Utilización del Machine Learning en la industria 4.0

Los mayores crecimientos económicos vienen impulsados por grandes innovaciones tecnológicas, como la máquina de vapor, la electricidad o el motor de combustión interna. Las empresas, por su parte, tratan de aprovechar estas revolucionarias tecnologías para crear nuevos modelos de negocio y generar altos beneficios con mínimo coste. Actualmente, nos encontramos en la cuarta revolución industrial, donde una de las tecnologías más importantes es la inteligencia artificial. En concreto, el aprendizaje automático o Machine Learning surge como un subcampo de la inteligencia artificial que da a las computadoras la habilidad de aprender sobre algo para lo que no han sido explícitamente programadas. En el presente Trabajo Fin de Máster se introducen los fundamentos del Machine Learning bajo el contexto de la Industria 4.0, se explican los diferentes tipos de problemas que es capaz de resolver y se exponen casos reales de aplicación en la industriaDepartamento de Organización de Empresas y Comercialización e Investigación de MercadosMáster en Ingeniería Industria

Modelling fashion microblogs to increase the influence of social media marketing in Ireland and China

With the breakthrough of social media in the 21st century, microblogging has become an influential medium for marketing fashion brands and products online. For this reason, this study explores ten Irish and another ten Chinese fashion microblogging influencers’ microblogs using Text Mining and Netnography. By this comparison, the study finds a current model of how fashion microblogs influence fashion consumption in Ireland and China. With the help of this model, the study proposes a typology of Irish and Chinese fashion microblogging influencers and their basic microblogging strategies. The proposed typology intends to help fashion marketers to model their fashion microblogs in order to increase the influence of social media marketing in Ireland and China. Furthermore, the proposed typology is applied to develop a digital artefact that not only can deal with Irish and Chinese fashion microblogs at the same time but also show the results employing text visualisation. This bilingual digital website tries to make up for the lack of attention to text analysis on fashion-related words in the development of text mining tools. Finally, the methodological combination of Text Mining and Netnography employs digital tools and computer programming to conduct studies in the field of arts and humanities. The success of methodological combination in the study opens up a bright prospect for interdisciplinary research methodology

Data mining and machine learning in textile industry

Data mining has been proven useful for knowledge discovery in many areas, ranging from marketing to medical and from banking to education. This study focuses on data mining and machine learning in textile industry as applying them to textile data is considered an emerging interdisciplinary research field. Thus, data mining studies, including classification and clustering techniques and machine learning algorithms, implemented in textile industry were presented and explained in detail in this study to provide an overview of how clustering and classification techniques can be applied in the textile industry to deal with different problems where traditional methods are not useful. This article clearly shows that a classification technique has higher interest than a clustering technique in the textile industry. It also shows that the most commonly applied classification methods are artificial neural networks and support vector machines, and they generally provide high accuracy rates in the textile applications. For the clustering task of data mining, a K-means algorithm was generally implemented in textile studies among the others that were investigated in this article. We conclude with some remarks on the strength of the data mining techniques for textile industry, ways to overcome certain challenges, and offer some possible further research directions. (C) 2017 Wiley Periodicals, Inc

Data mining and machine learning in textile industry

Data mining has been proven useful for knowledge discovery in many areas, ranging from marketing to medical and from banking to education. This study focuses on data mining and machine learning in textile industry as applying them to textile data is considered an emerging interdisciplinary research field. Thus, data mining studies, including classification and clustering techniques and machine learning algorithms, implemented in textile industry were presented and explained in detail in this study to provide an overview of how clustering and classification techniques can be applied in the textile industry to deal with different problems where traditional methods are not useful. This article clearly shows that a classification technique has higher interest than a clustering technique in the textile industry. It also shows that the most commonly applied classification methods are artificial neural networks and support vector machines, and they generally provide high accuracy rates in the textile applications. For the clustering task of data mining, a K-means algorithm was generally implemented in textile studies among the others that were investigated in this article. We conclude with some remarks on the strength of the data mining techniques for textile industry, ways to overcome certain challenges, and offer some possible further research directions. (C) 2017 Wiley Periodicals, Inc