Local Austenite grain size distribution in hot bar rolling of AISI 4135 Steel

In this paper, the local distribution of austenite grain size (AGS) was experimentally determined by conducting single round-oval and square-diamond pass hot bar rolling experiments of AISI4135 steel. The rolling experiments were carried out using the laboratory mill. The local distribution of AGS was also determined numerically. In order to predict AGS distribution, the AGS evolution model was combined with three dimensional non-isothermal finite element analyses by adopting a modified additivity rule. AGS evolution model was experimentally determined from hot torsion test according to Hodgson's model. The predicted results were in a reasonably good agreement with experimental results.

Characterization on ferrite microstructure evolution during large strain warm torsion testing of plain low carbon steel

Ferrite grain/subgrain structures evolution during the extended dynamic softening of a plain low carbon steel was investigated throughout the large strain warm deformation by hot torsion. Microstructural analysis with electron back-scattering diffraction (EBSD) scanning electron microscope (FEG/SEM) was carried out on the ferrite microstructural parameters. The results showed that the warm flow stress–strain curves are similar to those affected only by dynamic softening and an extended warm flow softening is seen during large strain deformation up to 30. Furthermore, with an increase in strain up to ~ vert, similar1 the grain size of ferrite, misorientation angle and fraction of high-angle boundaries gradually decrease and fraction of low-angle boundaries increases. With a further increase in the strain beyond ~, vert, similar2, these parameters remain approximately unchanged. No evidence of discontinuous dynamic recrystallisation involving nucleation and growth of new grains was found within ferrite. Therefore, the dynamic softening mechanism observed during large strain ferritic deformation is explained by continuous dynamic recrystallization (CDRX).

Microstructure evolution modeling of square-diamond pass hot bar rolling of AISI 4135 steel

In this study, kinetics of the static (SRX) and metadynamic recrystallization (MDRX) of AISI4135 steel was investigated using hot torsion tests. Continuous torsion tests were carried out to determine the critical strain for dynamic recrystallization (DRX). The times for 50% recrystallization of SRX and MDRX were determined, respectively, by means of interrupted torsion tests. Furthermore, austenite grain size (AGS) evolution due to recrystallization (RX) was measured by optical microscopy. With the help of the evolution model established, the AGS for hot bar rolling of AISI4135 steel was predicted numerically. The predicted AGS values were compared with the results using the other model available in the literature and experimental results to verify its validity. Then, numerical predictions depending on various process parameters such as interpass time, temperature, and roll speed were made to investigate the effect of these parameters on AGS distributions for square-diamond pass rolling. Such numerical results were found to be beneficial in understanding the effect of processing conditions on the microstructure evolution better and control the rolling processes more accurately.

Improving the quality of die castings by using artificial neural networks for porosity defect modelling

The aim of this work is to improve the quality of castings by minimizing defects and scrap through the analysis of the data generated by High Pressure Die Casting (HPDC) Machines using computational intelligence techniques. Casting is a complex process that is affected by the interdependence of die casting process parameters on each other such that changes in one parameter results in changes in other parameters. Computational intelligence techniques have the potential to model accurately this complex relationship. The project has the potential to generate optimal configurations for HPDC Machines and explain the relationships between die casting process parameters.