Identification of fiber characteristics of a filter media based on artificial intelligence (AI) with GeoDict

A. Grießer, R. Westerteiger, A. Wiegmann*, M. Azimian, Math2Market GmbH, Germany

The study of the micro-structure of the filter media is the starting point to understand, analyze and optimize a filter. The first simulation step on the media scale consists of processing of µCT-scan images of a real media to prepare a detailed 3D micro-structure model of the filter media.

µCT-scans are a powerful tool to gain deep insights and ideas for innovation and for quality control in material engineering. GeoDict provides the tools for a better understanding of CT-images and paves the way to overcome the challenges of modern material engineering. Nonwovens are used in many industries, including fibrous media for filtration, glass or carbon-fiber reinforced plastics used in mechanical applications, or gas-diffusion layers used in fuel cells.

The performance of nonwovens is governed mainly by the spatial distribution, orientation, length, curvature and center line of the individual fibers. For fibrous media with binder, the volume/weight percentage and the spatial distribution of the binder material are also essential. With GeoDict, binder can be segmented from fibers in CT-scans even if they have the same gray values.

Using nonwoven micro-structures modelled with GeoDict, a neural network is trained to label binder with artificial 3-D scans, for which the distribution of the binder is known. After the training, the neural network also recognizes the binder in 3-D scans of real nonwovens, which were scanned with µCT or FIB-SEM. The results are...

Session: F9 - Filter Media - Modelling, Artificial Intelligence, Machine Learning
Day: 24 October 2019
Time: 09:00 - 10:15 h

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