Hot Deformation Behavior of Non-Alloyed Carbon Steels

The hot deformation behavior of selected non-alloyed carbon steels was investigated by isothermal continuous uniaxial compression tests. Based on the analysis of experimentally determined flow stress curves, material constants suitable for predicting peak flow stress σp, peak strain εp and critical strain εcrDRX necessary to induce dynamic recrystallization and the corresponding critical flow stresses σcrDRX were determined. The validity of the predicted critical strains εcrDRX was then experimentally verified. Fine dynamically recrystallized grains, which formed at the boundaries of the original austenitic grains, were detected in the microstructure of additionally deformed specimens from low-carbon investigated steels. Furthermore, equations describing with perfect accuracy a simple linear dependence of the critical strain εcrDRX on peak strain εp were derived for all investigated steels. The determined hot deformation activation energy Q decreased with increasing carbon content (also with increasing carbon equivalent value) in all investigated steels. A logarithmic equation described this dependency with reasonable accuracy. Individual flow stress curves of the investigated steels were mathematically described using the Cingara and McQueen model, while the predicted flow stresses showed excellent accuracy, especially in the strains ranging from 0 to εp.

Hot deformation behavior of 60NiTi shape-memory alloy fabricated by hot isostatic pressing

The 60NiTi (Ni60wt%–Ti40wt%) intermetallic is a hard-to-process material. Understanding of hot deformation behavior is crucial for the hot working of 60NiTi. This work studied hot deformation behavior and corresponding microstructure of the hot isostatic pressed 60NiTi in the temperature range of 900 °C–1050 °C and at strain rates of 0.1, 0.01, and 0.001 s-1 through a hot compression test. The flow stress and microstructure were susceptible to the hot deformation parameters. The flow stress decreased with the increase in deformation temperature and decrease in strain rate. Work hardening occurred at a small strain, then followed by softening; finally, near-dynamic equilibrium was achieved between work hardening and softening. A constitutive equation was developed to describe the effects of strain rate and temperature on flow stress. Simulation of hot deformation via the finite element method revealed the workpiece’s inhomogeneous deformation. The deformation occurred mainly in the center area of the cylindrical sample, resulting in high stress and strain concentrations in this region and causing the equiaxial grains to be compressed into prolate grains. This work can provide guidance for the hot working, such as forging and hot rolling, of 60NiTi.

Hot Deformation Behavior of PM Ni with Harmonic Microstructure

The hot deformation behavior of a harmonic-structured pure nickel has been studied and compared with the hot deformability of a homogeneously structured nickel. Both materials were produced via the powder metallurgy route through the Spark Plasma Sintering (SPS) of mechanical milled and un-milled powders. Hot deformation was evaluated through compression tests at three different temperatures (400 °C, 800 °C, and 1300 °C), covering a wide range in the homologous temperature spectrum for Ni (from 0.39 to 0.91), and at three different strain rates (0.001, 0.01, and 0.1 s−1). The evaluation of the stress–strain curves showed a higher hot compression resistance for the harmonic-structured nickel, together with higher strain hardening and strain rate sensitivity, thanks to the peculiar microstructural features of this material. Through the metallographic analysis of the specimens after hot compression, different mechanisms were identified as responsible for the deformation behavior in relation to the temperature of testing. While at 400 °C dynamic recrystallization has slightly started, at 800 °C it is widely diffused, and at 1300 °C it is replaced by grain growth and diffusion creep phenomena.

Predicting flow stress of Ni steel based on machine learning algorithm

This article builds a stress–strain prediction model based on production data from the steel industry by using machine learning algorithms. Based on the stress–strain data of 9Ni steel hot deformation behavior, the prediction model of flow stress constitutive equation of 9Ni steel is established. Four models, including Arrhenius-type model considering strain compensation, Arrhenius-type model of Stochastic Configuration Networks (SCNs) neural network, Arrhenius-type model of Multi-objective Particle Swarm Optimization (AMPSO) and Support Vector Machine (SVM) model, are adopted in this research. The results show that the Arrhenius-type model considering strain compensation can predict the stress trend under different deformation conditions, but its prediction accuracy has some deviation. The prediction model based on SVM algorithm has the best prediction accuracy. The square of Correlation Coefficient (R2), the Average Absolute Relative Error (AARE), and Mean Square Error (MSE) are 0.99996, 0.002455, and 0.1998, respectively. Based on the data of 9Ni steel hot deformation behavior, the prediction models of machine learning algorithm have good application prospects in steel industry.