Sumantra Mandal

Artificial neural network modeling to evaluate and predict the deformation behavior of stainless steel type AISI 304L during hot torsion

The deformation behavior of type 304L stainless steel during hot torsion is investigated using artificial neural network (ANN). Torsion tests in the temperature range of 600-1200^oC and in the (maximum surface) strain rate range of 0.1-100s^-^1 were carried out. These experiments provided the required data for training the neural network and for subsequent testing. The input parameters of the model are strain, log strain rate and temperature while torsional flow stress is the output. A three layer feed-forward network was trained with standard back propagation (BP) and Resilient propagation (Rprop) algorithm. The paper makes a robust comparison of the performances of the above two algorithms. The network trained with Rprop algorithm is found to perform better and also needs less number of iterations for convergence. The developed ANN model employing this algorithm could efficiently track the work hardening, dynamic softening and flow localization regions of the deforming material. Sensitivity analysis showed that temperature and strain rate are the most significant parameters while strain affects the flow stress only moderately. The ANN model, described in this paper, is an efficient quantitative tool to evaluate and predict the deformation behavior of type 304L stainless steel during hot torsion.

Constitutive flow behaviour of austenitic stainless steels under hot deformation: artificial neural network modelling to understand, evaluate and predict

An artificial neural network (ANN) model is developed to predict the constitutive flow behaviour of austenitic stainless steels during hot deformation. The input parameters are alloy composition and process variables whereas flow stress is the output. The model is based on a three-layer feed-forward ANN with a back-propagation learning algorithm. The neural network is trained with an in-house database obtained from hot compression tests on various grades of austenitic stainless steels. The performance of the model is evaluated using a wide variety of statistical indices. Good agreement between experimental and predicted data is obtained. The correlation between individual alloying elements and high temperature flow behaviour is investigated by employing the ANN model. The results are found to be consistent with the physical phenomena. The model can be used as a guideline for new alloy development.

Evolution and characterization of dynamically recrystallized microstructure in a titanium-modified austenitic stainless steel using ultrasonic and EBSD techniques

A C-scan ultrasonic imaging system was used to investigate the microstructural evolution during dynamic recrystallization (DRX) of a 15Cr–15Ni–2.2Mo–Ti modified austenitic stainless steel (alloy D9). Four specimens were forged at 1273 K to different strains in the range 0.1–0.5. Specimens with true strains of 0.2 or lower did not show any variation in the amplitude of the first back-wall echo. However, a visible variation in the C-scan image was observed at and above the 0.3 strain level. This variation was attributed to the evolution of fine grains. The formation of fine grains was related to DRX, as indicated by electron backscattered diffraction. This study also revealed the characteristics of the DRX or ‘necklace grains’, as opposed to the so-called parent grains or rest of the microstructure.

Capability of a Feed-Forward Artificial Neural Network to Predict the Constitutive Flow Behavior of As Cast 304 Stainless Steel Under Hot Deformation

A model is developed to predict the constitutive flow behavior of as cast 304 stainless steel during hot deformation using artificial neural network (ANN). The inputs of the neural network are strain, strain rate, and temperature, whereas flow stress is the output. Experimental data obtained from hot compression tests in the temperature range 1023-1523 K, strain range 0.1-0.5, and strain rate range 10 -3 -10 2 s -1 are employed to develop the model. A three-layer feed-forward ANN is trained with standard back propagation and some upgraded algorithms like resilient propagation (Rprop) and superSAB. The performances of these algorithms are evaluated using a wide variety of standard statistical indices. The results of this study show that Rprop algorithm performs better as compared to others and thereby considered as the most efficient algorithm for the present study. It has been shown that the developed ANN model can efficiently and accurately predict the hot deformation behavior of as cast 304 stainless steel. Finally, an attempt has been made to quantify the extrapolation ability of the developed network.

Influence of State of Stress on Dynamic Recrystallization in a Titanium-Modified Austenitic Stainless Steel

The influence of the state of stress on the microstructure and dynamic recrystallization (DRX) in a titanium-modified austenitic stainless steel is assessed by performing plane-strain and uniaxial hot compression studies. Although the state of stress does not alter the mechanisms of DRX nucleation, the kinetics of DRX is hindered during plane-strain deformation vis-à-vis uniaxial deformation.

