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Publications (10 of 31) Show all publications
Sharma, S., Shukla, S., Rawal, A., Jee, S., Ayaydin, F., Vásárhelyi, L., . . . Kadi, N. (2024). Droplet navigation on metastable hydrophobic and superhydrophobic nonwoven materials. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 683, Article ID 132993.
Open this publication in new window or tab >>Droplet navigation on metastable hydrophobic and superhydrophobic nonwoven materials
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2024 (English)In: Colloids and Surfaces A: Physicochemical and Engineering Aspects, ISSN 0927-7757, E-ISSN 1873-4359, Vol. 683, article id 132993Article in journal (Refereed) Epub ahead of print
Abstract [en]

Rendering any surface non-wettable requires it to be clean and dry after the droplet is deposited or impacted. Leveraging and quantifying the non-periodic or random topology non-wettable is intricate as the Cassie-Baxter state competes with the Wenzel or impaled state, which becomes further challenging for irregular and heterogeneous nonwoven materials. Herein, we report the fundamental insights of the impalement dynamics of droplets on metastable nonwovens and self-similar nonwoven-titanate nanostructured materials (SS-Ti-NMs) using laser scanning confocal microscopy in three dimensions. Our results represent the first example of liquid imbibition in metastable nonwovens and SS-Ti-NMs involving a complex interplay between a triumvirate of factors – the number of fibres in the defined cross-sectional area (volume), pore features, and intrinsic wetting properties of the constituent entities. Predictive models of the apparent contact angle and breakthrough pressure for nonwovens and their SS-Ti-NMs have been proposed based on micro- and nano-scale structural parameters. Enabled by X-ray microcomputed tomography analysis, a key set of three-dimensional fibre and structural parameters of nonwovens has been unveiled, which played a vital role in validating the predictive models of apparent contact angles.

National Category
Textile, Rubber and Polymeric Materials
Research subject
Textiles and Fashion (General)
Identifiers
urn:nbn:se:hb:diva-31123 (URN)10.1016/j.colsurfa.2023.132993 (DOI)2-s2.0-85181536088 (Scopus ID)
Funder
Vinnova, 2021–04740
Available from: 2024-01-05 Created: 2024-01-05 Last updated: 2024-02-01Bibliographically approved
Kumar, V., Hernández, N., Jensen, M. & Pal, R. (2023). Deep learning based system for garment visual degradation prediction for longevity. Computers in industry (Print), 144, Article ID 103779.
Open this publication in new window or tab >>Deep learning based system for garment visual degradation prediction for longevity
2023 (English)In: Computers in industry (Print), ISSN 0166-3615, E-ISSN 1872-6194, Vol. 144, article id 103779Article in journal (Refereed) Published
Abstract [en]

Prolonging garment longevity is a well-recognized key strategy to reduce the overall environmental impact in the textile and clothing sector. In this context, change or degradation in esthetic or visual appeal of a garment with usage is an important factor that largely influence its longevity. Therefore, to engineer the garments for a required lifetime or prolong longevity, there is a need for predictive systems that can forecast the trajectory of visual degradation based on material/structural parameters or use conditions that can guide the practitioners for an optimal design. This paper develops a deep learning based predictive system for washing-induced visual change or degradation of selected garment areas. The study follows a systematic experimental design to generate and capture visual degradation in garment and equivalent fabric samples through 70 cycles in a controlled environment following guideline from relevant washing standards. Further, the generated data is utilized to train conditional Generative Adversarial Network-based deep learning model that learns the degradation pattern and links it to washing cycles and other seam properties. In addition, the predicted results are compared with experimental data using Frechet Inception Distance, to ascertain that the system prediction are visually similar to the experimental data and the prediction quality improves with training process.

Keywords
Garment longevity, Predictive system, Generative Adversarial Networks (GANs), Deep learning
National Category
Computer Sciences Information Systems Textile, Rubber and Polymeric Materials
Research subject
Textiles and Fashion (General)
Identifiers
urn:nbn:se:hb:diva-28623 (URN)10.1016/j.compind.2022.103779 (DOI)000865427500005 ()2-s2.0-85137731068 (Scopus ID)
Funder
University of Borås, 2019-04938
Available from: 2022-09-18 Created: 2022-09-18 Last updated: 2024-02-01Bibliographically approved
Harper, S., Pal, R. & Kumar, V. (2023). Modelling small-series supply network configuration and capabilities through a mixed-method structural analysis: Insights from high-cost textile/apparel contexts. International Journal of Services and Operations Management, 46(2), 232-259
Open this publication in new window or tab >>Modelling small-series supply network configuration and capabilities through a mixed-method structural analysis: Insights from high-cost textile/apparel contexts
2023 (English)In: International Journal of Services and Operations Management, ISSN 1744-2370, Vol. 46, no 2, p. 232-259Article in journal (Refereed) Published
Abstract [en]

