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Open research questions in Textile materials and evaluations

49 unresolved questions extracted from the limitations and future-work sections of 1,102 Textile materials and evaluations papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • The complexity of predicting fabric performance due to the variety of knitted fabrics. The limited capacity of some machine learning models to capture structural variability in small datasets.

    Comparative Evaluation of Machine Learning Models for Predicting Air Permeability of Knitted Fabrics · 2026 · DOI

  • The textile industry is highly energy-intensive. The sector has experienced significant energy-related challenges due to volatile fuel markets and increasing electricity prices. The paper highlights the need for improving energy resilience in the textile industry.

    Impact of the Global Energy Crisis on Textile Manufacturing: A Strategic Roadmap · 2026 · DOI

  • The paper identifies a research gap in the area of energy resilience in the textile industry. The study highlights the need for practical engineering strategies to improve energy resilience. The paper also notes that prior work has not adequately addressed the issue of energy resilience in the textile industry.

    Impact of the Global Energy Crisis on Textile Manufacturing: A Strategic Roadmap · 2026 · DOI

  • Prior methods for predicting thread consumption have limitations, such as high complexity and low accuracy. There is a need for a rapid and precise method for predicting sewing thread consumption. The study identifies a gap in the literature for a suitable machine learning model for predicting thread consumption.

    Machine learning prediction model for the sewing thread consumption · 2026 · DOI

  • 1. Darcy's law assumes homogeneous, isotropic, steady-state, single-phase flow. Woven Nylon 6,6 mesh is anisotropic (warp and weft permeabilities differ) and real conditions include lint, lubricant aerosol, and humidity that alter the effective pore structure over time. 2. The intrinsic permeability k used in specimen calculations is a nominal representative value. Actual k must be measured by ASTM D737 / ISO 9237 for the specific mesh construction. Variation of k by two orders of magnitude between fresh and aged states makes all flow predictions highly sensitive to this input. 3. The model treats the mesh as a rigid, non-deforming porous medium. Poroelastic coupling between mechanical tension and aperture geometry is not considered. 4. Nano-coating property data in Table 2 are from laboratory-scale specimens. Production coating non-uniformity, agglomeration, and batch variation will broaden these ranges in practice. 5. The service-life degradation curves in Figure 8 are modelled; actual lifetimes depend on fibre type, lubricant composition, cleaning frequency, and machine speed. Mills must develop their own curves using the monitoring protocol in Table 6. 6. The Forchheimer inertial coefficient β has not been independently measured for these mesh constructions. At ΔP > 500 Pa, the Forchheimer correction should be applied but requires experimental β determination. 7. Temperature effects on air viscosity (≈ 2% change across the 25–35°C spinning room range) are not explicitly modelled but are negligible within the accuracy of experimental k measurements.

    AERODYNAMIC PERFORMANCE AND NANO-COATING EFFECTS ON NYLON 6,6 MESH APRONS IN TEXTILE RING SPINNING · 2026 · DOI

  • Such customizable behavior is important for its adoption in India's diverse textile manufacturing sector, where cost and sustainability priorities vary widely among manufacturers [14]. Yu, "Fast fashion sales forecasting with limited data and time," Decision Support Systems, vol. The social media dataset collected over six weeks, while sufficient for early-stage model development, is insufficient for LSTM to learn seasonal trends from social media signals alone.

