Numerical investigation of austenite grain size distribution in square-diamond pass hot bar rolling

In this study, the austenite grain size (AGS) for hot bar rolling of AISI4135 steel was predicted based on two different AGS evolution models available in the literature. In order to predict the AGS more accurately, both models were integrated with a three-dimensional non-isothermal finite element program by implementing a modified additivity rule. The predicted results based on two models for the square-diamond (S-D) and round-oval (R-O) pass bar rolling processes were compared with the experimental data available in the literature. Then, numerical predictions depending on various process parameters such as interpass time, temperature, and roll speed were made to compare both models and investigate the effect of these parameters on the AGS distributions. Such numerical results were found to be beneficial to understand the effect of the microstructure evolution model on the rolling processes better and control the processes more accurately.

Prediction of failure in bending of an aluminium sheet alloy

This work is dedicated to numerical prediction of the bending of thin aluminium alloy sheets, with a focus on the material parameter identification and the prediction of rupture with or without pre-strains in tension prior to bending. The experimental database consists of i) mechanical tests at room temperature, such as tension and simple shear, performed at several orientations to the rolling direction and biaxial tension ii) air bending tests of rectangular samples after (or not) pre-straining in tension. The mechanical model is composed of the Yld2004-18p anisotropic yield criterion (Barlat et al. [3]) associated with a mixed hardening rule. The material parameters (altogether 21) are optimized with an inverse approach, in order to minimize the gap between experimental data and model predictions. Then, the Hosford-Coulomb rupture criterion is used in an uncoupled way, and the parameters are determined from tensile tests, both uniaxial and biaxial, with data up to rupture. In a second step, numerical simulations of the bending tests are performed, either on material in its original state or after pre-straining in tension, with pre-strain magnitudes increasing from 0.19 up to 0.3. The comparisons are performed on different outputs: load evolution, strain field and prediction of the rupture. A very good correlation is obtained over all the tests, in the identification step as well as in the validation one. Moreover, the fracture criterion proves to be successful whatever the amount of pre-strain may be. A convincing representation of the mechanical behavior at room temperature for an aluminium alloy is thus obtained.

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.

Study of KJ-66 micro gas turbine compressor: steady and unsteady Reynolds-averaged Navier-Stokes approach

Numerical study on the compressor stage of a KJ-66 micro gas turbine was conducted in this paper through both steady and unsteady Reynolds-averaged Navier–Stokes. The study was conducted for the numerical prediction of micro gas turbine compressor performance at various operation conditions, with special attention given to the transient flow behaviors during compressor operation. The numerical results showed reasonable agreements with experimental data while providing predictions for the charting of compressor performance map at various operation speeds. The simulation results indicated that the increase of operation speed from 80 k r/min to 117 k r/min would leads to an increased peak total pressure ratio from 1.54 to 1.96, while decreasing the peak adiabatic efficiency from 0.73 to 0.55. This paper also provided discussion on details of transient flow field within the compressor stage as well as demonstrated the smooth flow transition through rotor–stator interactions.

Analytical model of pass-by-pass strain in rod (or bar) rolling and its applications to prediction of austenite grain size

Rod rolling is a process where the deformation state of the workpiece between the work rolls is quite different from the strip rolling process. However, in most microstructure evolution models, the simple area strains (natural logarithm of the area reduction ratio) multiplied by a constant have been used to compute pass-by-pass evolution of austenite grain size (AGS) in rod (or bar) rolling, without any verification. The strains at a given pass play a crucial role in determining the recrystallization behavior (static or dynamic). In this study, an analytical model that calculates the pass-by-pass strain and strain rate in rod rolling has been developed and verified by conducting four-pass (oval–round) bar and plate rolling experiments. Numerical simulations have then been carried out for the four-pass rolling sequence using the area strain model and the new analytical model, focusing on the effect of the method for calculating the strain on the recrystallization behavior and evolution of AGS. The AGS predicted was compared with those obtained from hot torsion tests. It is shown that the analytical model developed in this study is more appropriate in the analysis of bar (or rod) rolling. It was found that the recrystallization behavior and evolution of AGS during this process were influenced significantly by the calculation method for the deformation parameters (strain and strain rate). The pass-by-pass strain obtained from the simple area strain model is inadequate to be used as an input to the equations for recrystallization and AGS evolution under these rolling conditions.

