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The numerical FEMs imulation of the metal flow during the drawing processes supplies an information about many process parameters, useful for the design of these processes. The effective strain distribution is very interesting for the... more
The numerical FEMs imulation of the metal flow during the drawing processes supplies an information about many process parameters, useful for the design of these processes. The effective strain distribution is very interesting for the analysis of the applied drawing technology. The results of FEM simulation of 1H18N9T steel rods are presented. The simulation was performed using commercial FORGE 2 program. The numerically calculated fields of strains were validated experimentally and compared with the results obtained by means of the viscoplastic method. The good agreement between the theoretical and the experimental results is observed. Obtained results show the high sensitivity of the effective strain distribution with respect to the technological parameters of the drawing process. The knowledge of the effective strain field allows the prediction of the localization of the faults in drawing products and can be used for the improvement of the product quality by the adequate choice o...
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The extrusion loads as well as the strain and hydrostatic pressure distributions within the deformation zone are investigated in the paper. The distribution modes are influenced by the die geometry and, in this way, the material structure... more
The extrusion loads as well as the strain and hydrostatic pressure distributions within the deformation zone are investigated in the paper. The distribution modes are influenced by the die geometry and, in this way, the material structure can be controlled. The objective of the present work is an analysis of an effect of the die hollow radius on the flow pattern during the direct extrusion. The effect of geometric shape of the die on the metal flow is presented. The experimental analysis was performed using axisymmetrical lead samples and a strong sensitivity of the flow pattern on the hollow radius was observed. The more uniform distribution of strains and mechanical properties in the extruded material was obtained when a die with the radius was applied. Opposite, the measurements show small influence of the die hollow radius on the extrusion loads. The finite element method (FEM) was used in the theoretical investigation of these problems. The results of calculations depend on the...
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The main objective of presented research is an attempt of application of techniques taken from a dynamically developing field of image analysis based on Artificial Intelligence, particularly on Deep Learning, in classification of steel... more
The main objective of presented research is an attempt of application of techniques taken from a dynamically developing field of image analysis based on Artificial Intelligence, particularly on Deep Learning, in classification of steel microstructures. Our research focused on developing and implementation of Deep Convolutional Neural Networks (DCNN) for classification of different types of steel microstructure photographs received from the light microscopy at the TU Bergakademie, Freiberg. First, brief presentation of the idea of the system based on DCNN is given. Next, the results of tests of developed classification system on 8 different types (classes) of microstructure of the following different steel grades: C15, C45, C60, C80, V33, X70 and carbide free steel. The DCNN based classification systems require numerous training data and the system accuracy strongly depend on the size of these data. Therefore, created data set of numerous micrograph images of different types of micro...
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The holistic approaches used for image processing are considered in various types of applications in the domain of applied computer science and pattern recognition. A new image filtering method based on the dynamic particles (DP) approach... more
The holistic approaches used for image processing are considered in various types of applications in the domain of applied computer science and pattern recognition. A new image filtering method based on the dynamic particles (DP) approach is presented. It employs physics principles for the 3D signal smoothing. The obtained results were compared with commonly used denoising techniques including weighted average, Gaussian smoothing and wavelet analysis. The calculations were performed on two types of noise superimposed on the image data i.e. Gaussian noise and salt-pepper noise. The algorithm of the DP method and the results of calculations are presented.
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... 2008. 13. Lukasik, L., Rauch, L.: Estimation of parameters of Feed-Back Pulse Coupled Neural Networks (FBPCNN) for purposes of microstructure images segmentation. Computer Methods in Materials Science (2009) (in print). ...
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The paper deals with the problem of the description of the flow stress in the finite element programs for the hot forming processes. Two aspects of the problem are considered. First is the fact that the flow stress function in the finite... more
The paper deals with the problem of the description of the flow stress in the finite element programs for the hot forming processes. Two aspects of the problem are considered. First is the fact that the flow stress function in the finite element codes is used for the local, current values of the temperatures, strain rates and strains, therefore, this
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The objective of this paper is to demon- strate a new approach to the optimization of production chains, based on metamodelling and application of nature-inspired tech- niques. The optimization of a metal forming process involving... more
The objective of this paper is to demon- strate a new approach to the optimization of production chains, based on metamodelling and application of nature-inspired tech- niques. The optimization of a metal forming process involving microalloyed steel has been chosen as the test case and a meta- model of this manufacturing process has been constructed. To solve resulting opti- mization problem, three modern, nature- inspired optimization techniques were tested: Simple Genetic Algorithm (SGA), Hierarchical Genetic Strategy (HGS) and Modified Particle Swarm Optimization (MPSO).
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Microstructure evolution model based on the differential equation describing evolution of dislocations was proposed. Sensitivity analysis was performed and parameters with the strongest influence on the output of the model were revealed.... more
Microstructure evolution model based on the differential equation describing evolution of dislocations was proposed. Sensitivity analysis was performed and parameters with the strongest influence on the output of the model were revealed. Identification of the model coefficients was performed for various metallic materials using inverse analysis for experimental data. The model was implemented in the finite element code and simulations of various hot forming processes were performed.
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The design of modern construction materials with heterogeneous microstructures requires a numerical model that can predict the distribution of microstructural features instead of average values. The accuracy and reliability of such models... more
The design of modern construction materials with heterogeneous microstructures requires a numerical model that can predict the distribution of microstructural features instead of average values. The accuracy and reliability of such models depend on the proper identification of the coefficients for a particular material. This work was motivated by the need for advanced experimental data to identify stochastic material models. Extensive experiments were performed to supply data to identify a model of austenite microstructure evolution in steels during hot deformation and during the interpass times between deformations. Two sets of tests were performed. The first set involved hot compressions with a nominal strain of 1. The second set involved hot compressions with lower nominal strains, followed by holding at the deformation temperature for different times. Histograms of austenite grain size after each test were measured and used in the identification procedure. The stochastic model, ...
