# Advanced optical measurement techniques for simultaneous fibre orientation and flow analysis complemented by machine learning

## TL;DR

This paper introduces a new optical method combined with machine learning to measure fiber orientation and flow in transparent fluids, helping to better understand how fibers align during fluid movement.

## Contribution

The novel contribution is a PIV-based measurement system with machine learning for quasi-simultaneous fiber orientation and flow analysis.

## Key findings

- Fibers show strong orientation effects near interfering bodies.
- Machine learning enables accurate fiber detection with minimal training data.
- Velocity vector fields and fiber motion are successfully combined and analyzed.

## Abstract

A new measuring method is presented that allows time-resolved quasi-simultaneous measurement of fibre orientation and flow velocity of a transparent fluid such as a substitute fluid for fresh concrete or polymer melt, thus enabling the flow-related fibre orientation process to be visualised and thus better understood. In order to study individual fibres and their orientation in detail a PIV-based measurement stand was built, which is capable of analysing the flow and the fibre orientation in the suspension quasi-simultaneously. To measure the fibre orientation, black light can be switched on so that the fibres are stimulated by phosphorescence and become visible in the fluid. A random forest algorithm is used to detect the fibres in the images. This machine learning method allows the fibres to be accurately detected with little training data and a short training and processing time. The results show that there is a strong orientation effect on the fibres the closer they are to the orbit in the vicinity of the interfering body. In addition, a rapidly occurring orientation can be determined in detail. This allows the velocity vector fields and the actual fibre motion to be combined and analysed in this work.