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Systematic Analysis of In Vitro Cell Rolling Using a Multi-well Plate Microfluidic System

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Developments towards a Multiscale Meshless Rolling Simulation System.

Umut Hanoglu^1,2, Božidar Šarler^1,2

¹Institute of Metals and Technology, Lepi Pot 11, 1000 Ljubljana, Slovenia.

Materials (Basel, Switzerland)

|August 7, 2021

Summary

This study presents a new simulation system to predict steel grain size during hot rolling. The system accurately models austenite and ferrite grain sizes, aiding in optimizing rolling mill design.

Keywords:

austenite grain size ferrite grain size hot rolling meshless methods radial basis functions recrystallization steel

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Area of Science:

Materials Science
Metallurgical Engineering
Computational Mechanics

Background:

Accurate prediction of steel grain size during hot rolling is crucial for material properties and performance.
Existing simulation methods often require complex meshing and significant computational resources.
Understanding microstructural evolution, including recrystallization, is key to controlling final grain size.

Purpose of the Study:

To develop and validate a macroscopic simulation system for predicting steel grain size during continuous hot rolling.
To integrate novel grain size models with a meshless numerical method for efficient computation.
To provide a user-friendly application for industrial optimization of rolling processes.

Main Methods:

A macroscopic simulation system employing the slice model assumption was developed.
Grain size models were coupled with macro-scale calculations using a novel radial basis function (RBF) collocation method.
A meshless approach with an efficient node generation algorithm was implemented for irregular domains.
Empirical relations were used for austenite and ferrite grain size prediction.

Main Results:

The simulation system accurately predicts austenite grain size at each rolling pass and ferrite grain size at the end of rolling.
Recrystallization was predicted to occur at each pass under typical rolling schedules.
The meshless RBF method demonstrated efficiency and accuracy for complex geometries.
The developed C# application provides predictions in under an hour on standard PCs.

Conclusions:

The developed simulation system offers a robust and efficient tool for predicting steel grain size during hot rolling.
The meshless RBF collocation method is suitable for complex, large-scale simulations in continuous rolling.
The user-friendly application facilitates the optimization of rolling mill design and process parameters.
This approach aids in achieving desired material properties through controlled microstructural evolution.