Application of Artificial Intelligence Algorithms for Technical Textiles Design Based on Performance Requirements | ||
| Advanced Sciences and Technology Journal | ||
| Volume 2, Issue 3, December 2025, Pages 1-11 PDF (332.19 K) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/astj.2025.393157.1070 | ||
| Authors | ||
| Osama Elshazly Habib1; Hossam El-deen El-Sayed Mohamed2; Kholoud Yassin Sabbah3; Haidy Ebrahim El-Far* 3 | ||
| 1Mechatronics Department, Faculty of Engineering, Menoufia University | ||
| 2Damietta University, Weaving, Spinning and Knitting Department | ||
| 3High Institute of Engineering and Technology – El-Mahala El-Kobra, Egypt - Textile Engineering Department | ||
| Abstract | ||
| This paper presents a systematic approach to optimize technical textile selection using fuzzy logic as one of the AI techniques. Traditional methods for material design rely heavily on iterative prototyping, which is time-consuming and lacks adaptability to complex performance requirements. To address this, we propose a fuzzy inference system (FIS) that maps four critical performance parameters—tensile strength, elastic recovery, thermal conductivity, and moisture regain—to a suitability score for a predefined set of textiles. The system leverages MATLAB's Fuzzy Logic Toolbox to model linguistic variables (e.g., "high," "low") and employs a Mamdani FIS with tunable membership functions and rule bases. Case studies demonstrate the system's ability to recommend materials with high accuracy compared to expert evaluations, thereby significantly reducing design cycles. This framework is particularly valuable for industries requiring rapid, data-driven decisions, such as aerospace, healthcare, and sportswear. This framework is particularly valuable for industries requiring rapid, data-driven decisions, such as aerospace, healthcare, and sportswear. | ||
| Keywords | ||
| Artificial Intelligence; Technical Textile; Fuzzy Logic Inference | ||
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