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Internal variable method (IVM) is an alternative for conventional models describing processing of materials. When the latter are used the history of the process is not accounted for. Change of the process conditions moves the model to a... more
Internal variable method (IVM) is an alternative for conventional models describing processing of materials. When the latter are used the history of the process is not accounted for. Change of the process conditions moves the model to a new equation of state without delay, which is observed in experiments. When external variables are replaced by internal ones, this disadvantage is eliminated. An approach based on works [1,2], which uses dislocation density as the independent variable, was considered. The differential equation describing evolution of dislocation populations () is: cr d t A B t C t t dt (1)
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This work is concerned with complex optimization problems which can be divided into multiple, multi-dimensional problems arranged linearly (as can be observed in the multi-stage industrial processes). The relations between complexity of... more
This work is concerned with complex optimization problems which can be divided into multiple, multi-dimensional problems arranged linearly (as can be observed in the multi-stage industrial processes). The relations between complexity of the problem, level of aggregation of stages into larger groups, and efficiency of search for optimal solution were investigated.
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It is often difficult to estimate parameters for a two-stage production process of blister copper (containing 99.4 wt.% of Cu metal) as well as those for most industrial processes with high accuracy, which leads to problems related to... more
It is often difficult to estimate parameters for a two-stage production process of blister copper (containing 99.4 wt.% of Cu metal) as well as those for most industrial processes with high accuracy, which leads to problems related to process modeling and control. The first objective of this study was to model flash smelting and converting of Cu matte stages using three different techniques: artificial neural networks, support vector machines, and random forests, which utilized noisy technological data. Subsequently, more advanced models were applied to optimize the entire process (which was the second goal of this research). The obtained optimal solution was a Pareto-optimal one because the process consisted of two stages, making the optimization problem a multi-criteria one. A sequential optimization strategy was employed, which aimed for optimal control parameters consecutively for both stages. The obtained optimal output parameters for the first smelting stage were used as input parameters for the second converting stage. Finally, a search for another optimal set of control parameters for the second stage of a Kennecott–Outokumpu process was performed. The optimization process was modeled using a Monte-Carlo method, and both modeling parameters and computed optimal solutions are discussed.
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The paper is a continuation of the authors' earlier work dealing with application of artificial neural networks to the prediction of yield stress in hot forming of metals. At present, the task of the network is to predict a... more
The paper is a continuation of the authors' earlier work dealing with application of artificial neural networks to the prediction of yield stress in hot forming of metals. At present, the task of the network is to predict a time-derivative of the dislocation density during hot deformation. The inputs are the state of the material defined by the current dislocation density and by the time-integral of strain, the current strain rate and temperature. The flow stress curve is determined from the dislocation density vs. strain function, which is calculated using a finite difference technique in which the time-derivative of the dislocation density is supplied by the artificial neural network. Examples of calculations are presented for the axi-symmetrical compression of low carbon steel
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The need for a reliable prediction of the distribution of microstructural parameters in metallic materials during processing was the motivation for this work. The model describing the evolution of dislocation populations, which considers... more
The need for a reliable prediction of the distribution of microstructural parameters in metallic materials during processing was the motivation for this work. The model describing the evolution of dislocation populations, which considers the stochastic aspects of occurring phenomena, was formulated. The validation of the presented model requires the application of proper parameters corresponding to the considered materials. These parameters have to be identified through the inverse analysis, which, on the other hand, uses optimization methods and requires the formulation of the appropriate objective function. In our case, where the model involves the stochastic parameters, it is a crucial task. Therefore, a specific form of the objective function for the inverse analysis was developed using a measure based on histograms. The elaborated original stochastic approach to modeling the phenomena occurring during the thermomechanical treatment of metals was validated on commercially pure c...
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Enhancing strength-ductility synergy of materials has been for decades an objective of research on structural metallic materials. It has been shown by many researchers that significant improvement of this synergy can be obtained by... more
Enhancing strength-ductility synergy of materials has been for decades an objective of research on structural metallic materials. It has been shown by many researchers that significant improvement of this synergy can be obtained by tailoring heterogeneous multiphase microstructures. Since large gradients of properties in these microstructures cause a decrease of the local fracture resistance, the objective of research is to obtain smoother gradients of properties by control of the manufacturing process. Advanced material models are needed to design such microstructures with smooth gradients. These models should supply information about distributions of various microstructural features, instead of their average values. Models based on stochastic internal variables meet this requirement. Our objective was to account for the random character of the recrystallization and to transfer this randomness into equations describing the evolution of dislocations and grain size during hot deforma...
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The paper deals with the problem of long computing times during optimization of real processes. All commonly used optimization methods search for optimal solution in iterative way. Therefore, they require many simulations of the model of... more
The paper deals with the problem of long computing times during optimization of real processes. All commonly used optimization methods search for optimal solution in iterative way. Therefore, they require many simulations of the model of optimized process. In case of numerous processes (e.g. metallurgical) the models are often complex and require time consuming numerical computations. This cause that optimization time may be unacceptable high. This is the reason why new optimization methods which need less simulation runs are searched. The main goal of the paper is to present a new, more efficient approximation based optimization method. The elaborated method was validated using frequently employed benchmark functions and applied in optimization of laminar cooling of rolled DP steel strips process