Dynamic Recrystallization in a Ti Modified Austenitic Stainless Steel During High Strain Rate Deformation

The article discusses the dynamic recrystallization (DRX) behavior in a 15Cr-15Ni-2.2Mo-Ti modified austenitic stainless steel (commonly known as alloy D9) during high strain rate deformation. Hot compression tests were conducted in a Gleeble thermomechanical simulator in the temperatures range 1173 K–1373 K with a strain rate 10 and 100 s−1 to different strains. Hot deformed microstructures were characterized using Electron Back Scatter Diffraction (EBSD) technique. Recrystallized grains were partitioned from the deformed grains employing grain orientation spread approach. Extent of dynamic recrystallization (DRX) was found to be minimal at 1173 K. DRX was predominantly found to happen at and above 1273 K which increases with increase in strain. The fraction of Σ3 and Σ9 boundaries was found to increase with %DRX. The origin and the possible role of these boundaries on DRX in alloy D9 are also explained.

Kinetics, Mechanism and Modeling of Microstructural Evolution during Dynamic Recrystallization in a 15Cr-15Ni-2.2Mo-Ti Modified Austenitic Stainless Steel

Kinetics, mechanism and modeling of the microstructural evolution of a 15Cr-15Ni- 2.2Mo-0.3Ti modified austenitic stainless steel (alloy D9) during dynamic recrystallization (DRX) have been investigated. The kinetics of DRX has been investigated employing a modified Johnson- Mehl-Avrami-Kolmogorov (JMAK) model. The microstructural study shows that nucleation of new grains during DRX takes place on the parent grain boundary by a bulging mechanism. No significant texture component has been found to develop in the recrystallized matrix. A substantial amount of twins have been observed in the recrystallized matrix. It is proposed that twins play an important role during the nucleation and subsequent expansion of DRX in alloy D9, which in turn moderates the texture in the recrystallized matrix. An artificial neural network model has also been developed to predict the fraction of DRX and grain size, as a function of processing conditions. A good correlation between experimental findings and predicted results has been obtained.

An Overview of Neural Network Based Modeling in Alloy Design and Thermomechanical Processing of Austenitic Stainless Steels

This overview reports some of the research works carried out by us in recent past on the application of artificial neural network (ANN) based modeling in alloy design and thermomechanical processing of austenitic stainless steels. Different ANN models were created in order to simulate various correlations and phenomena in austenitic stainless steels. These include: prediction of mechanical properties of alloy D9 from its alloy content, modeling constitutive flow behavior of austenitic stainless steels, and prediction of torsional flow behavior of type 304L stainless steel. Attempt has been made to explain the simulated results by relevant fundamental metallurgical phenomena.

Origin and Role of Σ3 Boundaries during Thermo-Mechanical Processing of a Ti-Modified Austenitic Stainless Steel

The origin and role of S3 boundaries during dynamic recrystallization (DRX) and grain boundary engineering (GBE) of a Ti-modified austenitic stainless steel (alloy D9) is studied. Hot deformation tests were carried out on solution-annealed (SA) specimens to study the DRX behavior whereas a series of cold deformation and annealing were performed on SA specimens to realize GBE microstructure. A linear relationship between the area fraction of DRX and the number fraction of Σ3 boundaries was observed during hot deformation. This high fraction of Σ3 boundaries could account for the formation of coherent annealing twins by “growth accidents” during DRX. For certain combinations of cold deformation and annealing, a significant increase in S3 boundaries was observed. In contrast to hot deformation, majority of these new S3 boundaries during cold deformation and annealing were formed by geometrical interactions between the pre-existing Σ3 boundaries. The role of the S3 boundaries during DRX and on tailoring microstructure through grain boundary engineering approach is discussed.

Thermally activated deformation of a high-nitrogen grade 316LN stainless steel under compressive loading

In this paper, the deformation behaviour of a high-nitrogen grade 316LN stainless steel (with 0.14%N) has been studied over a wide range of temperatures (1123-1423K) and strain rates (0.001-10 s-1). The key deformation controlling mechanisms have been investigated using thermal activation parameters, such as activation volume and activation enthalpy. The chromium nitride precipitates, dislocation intersections, both conservative and recovery, are found to be the key deformation controlling mechanism at different temperature–strain-rate domain during hot deformation of this material.