Łukasz Rauch

Filtering of Thermomagnetic Data Curve Using Artificial Neural Network and Wavelet Analysis

New methods of filtering of experimental data curves, based on the artificial neural networks and the wavelet analysis are presented in the paper. The thermomagnetic data curves were filtered using these methods. The obtained results were validated using the modified algorithm of the cubic spline approximation.

Application of Metamodels to Identification of Metallic Materials Models

Improvement of the efficiency of the inverse analysis (IA) for various material tests was the objective of the paper. Flow stress models and microstructure evolution models of various complexity of mathematical formulation were considered. Different types of experiments were performed and the results were used for the identification of models. Sensitivity analysis was performed for all the models and the importance of parameters in these models was evaluated. Metamodels based on artificial neural network were proposed to simulate experiments in the inverse solution. Performed analysis has shown that significant decrease of the computing times could be achieved when metamodels substitute finite element model in the inverse analysis, which is the case in the identification of flow stress models. Application of metamodels gave good results for flow stress models based on closed form equations accounting for an influence of temperature, strain, and strain rate (4 coefficients) and additionally for softening due to recrystallization (5 coefficients) and for softening and saturation (7 coefficients). Good accuracy and high efficiency of the IA were confirmed. On the contrary, identification of microstructure evolution models, including phase transformation models, did not give noticeable reduction of the computing time.

Application of Sensitivity Analysis to Grid-Based Procedure Dedicated to Creation of SSRVE

The methods of sensitivity analysis allow to reduce computational cost of multi-iterative optimization procedures by finding the most influential parameters of the particular model. The article presents details of implementation of the numerical library, which is dedicated to sensitivity analysis and can be used by middleware in e-infrastructures. Then, the application of implemented methods to parallel and distributed models is presented on the example of Statistically Similar Representative Volume Element SSRVE in the field of metal forming. The influence of parameters, used in the SSRVE methodology, on accuracy of obtained results and performance of calculations is analyzed. The results of sensitivity analysis are presented in the article as well.

Optimization of parameters of feed-back pulse coupled neural network applied to the segmentation of material microstructure images

The paper presents application of bio-inspired optimization procedures to the problem of image segmentation of material microstructures. The method used for image processing was Feed-Back Pulse Coupled Neural Network (FBPCNN), which is very flexible in the case of highly diversified images, offering interesting results of segmentation. However, six input parameters of FBPCNN have to be adjusted dependently on image content to obtain optimal results. This was the main objective of the paper. Therefore, the procedure of image segmentation assessment was proposed on the basis of number of segments, their size, entropy and fractal dimension. The proposed evaluation was used as objective function in optimization algorithms. The results obtained for Simple Genetic Algorithms, Particle Swarm Optimization and Simulated Annealing are presented.

Problem of Identification of Phase Transformation Models Used in Simulations of Steels Processing

Accuracy of phase transformation models depends on the correctness of coefficients evaluation, adequate to the investigated material. Dilatometric tests combined with the inverse analysis are used to perform identification. Since the problem is nonlinear, analytical approach is not possible and the inverse solution is transferred into the optimization task. It leads to difficulties typical for optimization of multivariable function such as local minima and lack of proof of the uniqueness. The problem of the effectiveness and uniqueness of the inverse algorithms used for identification of phase transformation models for steels was investigated for two models. The first was a modified JMAK (Johnson–Mehl–Avrami–Kolmogorov) equation. The second was an upgrade of the Leblond equation, in which second-order derivative of the volume fraction with respect to time was introduced. In classical identification, the result for one transformation depends on the coefficients for the remaining transformations and optimization has to be performed several times until the compatibility between transformations is reached. To avoid encountered problems, complex optimization simultaneously for all coefficients in the models was performed. This approach was based on nature-inspired optimization techniques. Models with identified coefficients for various steels were validated in simulations of industrial processes of laminar cooling and continuous annealing of strips.

Ontology Dedicated to Knowledge-Driven Optimization for ICME Approach

Development of new materials, products and technologies with the ICME approach requires challenging computations, controlled by optimization algorithms. A computational time might be decreased with a “knowledge-driven optimization”—an optimization process is controlled not only by a numerical algorithm, but also by a Knowledge Based System. That requires development of a common language, able to cover communication between numerical models without sophisticated translators. There are several formalisms of knowledge representation, but the most common ones are based on First Order Logic (FOL) and Description Logic (DL). None of them meets all the requirements of knowledge management in ICME processes. We present an approach to development of an environment for knowledge management, combining DL and FOL. An exemplary multiscale problem is described, as well as an OWL2 based ontology and rules controlling an optimization process.

Hybrid computer system for the identification of metallic material models on the basis of laboratory experiments

The identification of the proper parameters of material models plays a crucial role in the design of production technologies, especially in the case of modern materials with diversified properties under different boundary conditions. The procedure of identification is usually based on an optimization algorithm that uses sophisticated numerical simulations as a part of the goal function and compares the obtained results with experimental tests. Despite its reliability, such an approach is numerically inefficient. This paper presents the concept of how to replace the most numerically-demanding part of the identification procedure with metamodels, allowing us to maintain uniform result quality. The computer system, which allows us to manage input data, metamodels, and calculations, is proposed and described in detail in this paper. Finally, the proposed approach is validated on the basis of tests performed in the laboratory.

Numerical simulations of hypoeutectoid steels under loading conditions, based on image processing and digital material representation

Numerical simulations of material behavior under loading conditions play crucial role in determination of final properties of material, design of production technology, lifecycle modeling, etc. Special interest in this area is devoted to simulations of microstructures with precisely described grains, inclusions or crystallographic orientation, according to the idea of the Digital Material Representation (DMR). The DMR is applied in the present work to simulate the hypoeutectoid steel, which due to presence of Widmannstatten ferrite is characterized by specific properties. The 2D microstructure model is prepared on the basis of the optical microscopy image as a result of image segmentation algorithm. Specific material properties are attached to each microstructure component. Furthermore, the model is equipped with homogenic mesh and processed with the Finite Element (FE) Forge2 software. The results obtained from the simulations are discussed and presented in the paper as well.