Danuta Szeliga

Identification of rheological parameters on the basis of various types of plastometric tests

Abstract Axisymmetric compression and plane-strain compression tests have been performed on specimens of carbon–manganese steel containing 0.17% of carbon. Load–displacement relationships were monitored for each test and the inverse analysis method used to determine the material flow stress for constant strain rate.

Testing of the inverse software for identification of rheological models of materials subjected to plastic deformation

The general objective of the present work was to perform numerical tests for the inverse analysis of various plastometric tests. Uniaxial compression, plane strain compression and ring compression were investigated for different materials. The experimental results, in the form of load vs. displacement measurements carried out in two laboratories for various sample dimensions, were used as input for inverse calculations. As a result, a large number of data was obtained and the comparison of flow stress values determined in various tests and in various laboratories was possible. The capabilities of the inverse analysis as well as the influence of the method of testing on the material properties were examined. It is shown, in general, that when the inverse analysis is applied to the interpretation of the plastometric tests, the properties of the material are insensitive to the method of testing and to the sample dimensions.

Conventional and Multiscale Modeling of Microstructure Evolution During Laminar Cooling of DP Steel Strips

Physical and numerical simulations of the hot rolling and laminar cooling of DP steel strips are presented in the paper. The objectives of the paper were twofold. Physical simulations of hot plastic deformation were used to identify and validate numerical models. Validated models were applied to simulate the manufacturing of DP steel strips. Conventional flow stress model and microstructure evolution model were used in the hot deformation part. The approach to the complex systems analysis based on global thermodynamic characterization and detailed microstructure characterization was applied to determine equilibrium state at various temperatures. Finally, two numerical models were used to simulate kinetics of austenite decomposition at varying temperatures: the first, conventional model based on the Avrami equation, and the second, the discrete Cellular Automata approach. Plastometric tests and stress relaxation tests were used for identification of the hot rolling model for the DP steel. Dilatometric tests were performed to identify the phase transformation models. Verification confirmed good accuracy of all models. Validated models were applied to simulate the manufacturing of DP steel strips. Influence of technological parameters (e.g., strip thickness and velocity, active sections in the laminar cooling, and water flux in the sections) on the DP microstructure was analyzed. The cooling schedules, which give required microstructures were proposed. The numerical tool, which simulates manufacturing chain for DP steel strips is the main output of the paper.

Identification of rheological models and boundary conditions in metal forming

Identification of rheological models and boundary conditions in metal forming is performed. Various rheological models, from classical closed-form equations to advanced multiscale models, are investigated. Capabilities and limitations of models are discussed. Inverse method is applied to identification of the models on the basis of plastometric tests. Ability of this technique to eliminate the effect of inhomogeneity of deformation is demonstrated. Importance of rheological parameters is evaluated using parameter sensitivity analysis. Finally, sensitivity of the inverse approach with respect to assumptions is performed and accuracy of this approach is evaluated. Good practice guide for identification of rheological model is proposed.

Application of inverse analysis with metamodelling for identification of metal flow stress

Abstract The problem of effectiveness of the inverse algorithms used for identification of material model is investigated in the paper. Identification of flow stress models in metal forming processes is considered. This identification is usually performed by coupling the Finite element (FE) model with optimisation techniques which leads to long computing times. A proposition of application of the metamodel in the inverse analysis is presented in the paper. Metamodel is an alternative for the FE model. Artificial neural network was used as a metamodel of the axisymmetrical compression test. Experiments were performed on the Gleeble 3800 simulator for various materials and inverse calculations with the metamodel were performed. Validation of the results confirmed with higher degree of accuracy of the proposed approach. Dans cet article, on examine le problème d’efficacité des algorithmes inverses utilisés dans l’identification de modèle de matériau. On considère l’identification de modèles de contrainte d’écoulement dans les procédés de traitement du métal. Cette identification est habituellement effectuée en couplant le modèle d’EF à des techniques d’optimisation, ce qui mène à de longues durées de calculs. Dans cet article, on propose l’application du métamodèle dans l’analyse inverse. Le métamodèle est une substitution du modèle d’EF. On a utilisé le réseau neuronal artificiel comme métamodèle de l’essai de compression axisymétrique. On a effectué des expériences avec le simulateur Gleeble 3800 pour des matériaux variés et l’on a effectué des calculs inverses à l’aide du métamodèle. La validation des résultats a confirmé le très bon degré d’exactitude de cette approche.

