AI in Mechanics of Nonwoven Materials

In contrast to composites and woven fabrics, nonwoven materials have a unique web structure, which is composed of randomly oriented fibres bonded via mechanical, thermal or chemical techniques. Nonwoven materials are engineered fabrics, which could be disposable or durable, and the properties of these materials such as, strength, softness, filtration, flame retardancy, absorbency, stretch etc., could be designed according to their specific purposes (hygiene, filtering etc.). Nonwovens had a vital role during COVID’19 pandemic as they were utilized in fabrication of disposable masks, tissues, overalls, bonnets and hygiene products, since these products are mostly made of polymers-based nonwovens.

The main aim of the proposed project is to develop a novel artificial intelligence (AI) scheme for designing nonwoven materials considering required microstructure and mechanical performance. This novel scheme will analyse 3D orientation distribution function (ODF) of the nonwoven fibres and their corresponding manufacturing parameters (bond pattern, basis weight, speed, angle, stretch, temperature, etc.). The outcomes of the proposed project are vital for developing, prototyping and simulating real-life performance of the nonwoven materials (strength, filtration, absorption, wear, cost, basis weight, etc.), and will lead to design and manufacturing of innovative advanced nonwoven materials with minimum time and cost based on AI driven machine learning system.

The AI based software tool will lead to various innovations in manufacturing of nonwovens with reduced basis weight, cost, waste, buckling and local damage associated with the traditional processes. The proposed scheme will forward-predict and optimise process parameters considering their short- and long-term effects, time influence, loading conditions and fibre-to-fibre interaction. This project will answer the following question: “What should be done to design a new nonwoven or improve an existing one at the design stage before manufacturing?”, and lead to more diverse and innovative advanced materials and efficient processes with improved sustainability, efficiency and fabric performance.

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