Breathable Systems: Integrating Computational Knitting-Informed Ceramic Textiles and Auxetic Structures For Advanced Architectural Solutions

Ceramic textiles, traditionally composed of steel wire mesh encased in clay tiles, offer structural integrity, fire resistance, and energy absorption, making them ideal architectural envelopes. This study reimagines textiles by integrating computational knitting and auxetics to enhance adaptability and aesthetics. The research employs a multidisciplinary approach: hand-cutting auxetic patterns, programming digital patterns, and conducting dynamic analyses to assess structural behavior. A dataset of auxetic patterns was formed using visual programming, and a Convolutional Neural Network (CNN) model was trained to rank patterns based on architectural effectiveness. Deep learning identified the optimal auxetic pattern, influencing tile design. Parametric weaving produced knitting-informed textures, followed by 3D simulation and printing to test the ceramic textile assembly. Results show effectiveness in pattern evaluation and improved structural integrity, aesthetics, and energy efficiency in the developed textiles. This paper showcases the prospective of merging auxetics and computational knitting with ceramics, fostering interdisciplinary design solutions and sustainability.