Year

2018

Degree Name

Doctor of Philosophy

Department

School of Mechanical, Materials, Mechatronic and Biomedical Engineering

Abstract

In hot steel rolling, abrasive wear occurs when the asperities of oxidized strip and wear debris slide over High Speed Steel (HSS) work roll and as a result wear particles will be removed from the work roll surface. In this thesis a novel Breakage Carbides Model (BCM) in the Discrete Element Method (DEM) frame work has demonstrated successfully that it is very well suited to study the abrasive wear of the HSS roll at the elevated temperature (650oC) under dry condition. This approach has touched an important issue and can make a remarkable contribution to the contact mechanics in hot rolling and to the ability to predict roll wear at elevated temperature that will significantly improve the product quality and the rolling process.

As the surfaces of rolls are covered by an oxide layer it is important that the mechanical and tribological properties of these oxides be known. A deep understanding of the mechanical properties of the oxide layer will lead to a better prediction of its behaviour during hot rolling operations. Due to the complex nature of the phenomena that occurs at the contact, and the practical difficulty of accessing the hot strip -work roll interface at elevated temperature, computer-based simulations need to be coupled with experiments to understand and predict the wear of the HSS work roll as well as the strip surface roughness.

This thesis introduces a new abrasive wear model called “Breakage Carbides Model” (BCM) which is based on a 3D Discrete Element Method (DEM). In BCM the hard carbides are realistically modelled by bonding a group of breakable spheres that are embedded among the particles of iron oxide layers. This work has overcome the challenge in determining the bond properties to reflect accurately the mechanical properties of the oxide material at the evaluated temperature. It has taken advantage of DEM that can produce naturally material removal in the prediction of abrasive wear.

This thesis is unavailable until Thursday, September 05, 2019

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