The purpose of this paper is to understand supply network configuration for small-series production within high-cost contexts, and the context-specific decision logics associated. A total interpretive structural modelling (TISM) and MICMAC mixed-methods approach is used to determine and interpret interrelationships among SNC and capability-related aspects identified from the literature. Respondents come from EU textile/apparel companies, undertaking small-series production/sourcing in the region, with different roles in the value chain. The findings led to several propositions. They highlight the foundational nature of supply chain relationships and digital data sharing; interacting product/process flexibility and specialisation considerations, with associated enablers and barriers; the challenges related to location, which is the crucial supply chain driver; and the need to balance various interrelated capability drivers, such as quality, innovation, and sustainability. These findings can support practitioners for reconfiguration, and the approach can be used to address other contexts and thus enhance generalisability.

Place, publisher, year, edition, pages
InderScience Publishers, 2023
Keywords
supply network configuration, supply chain design, small-series production, decision-making, total interpretive structural modelling, TISM, operations management, textile/apparel, European Union, EU
National Category
Economics and Business Textile, Rubber and Polymeric Materials
Research subject
Textiles and Fashion (General)
Identifiers
urn:nbn:se:hb:diva-27398 (URN)10.1504/IJSOM.2021.10042173 (DOI)
Projects
Fashion Big Data Business Model
Funder
EU, Horizon 2020, 761122
Available from: 2022-01-28 Created: 2022-01-28 Last updated: 2024-01-13
Rawal, A., Majumdar, A. & Kumar, V. (2023). Textile architecture for composite materials: back to basics. Oxford Open Materials Science, 3(1), Article ID itad017.
Open this publication in new window or tab >>Textile architecture for composite materials: back to basics
2023 (English)In: Oxford Open Materials Science, E-ISSN 2633-6979, Vol. 3, no 1, article id itad017Article in journal (Refereed) Published
Abstract [en]

In the last several decades, textile-reinforced composites have emerged as a unique class of materials offering intricate features, reduced fabrication costs, introduced multiaxial reinforcement, and enhanced damaged tolerance. Despite these benefits, textile-reinforced composites face challenges as predicting their performance often relies on heuristics and past experiences without gaining insights into the underlying structure of the textile material and its constituents. This tutorial-based mini-review aims to delve into the fundamentals of textile architecture in the context of textile-reinforced composites and provide an overview of their significant physical and structural features that influence the performance characteristics of textile-reinforced composites. 

Keywords
fiber, braid, knitted, nonwoven, woven, textile-reinforced composites
National Category
Textile, Rubber and Polymeric Materials
Research subject
Textiles and Fashion (General)
Identifiers
urn:nbn:se:hb:diva-30698 (URN)10.1093/oxfmat/itad017 (DOI)001081399100001 ()2-s2.0-85175058059 (Scopus ID)
Projects
SustDesignTex
Funder
EU, Horizon Europe
Available from: 2023-10-31 Created: 2023-10-31 Last updated: 2024-02-01Bibliographically approved
Kumar, V., Ekwall, D. & Zhang, D. S. (2022). Investigation of rental business model for collaborative consumption - workwear garment renting in business-to-business scenario. Resources, Conservation and Recycling, 182, Article ID 106314.
Open this publication in new window or tab >>Investigation of rental business model for collaborative consumption - workwear garment renting in business-to-business scenario
2022 (English)In: Resources, Conservation and Recycling, ISSN 0921-3449, E-ISSN 1879-0658, Vol. 182, article id 106314Article in journal (Refereed) Published
Keywords
Collaborative consumption, Workwear apparel renting, Discrete event simulation, Business-to-business
National Category
Economics Business Administration Textile, Rubber and Polymeric Materials
Research subject
Business and IT; Textiles and Fashion (General)
Identifiers
urn:nbn:se:hb:diva-27723 (URN)10.1016/j.resconrec.2022.106314 (DOI)000806898600001 ()2-s2.0-85127784023 (Scopus ID)
Available from: 2022-04-08 Created: 2022-04-08 Last updated: 2023-02-06Bibliographically approved
Kumar, V. (2022). Product Recalls in European Textile and Clothing Sector—A Macro Analysis of Risks and Geographical Patterns. Stats, 5(4), 1044-1061
Open this publication in new window or tab >>Product Recalls in European Textile and Clothing Sector—A Macro Analysis of Risks and Geographical Patterns
2022 (English)In: Stats, E-ISSN 2571-905X, Vol. 5, no 4, p. 1044-1061Article in journal (Refereed) Published
Abstract [en]