    Textile Intelligence Stack: A Multi-Model AI Framework for Trend Prediction, Demand Forecasting and Fabric Recommendation in the Garment Manufacturing Industry · 2026 · DOI

  • The industry lacks a comprehensive understanding of AI applications - The industry needs to address the challenges of AI integration

    AI IN TEXTILE & APPAREL: TRANSFORMING AN INDUSTRY · 2026 · DOI

  • Emerging research and industry trends suggest that the next decade will see: 1. Hybrid defect-detection models blending motif-based and deep-learning techniques for universal fabric inspection 2. Neuromorphic computing & spiking neural networks for ultra-low-power wearable e-textiles 3. Federated learning to enable collaborative AI without exposing proprietary or personal data 4. Digital-twin-driven life-cycle assessment for sustainable product development 5. Generative AI for mass-customized prints & 3D virtual fashion avatars, enabling on-demand production and reducing waste 8. CONCLUSION AI is no longer optional, it is a strategic necessity for textile and apparel companies aiming for operational agility, environmental responsibility, and personalized consumer experiences. Research by Kumar et al. (2024) and Ngan et al. (2011) clearly shows that AI delivers measurable gains in efficiency, quality, and sustainability, yet realizing its full promise requires cross-disciplinary research, industry–academia collaboration, and strong ethical frameworks. From factory floors to fashion runways, AI is redefining textiles as smarter, greener, and more human-centric, promising a future where innovation and sustainability are not at odds but interwoven. REFERENCES: 1. Kumar, T. S., Muthuvelammai, S., & Jayachandran, N. (2024). AI in textiles: A review of emerging trends and applications. International Journal for Research in Applied Science & Engineering Technology (IJRASET), 12(4), 1–15. https://doi.org/10.22214/ijraset.2024.64404 2. Santhanam, R., & Khare, A. K. (2024). Systematic literature review on artificial intelligence and sustainable practices in the apparel industry. Journal of Informatics Education and Research, 46(2), 215–240. 3. Bravo, M. V. C., & Iturralde, W. M. P. (2024). Artificial intelligence in the management of textile companies: A contextual analysis. Open Journal of Business and Management, 12(3), 150–170. 4. Wu, X., & Li, L. (2023). Generative AI for knitted textile design: Bridging creativity and computational modeling. Textile Research Journal, 93(11), 2205–2218. 5. Ivanoska-Dacikj, A., & Stachewicz, U. (2022). Smart textiles and personal protective equipment in the pandemic era. Materials Today: Proceedings, 65, 431–438. 6. Spyridis, M., Papadopoulos, K., & Georgiadis, P. (2024). AI-enabled industrial fiber sorting for sustainable textile recycling. Resources, Conservation & Recycling, 196, 107–118. IJIRMPS2603233101 Website: www.ijirmps.org Email: [email protected] 3 Volume 14 Issue 3 @ May-June 2026 IJIRMPS | ISSN: 2349-7300 7. Huang, Y., Zhang, R., & Lin, J. (2024). AI-driven digital twins for smart manufacturing in textile production. Journal of Industrial Information Integration, 34, 102–112. 8. Ahmad, S., Rezaei, M., & Banerjee, P. (2020).

    AI IN TEXTILE & APPAREL: TRANSFORMING AN INDUSTRY · 2026 · DOI

  • Traditional tension control techniques ignore the impact of tension variation on wastewater production and chemical treatment. There is a need for a novel method that can maintain ideal, constant tension across different yarn counts.

    A sustainable yarn tension control technique for optimizing textile dyeing efficiency and water use · 2026 · DOI

  • The high market price of cashmere. The need for more cost-effective alternatives to cashmere. Maintaining fabric quality and user comfort while reducing production costs.

    Cashmere, silk and wool blended woven fabrics: an investigation of physical and handle properties · 2026 · DOI

  • The high market price of cashmere poses challenges for large-scale industrial use. There is a need for more cost-effective alternatives to cashmere that maintain similar tactile and performance qualities.

    Cashmere, silk and wool blended woven fabrics: an investigation of physical and handle properties · 2026 · DOI

  • The existing framework of manipulations in textiles is limited to manipulations applied to fabrics after production. The study identifies a gap in the literature regarding the examination of structural interventions during the weaving process.

    Structural Intervention and Forming in Woven Fabrics: Structural Weft and Warp Manipulation Methods · 2026 · DOI

  • The scientific foundations of sizing remain underappreciated in industrial practice. The dominant industrial paradigm treats sizing as a recipe optimisation problem.

