A novel, fast and clean 3D permeability measurement method for multi-layer textiles and fabrics

Permeability quantifies flow conductance of textile reinforcements, is required for process simulations, and can be used for a range of materials and process monitoring by industry. Current permeability measurement tech-niques are destructive, either because a liquid penetrates the sample or the sample must be cut from products to a specific size. A non-destructive measurement concept is introduced based on air flow established between flat circular patterns containing flow rate and pressure sensors. The presented experimental studies show that from flow rate and pressure distribution data, the technique clearly distinguishes changes in volume fraction and layup while being applicable to a range of woven and non-crimp textiles. The concept interprets permeability from a neural network trained using a large set of air flow simulations. A preliminary set of 250,000 simulated cases was applied to train seven different neural network types. Limited predictive accuracy has been achieved, utilizing a CGB neural net structure.

Automated summary

Permeability is an important parameter for textile reinforcements, and current measurement methods are destructive. This study introduces a non-destructive measurement concept based on air flow and utilizes a neural network to interpret the data. Limited predictive accuracy has been achieved.