Textile and clothing (T&C) products contribute to a substantial proportion of the non-food product recalls in the European Union (EU) due to various levels of associated risks. Out of the listed 34 categories for product recalls in the EU’s Rapid Exchange of Information System (RAPEX), the category ’clothing, textiles, and fashion items’ was among the top 3 categories with the most recall cases during 2013–2019. Previous studies have attempted to highlight the issue of product recalls and their impacts from the perspective of a single company or selected companies, whereas limited attention is paid to understand the problem from a sector-specific perspective. However, considering the nature of product risks and the consistency in a higher number of recall cases, it is important to analyze the issue of product recalls in the T&C sector from a sector-specific perspective. In this context, the paper focuses on investigating the past recalls in the T&C sector reported RAPEX during 2005–2021 to understand the major trends in recall occurrence and associated hazards. Correspondence Analysis (CA) and Latent Dirichlet Allocation (LDA) were applied to analyze the qualitative and quantitative recall data. The results reveal that there is a geographical pattern for the product risk that leads to the recalls. The countries in eastern part of Europe tend to have proportionately high recalls in strangulation and choking-related issues, whereas chemical-related recalls are proportionately high in countries located in western part of Europe. Further, text-mining results indicate that design-related recall issues are more prevalent in children’s clothing.

Keywords
product recall; product risk; textile and clothing; correspondence analysis; Latent Dirichlet Allocation
National Category
Other Computer and Information Science Economics and Business
Research subject
Textiles and Fashion (General)
Identifiers
urn:nbn:se:hb:diva-28971 (URN)10.3390/stats5040062 (DOI)000902619000001 ()2-s2.0-85144667191 (Scopus ID)
Available from: 2022-11-25 Created: 2022-11-25 Last updated: 2023-02-07Bibliographically approved
Agrawal, T. K., Kumar, V., Pal, R., Wang, L. & Chen, Y. (2021). Blockchain-based Framework for Supply Chain Traceability: A Case Example of Textile and Clothing Industry. Computers & industrial engineering
Open this publication in new window or tab >>Blockchain-based Framework for Supply Chain Traceability: A Case Example of Textile and Clothing Industry
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2021 (English)In: Computers & industrial engineering, ISSN 0360-8352, E-ISSN 1879-0550Article in journal (Refereed) Published
Abstract [en]

Traceability has emerged as a prime requirement for a multi-tier and multi-site production. It enables visibility and caters to the consumer requirements of transparency and quality assurance. Textile and clothing industry is one such example that requires traceability implementation to address prevailing problems of information asymmetry and low visibility. Customers find it difficult to access product data that can facilitate ethical buying practices or assure product authenticity. Besides, it is challenging for stakeholders to share crucial information in an insecure environment with risk of data manipulations and fear of losing information advantage. In this context, this study investigates and proposes a blockchain-based traceability framework for traceability in multi-tier textile and clothing supply chain. It conceptualizes the interaction of supply chain partners, and related network architecture at the organizational level and smart contract and transaction validation rules at the operational level. To illustrate the application of the proposed framework, the study presents an example of organic cotton supply chain using blockchain with customized smart contract and transaction rules. It finally demonstrates the applicability of the developed blockchain by testing it under two parameters. The proposed system can build a technology-based trust among the supply chain partners, where the distributed ledger can be used to store and authenticate supply chain transactions. Further, the blockchain-based traceability system would provide a unique opportunity, flexibility, and authority to all partners to trace-back their supply network and create transparent and sustainable supply chain.

Keywords
Blockchain, Traceability, Manufacturing, Textile and Clothing, Information sharing, Supply chain
National Category
Transport Systems and Logistics
Research subject
Textiles and Fashion (General); Textiles and Fashion (General)
Identifiers
urn:nbn:se:hb:diva-24890 (URN)10.1016/j.cie.2021.107130 (DOI)000632964300030 ()2-s2.0-85100037846 (Scopus ID)
Available from: 2021-01-26 Created: 2021-01-26 Last updated: 2022-01-05Bibliographically approved
Kumar, V., Harper, S. & Pal, R. (2020). A data-driven approach to incorporate multi-level input in Interpretive Structural Modelling with a case example of small-series supply chain network configuration. In: : . Paper presented at 21st International Working Seminar on Production Economics, Innsbruck, February 24-28, 2020..
Open this publication in new window or tab >>A data-driven approach to incorporate multi-level input in Interpretive Structural Modelling with a case example of small-series supply chain network configuration
2020 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Interpretive Structural Modelling (ISM) is widely employed in production research to study the complex interaction among various factors or elements which define a complex production or supply chain problem. It transforms the poorly articulated mental model of the problem into a visible well-defined relational model using an element-relationship-matrix. Building ISM involves primarily pairwise comparison of factors in rotation i.e. each factor is compared with all remaining factors as input. In general, these relations among the compared pairs are defined in binary levels i.e. the relations are defined in terms of “yes/no”; hence, the interactions are treated equally for all levels of interaction magnitude. Consequently, the interpretation of the results does not capture the intensity of interrelation, which limits the exploitation of the relational model for concrete production/supply chain decision-making. This paper introduces a data-driven algorithm to convert a multi-level pairwise comparison into bi-level groups i.e. groups with weak and strong relations, to incorporate and account for non-binary relations. The bi-level groups are created based on a threshold point in multi-level input that simultaneously maximizes the inter-group variance whereas minimizes the intra-group variance. The application of the proposed approach is demonstrated in context to small-series textile/apparel supply network configuration, in order to show its practical significance in strategic decision-making.