Rheological model of Cu based alloys accounting for the preheating prior to deformation

Development of the rheological model of copper based alloys, accounting for the state of the solid solution prior to deformation, is the objective of the paper. Two alloys are considered, Cu-1%Cr and Cu-0.7%Cr-1%Si-2%Ni. Plastometric tests were performed at various temperatures and various strain rates. Different preheating conditions before the tests were applied aimed at investigation of the effect of the initial microstructure on the flow stress. Three different rheological models for the investigated alloys were developed using inverse analysis of the tests results. Accuracy of the inverse analysis for various models was compared and the best model was selected. This model allowed comparison of the flow stress for various preheating schedules at different temperatures and strain rates, including also those which were not applied in the plastometric tests. Developed models were implemented into the finite element code FORGE based on the Norton-Hoff visco-plastic flow rule and simulations of forging of the alloys were performed.

Data Exploration Approach Versus Sensitivity Analysis for Optimization of Metal Forming Processes

Product properties for innovative materials, e.g. dual phase steels, require precise control of production processes. Difficulties in optimization of process parameters correspond with large number of control variables, which should be considered in the technology design. Sensitivity analysis allows evaluating the importance of all process inputs on the final properties of material. Information on the most important inputs is crucial for further design of the process. Application of sensitivity analysis requires detailed knowledge of the process phenomena as well as the definition of the mathematical model of the thermomechanical process. Furthermore, some sensitivity analysis algorithms are of the high computational cost. Presented work concerns possibility of the application of data exploration approach in evaluation of the importance of process inputs as the alternative for sensitivity analysis. Use of data mining algorithms eliminates necessity of mathematical model development, it also does not require any apriori knowledge about the process. Authors presents the comparison of sensitivity analysis and data exploration approach in evaluating relationships between inputs and outputs of the hot rolling for dual phase steel strips. The presented approach and the perspectives of the practical application could lead to significant decrease of time necessary for the computations of process design. The theoretical considerations are supplemented with the results of both types of analysis.

Identification of Multi-inclusion Statistically Similar Representative Volume Element for Advanced High Strength Steels by Using Data Farming Approach☆

Abstract Statistically Similar Representative Volume Element (SSRVE) is used to simplify computational domain for microstructure representation of material in multiscale modelling. The procedure of SSRVE creation is based on optimization loop which allows to find the highest similarity between SSRVE and an original material microstructure. The objective function in this optimization is built upon computationally intensive numerical methods, including simulations of virtual material deformation, which is very time consuming. To avoid such long lasting calcu- lations we propose to use the data farming approach to identification of SSRVE for Advanced High Strength Steels (AHSS) characterized by multiphase microstructure. The optimization method is based on a nature inspired approach which facilitates distribution and parallelization. The concept of SSRVE creation as well as the software architecture of the proposed solution is described in the paper in details. It is followed by examples of the results obtained for the identification of SSRVE parameters for DP steels which are widely exploited in modern automotive industry. Possible directions for further development as well as possible industrial applications are described in the conclusions.

Modelling techniques for optimizing metal forming processes

Abstract: This chapter presents optimization techniques and strategies, and their applications to solving problems associated with metal forming processes. Most of the classical optimization models for such processes are strongly non-linear and demand long computing times for complex numerical simulations. More robust and time-effective optimization methods have been intensively researched. Probabilistic, nature-inspired optimization techniques belonging to this group of robust methods, as well as metamodel-driven and approximation-based optimization strategies, are discussed here. Some case studies of the application of these methods to particular metal forming problems are presented.

Optimization as a support for design of hot rolling technology of dual phase steel strips

The objective of the paper was performing of the sensitivity analysis of the model used for design of manufacturing technology for auto body parts made of the Advanced High Strength Steels (AHSS). Dual phase steel was considered as an example. The sensitivity analysis was performed to evaluate the importance of all variables as far as their influence on the finishing rolling temperature and grain size. The phase composition after cooling was also considered. An arbitrary hot rolling process characterized only by a number of passes and cooling conditions between passes, as well as by laminar cooling parameters, was selected for the analysis. Metamodel of the rolling cycle was developed to decrease the computing costs for the optimization task. Modified Avrami equation was used for modelling phase transformations during cooling. Such process parameters as the initial temperature, interpass times, heat exchange coefficients and rolling velocities were selected as optimization variables for the rolling proces...