    The Deep Science of Warp Sizing: A Multi-Physics and Multi-Scale Engineering Analysis · 2026 · DOI

  • The lack of precise micro-economic quantification of all conversion cost elements in the Indian spinning industry. The need for a rigorous, data-driven techno-economic model to calculate the exact financial burden of a single electronic yarn clearer cut.

    Techno-Economic Analysis of Yarn Clearer Cuts in the Autoconer Process: A Comprehensive Cost Evaluation Framework for the Indian Spinning Industry · 2026 · DOI

  • Further study of the benefits and limitations of autolevelling technology. Investigation of new sensor technologies and control strategies for autolevellers.

    Autolevellers in Draw Frames: Principles, Modelling, Control Strategies and Industrial Applications · 2026 · DOI

  • The need for a comprehensive investigation of autolevelling systems. The lack of understanding of the benefits and limitations of autolevelling technology.

    Autolevellers in Draw Frames: Principles, Modelling, Control Strategies and Industrial Applications · 2026 · DOI

  • The study identifies a gap in the understanding of the effects of transfer and emboss printing on the physical and structural properties of chenille fabrics. The interaction between printing processes and fabric structure is a relevant performance consideration that has not been fully explored.

    EFFECT OF TRANSFER AND EMBOSS PRINTING ON ABRASION RESISTANCE AND STRUCTURAL PROPERTIES OF CHENILLE FABRICS · 2026 · DOI

  • There is limited research on Estonian local sheep wool (Estonian Darkhead, Estonian Whitehead, Kihnu Native sheep) fibre properties and no data on the combined effect of these fibre properties on textile material properties in various stages of textile production, such as yarn, knitted material, and knitted felted material.

    Revaluation of Estonian local sheep wool – impact of different wool types on textile material properties · 2026 · DOI

  • The MARS model has limited capacity to capture structural variability in small datasets. The study only used 21 knitted fabric samples.

    Comparative Evaluation of Machine Learning Models for Predicting Air Permeability of Knitted Fabrics · 2026 · DOI

  • There is a need for clothing that satisfies both aesthetic and health, safety, hygiene, and ergonomic standards for preschool children. Current garment manufacturing may not prioritize the specific needs of preschool children.

    DESIGN AND MANUFACTURING TECHNOLOGY OF OVERALLS FOR PRESCHOOL CHILDREN · 2026 · DOI

  • The study only examined upper-arm sleeves and did not consider other types of compression garments. The study used a limited number of fabric materials and did not consider other factors that may affect compression responses.

    Nonlinear compression responses reveal limits of elongation-based design in knitted compression sleeves · 2026 · DOI

  • The validity of elongation-based pattern reduction under controlled static wearing conditions remains insufficiently verified. There is a need for an integrated design framework considering fabric anisotropy, layering structure, tissue response, and wearer perception.

    Nonlinear compression responses reveal limits of elongation-based design in knitted compression sleeves · 2026 · DOI

  • The paper does not provide a solution to the ongoing challenges with the disposal of electronic textiles. The lack of material details makes it difficult to further analyze the product and identify environmentally harmful materials.

    Reflecting on the Disposal of Electronic Textiles in the Era of Sustainability · 2026 · DOI

  • There is a lack of research on end-of-life solutions for electronic textiles. The complexity of combining electronics and textiles poses significant environmental sustainability challenges.

    Reflecting on the Disposal of Electronic Textiles in the Era of Sustainability · 2026 · DOI

  • There is a need to investigate the properties of recycled polyester yarns for sustainable fashion applications. The differences between virgin and recycled polyester yarns in terms of structural, thermal, and electrical characteristics are not well understood.

    Comparative analysis of structural, thermal, and electrical characteristics of virgin and recycled polyester yarns · 2026 · DOI

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49 open questions have been extracted from the limitations and future-work passages of 1,102 Textile materials and evaluations papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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