Keywords
Interpretive Structural Modelling, Data-driven Threshold, Supply Chain Network Configuration
National Category
Business Administration
Identifiers
urn:nbn:se:hb:diva-22955 (URN)
Conference
21st International Working Seminar on Production Economics, Innsbruck, February 24-28, 2020.
Available from: 2020-03-04 Created: 2020-03-04 Last updated: 2020-03-04Bibliographically approved
Jain, S. & Kumar, V. (2020). Garment Categorization Using Data Mining Techniques. Symmetry (6), Article ID 984.
Open this publication in new window or tab >>Garment Categorization Using Data Mining Techniques
2020 (English)In: Symmetry, E-ISSN 2073-8994, no 6, article id 984Article in journal (Refereed) Published
Abstract [en]

The apparel industry houses a huge amount and variety of data. At every step of the supply chain, data is collected and stored by each supply chain actor. This data, when used intelligently, can help with solving a good deal of problems for the industry. In this regard, this article is devoted to the application of data mining on the industry’s product data, i.e., data related to a garment, such as fabric, trim, print, shape, and form. The purpose of this article is to use data mining and symmetry-based learning techniques on product data to create a classification model that consists of two subsystems: (1) for predicting the garment category and (2) for predicting the garment sub-category. Classification techniques, such as Decision Trees, Naïve Bayes, Random Forest, and Bayesian Forest were applied to the ‘Deep Fashion’ open-source database. The data contain three garment categories, 50 garment sub-categories, and 1000 garment attributes. The two subsystems were first trained individually and then integrated using soft classification. It was observed that the performance of the random forest classifier was comparatively better, with an accuracy of 86%, 73%, 82%, and 90%, respectively, for the garment category, and sub-categories of upper body garment, lower body garment, and whole-body garment.

Place, publisher, year, edition, pages
MDPI, 2020
Keywords
data mining, machine learning, classification, big data, decision trees, naïve bayes, bayesian forest, random forest
National Category
Computer and Information Sciences
Research subject
Textiles and Fashion (General)
Identifiers
urn:nbn:se:hb:diva-23776 (URN)10.3390/sym12060984 (DOI)000553945600001 ()2-s2.0-85089077127 (Scopus ID)
Available from: 2020-09-16 Created: 2020-09-16 Last updated: 2024-02-01Bibliographically approved
Siddharth, S., Kumar, V., Kameswara Rao, P., Sharma, S., Sebők, D., Szenti, I., . . . Kukovecz, A. (2020). Probing the three-dimensional porous and tortuous nature of absorptive glass mat (AGM) separators. Journal of Energy Storage, 2017(101003)
Open this publication in new window or tab >>Probing the three-dimensional porous and tortuous nature of absorptive glass mat (AGM) separators
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2020 (English)In: Journal of Energy Storage, ISSN 2352-152X, E-ISSN 2352-1538, Vol. 2017, no 101003Article in journal (Refereed) Published
Abstract [en]

The valve regulated lead acid (VRLA) battery is a predominant electrochemical storage system that stores energy in a cheap, reliable and recyclable manner for innumerable applications. The absorptive glass mat (AGM) separator is a key component, which is pivotal for the successful functioning of the VRLA battery. Herein, the intricate three-dimensional (3D) porous structure of AGM separators has been unveiled using X-ray micro-computed tomography (microCT) analysis. X-ray microCT has quantified a variety of fiber and structural parameters including fiber orientation, porosity, tortuosity, pore size distribution, pore interconnectivity and pore volume distribution. A predictive model of hydraulic tortuosity has been developed based upon some of these fiber and structural parameters. Moreover, the pore size distribution extracted via X-ray microCT analysis has served as a benchmark for making a comparison with the existing analytical model of the pore size distribution of AGM separators. Pore size distributions obtained via the existing analytical model and through X-ray microCT analysis are in close agreement.

Keywords
Fiber orientation, Separator, Pore size, Tortuosity, Porosity
National Category
Textile, Rubber and Polymeric Materials
Identifiers
urn:nbn:se:hb:diva-22224 (URN)10.1016/j.est.2019.101003 (DOI)000516714200084 ()2-s2.0-85076242830 (Scopus ID)
Available from: 2019-12-18 Created: 2019-12-18 Last updated: 2023-08-28Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9955-6273

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