Introduction

Thermomechanical controlled processing (TMCP) is used to maximise the properties of steel, alongside alloy and downstream process design. Optimisation of the TMCP schedule requires knowledge of microstructure development, with accurate metallurgical rules used in combination with FE, or fast mathematical, models. The predictive capability can be used to support new grade development, process optimisation, process resilience to upstream variability, fast mill models, data generation to complement mill data (for ‘scarce data’ regions at the extremes of mill operation) and for data analytics modelling providing a route to exploitation of the metallurgical science generated. Changes in steel making practices, as part of the drive to low CO2 processing, include greater scrap use in BF-BOF or increased EAF capacity. This gives rise to higher residual element contents in the steel compositions, which also needs to be considered for TMCP.

Research in Task 17 will focus on three key areas:

1. Development of full grain size distribution modelling during hot deformation incorporating how dislocation density distributions within the grain affects recrystallisation nucleation and growth behaviour and the strain partitioning behaviour across the grain size distribution. This microstructural model will be coupled to existing process models for temperature and global strain variations through thickness for different rolling profiles and schedules. The model will support predictions for austenite grain size develop in area 2.

2. Development of novel approaches to the control of austenite recrystallisation and strain induced ferrite formation to give a room temperature structure that contains a high dislocation density with recrystallisation nuclei embedded in the structure. This opens up the ability to reduce alloy content but retain, or even improve, the final properties.

3. Development of metallurgical rules for TMCP incorporating residual element effects on boundary motion. The work will extend state of the art knowledge on the effect of elements on nucleation and growth processes. Incorporating these effects into the full TMCP models will allow for potential changes in compositions to be more rapidly accommodated by modifications to process schedules with fewer mill trials, data to support the fast mill models, and potentially more resilient processing through adaptive rolling / cooling schedules.

Workshops with industrial partners are planned throughout the programme: 1) to explore industrial challenges that can form case studies in the latter part of the work using the knowledge gained; and 2) to demonstrate the capabilities (and limitations) of the predictive models and novel processing routes.

Progress to Date

At Sheffield, a systematic approach is being taken to investigate the role that solute, including residual elements, have on the behaviour of the austenite during hot working. The aim is to optimise mechanical properties with the minimum alloy additions. The austenite grain boundary mobility is controlled by the solute, which determines grain growth, recrystallisation, strain induced ferrite formation, and transformation temperature. Plane strain compression of model alloys has been used to determine the key deformation parameters of the non-recrystallisation temperature (Tnr), work hardening and transformation temperature as a function of composition. A surprising difference in flow stress and recrystallisation stop temperature is found with small differences in alloy additions, in particular, Cr has a particularly strong effect on Tnr. The temperature difference between Tnr and the transformation temperature is surprisingly small with the additional presence of Cu.   

At Warwick, work has focussed on two areas. The first has been a detailed study on the effect of residual elements (focussing on Cu and Sn) on grain growth, phase transformation and recrystallisation kinetics.  Model alloys containing Sn (0 - 0.2 wt%) and Cu (0 - 0.9 wt%) have been cast, hot rolled and normalised before testing. Grain growth kinetics have revealed a significant effect of Sn on grain size development, with a greater rate of retardation being seen at lower Sn additions. Initial TEM assessment has revealed significant Sn enrichment at the prior austenite grain boundaries. Using this data appropriate heat treatment has been used to generate consistent starting austenite grain sizes for the different Sn containing alloys before dilatometry for phase transformation analysis. Sn has been seen to affect ferrite and bainite transformation but has little effect on the martensite start temperature. Characterisation of the Sn samples is almost complete, and Cu samples started. When completed compositions with appropriate combined Cu and Sn levels will be generated to determine synergistic effects.  

The second area of work has examined the dislocation distribution (via KAM mapping) in austenite, using a model Fe-36Ni alloy, to determine the overall dislocation levels and grain boundary intensity for different grain sizes and strains. The information is being used to develop the recrystallisation/grain development model for TMCP and for validation of crystal plasticity modelling (carried out by Prof Hector Basoalto’s group at Sheffield University). A saturation in total dislocation density has been observed as strain increases with different local distributions based on grain size and proximity to the grain boundaries.