Design of experiments and springback prediction for AHSS automotive components with complex geometry

With the drive towards implementing Advanced High Strength Steels (AHSS) in the automotive industry; stamping engineers need to quickly answer questions about forming these strong materials into elaborate shapes.
Commercially available codes have been successfully used to accurately predict formability, thickness and strains in complex parts. However, springback and twisting are still challenging subjects in numerical simulations of AHSS components. Design of Experiments (DOE) has been used in this paper to study the sensitivity of the implicit and explicit numerical results with respect to certain arrays ofuser input parameters in the forming ofan AHSS component. Numerical results were compared to experimental measurements of the parts stamped in an industrial production line. The forming predictions of the implicit and explicit codes were in good agreement with the experimental measurements for the conventional steel grade, while lower accuracies were observed for the springback predictions. The forming
predictions of the complex component with an AHSS material were also in good correlation with the respective experimental measurements. However, much lower accuracies were observed in its springback predictions. The number of integration points through the thickness and tool offset were found to be of significant importance, while coefficient of friction and Young's modulus (modeling input parameters) have no significant effect on the accuracy of the predictions for the complex geometry.

Analytic prediction of structural stress-strain relations of microstructured metal

Nanostructured and ultra-fine grained metals have higher strength but extremely limited ductility compared to coarse grained metals. However, their ductility can be greatly improved by introducing a specific range of grain sizes in the microstructures. In the paper, multiscale unit cell approach (UCA) is developed and applied to predict the averaged stress-strain relations of the multiscale microstructure metals. The unit cell models are three-phase structured at different scale lengths of 100 nm, 1 μm and 10 μm with different volume fractions and periodic boundary conditions. The contributions of multi-scale microstructures to the macroscopic structural properties of metals are also studied using a analytic approach—two-step mean-field method (TSMF), where three microstructural parameters are introduced and thus mechanical properties such as strength and ductility are presented as a function of these parameters. For verification of these proposed numerical and theoretical algorithms, the structural properties of the pure nickel with three-grain microstructures are studied and the results from FEA and the proposed theory have good agreement.

Prediction of ductile fracture in the cold forging of steel

The thesis describes the development of a model of the cold forging process that accurately predicts the initiation of ductile fracture. The effect of the deformation history of the material during multiple forming operations is considered. Both two- and three-dimensional numerical models of the forging process were combined with a ductile fracture criterion to predict the material ductility and damage distribution in the workpiece.

Design earthquake ground motion prediction for Perth metropolitan area with microtremor measurements for site characterization

Perth is the largest city in Western Australia and home to three-quarters of the state's residents. In recent decades, there have been a lot of earthquake activities just east of Perth in an area known as the South-West Seismic Zone. Previous numerical results of site response analyses based on limited available geology information for PMA indicated that Perth Basin might amplify the bedrock motion by more than 10 times at some frequencies and at some sites. Hence, more detailed studies on site characterization and amplification are necessary. The microtremor method using spatial autocorrelation (SPAC) processing is a useful tool for gaining thickness and shear wave velocity (SWV) of sediments and has been adopted in many previous studies. In this study, the response spectrum of rock site corresponding to the 475-year return period for PMA is defined according to the probabilistic seismic hazard analysis (PSHA) based on the latest ground motion attenuation model of Southwest Western Australia. Site characterization in PMA is performed using two microtremor measurements, namely SPAC technique and H/V method. The clonal selection algorithm (CSA) is introduced to perform direct inversion of SPAC curves to determine the soil profiles of representative PMA sites investigated in this study. Using the simulated bedrock motion as input, the responses of the soil sites are estimated using numerical method based on the shear-wave velocity vs. depth profiles determined from the SPAC technique. The response spectrum of the earthquake ground motion on surface of each site is derived from the numerical results of the site response analysis, and compared with the respective design spectrum defined in the Australian Earthquake Loading Code. The comparison shows that the code spectra are conservative in the short period range, but may slightly underestimate the response spectrum at some long period range.

Prediction interval-based control of nonlinear systems using neural networks

Prediction interval (PI) is a promising tool for quantifying uncertainties associated with point predictions. Despite its informativeness, the design and deployment of PI-based controller for complex systems is very rare. As a pioneering work, this paper proposes a framework for design and implementation of PI-based controller (PIC) for nonlinear systems. Neural network (NN)-based inverse model within internal model control structure is used to develop the PIC. Firstly, a PI-based model is developed to construct PIs for the system output. This model is then used as an online estimator for PIs. The PIs from this model are fed to the NN inverse model along with other traditional inputs to generate the control signal. The performance of the proposed PIC is examined for two case studies. This includes a nonlinear batch polymerization reactor and a numerical nonlinear plant. Simulation results demonstrated that the proposed PIC tracking performance is better than the traditional NN-based controller.