A decisional framework for second-life applications of textile waste: A data-driven and generative AI approach
Giovanni DE MARTINO, Rosa LEONE, Marica BUCCI, Michele DASSISTI
Abstract. The textile sector generates significant waste materials demonstrating potential for innovative repurposing applications. This study presents a decision-making framework utilizing physical-qualitative criteria including composition strength and weight of textile waste to identify reuse opportunities across new sectors. The methodology integrates laboratory analyses with generative artificial intelligence-supported decision models examining data patterns and proposing innovative reuse scenarios. Generative AI analyzes collected data to explore creative applications incorporating technical economic and sustainability parameters ensuring optimal resource utilization. The framework’s objective focuses on identifying novel applications while maximizing value creation and minimizing environmental impact through systematic evaluation of material properties based on quantifiable metrics. Findings indicate that textile waste can be effectively repurposed into high-value applications across multiple industries, demonstrating the necessity of standardized evaluation criteria, cross-sector collaboration and traceability mechanisms to enhance scalability and sustainability within circular economy frameworks.
Keywords
Sustainable Processes, Machine Learning, Textile Demanufacturing
Published online 9/10/2025, 9 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Giovanni DE MARTINO, Rosa LEONE, Marica BUCCI, Michele DASSISTI, A decisional framework for second-life applications of textile waste: A data-driven and generative AI approach, Materials Research Proceedings, Vol. 57, pp 475-483, 2025
DOI: https://doi.org/10.21741/9781644903735-56
The article was published as article 56 of the book Italian Manufacturing Association Conference
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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