A novel, fast and clean 3D permeability measurement method for multi-layer textiles and fabrics. (November 2022)

• Record Type: Journal Article
• Authors: Abdoli, Hamed; Hermann, Tino; Bickerton, Simon
• Is Part Of: Composites. Volume 162(2022)
• Publication Date: 2022-11
• Subjects: Resin Transfer Moulding (RTM) -- Permeability Measurement -- Non-Destructive Testing Method; Composite materials -- Periodicals; Manufacturing processes -- Periodicals; Composite materials; Manufacturing processes; Periodicals; 620.11805
• DOI: 10.1016/j.compositesa.2022.107167
• Languages: English
• ISSNs: 1359-835X

Abstract

Highlights: A non-destructive method for permeability measurment using artificial intelligence. Training a Neural Netwrok using results from 250 thousands of smiulations. Exploring a method to measure permabilities in all directions at the same time. Improving the efficiency of the manufacturing process of composite laminates. Providing an in-line measurment technique which is easy to conduct and interpert. Abstract: 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 techniques 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.