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Artificial Neural Networks as a Tool for Supporting a Moulding Sand Control System Based on the Dependency between Selected Moulding Sand Properties

Mrzygłód Barbara, Jakubski Jarosław, Opaliński Andrzej, Regulski Krzysztof

Journal of Casting & Materials Engineering

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2023

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Vol. 7, no. 2

15--21

EN

The article presents the potential for using artificial neural networks to support decisions related to the rebonding of green moulding sand. The basic properties of the moulding sand tested in foundries are discussed, especially compactibility as it gives the most information about the quality of green moulding sand. First, the data that can predict the compactibility value without the need for testing are defined. Next, a method for constructing an artificial neural network is presented and the network model which produced the best results is analysed. Additionally, two applications were designed to allow the investigation results to be searchable by determining the range of values of the moulding sand parameters.

2

The Use of the XGBoost and Kriging Methods in the Prediction of the Microstructure of CGI Cast Iron

Sztangret Łukasz, Olejarczyk-Wożeńska Izabela, Regulski Krzysztof, Gumienny Grzegorz, Mrzygłód Barbara

Archives of Foundry Engineering

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2023

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Vol. 23, iss. 4

22--33

EN

Compacted Graphite Iron (CGI) is a unique casting material characterized by its graphite form and extensive matrix contact surface. This type of cast iron has a tendency towards direct ferritization and possesses a complex set of intriguing properties. The use of data mining methods in modern foundry material development facilitates the achievement of improved product quality parameters. When designing a new product, it is always necessary to have a comprehensive understanding of the influence of alloying elements on the microstructure and consequently on the properties of the analyzed material. Empirical studies allow for a qualitative assessment of the above-mentioned relationships, but it is the use of intelligent computational techniques that allows for the construction of an approximate model of the microstructure and, consequently, precise predictions. The formulated prognostic model supports technological decisions during the casting design phase and is considered as the first step in the selection of the appropriate material type.

3

Modelling the high‑temperature deformation characteristics of S355 steel using artificial neural networks

Olejarczyk-Wożeńska Izabela, Mrzygłód Barbara, Hojny Marcin

Archives of Civil and Mechanical Engineering

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2023

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Vol. 23, no. 1

art. no. e1, 2023

EN

In this study, artificial neural networks were used to predict the plastic flow behaviour of S355 steel in the process of high-temperature deformation. The aim of the studies was to develop a model of changes in stress as a function of strain, strain rate and temperature, necessary to build an advanced numerical model of the soft-reduction process. The high-temperature characteristics of the tested steel were determined with a Gleeble 3800 thermo-mechanical simulator. Tests were carried out in the temperature range of 400-1450 °C for two strain rates, i.e. 0.05 and 1 s-1. The test results were next used to develop and verify a rheological model based on artificial neural networks (ANNs). The conducted studies show that the selected models offer high accuracy in predicting the high-temperature flow behaviour of S355 steel and can be successfully used in numerical modelling of the soft-reduction process.

4

Bainite transformation time model optimization for Austempered Ductile Iron with the use of heuristic algorithms

Olejarczyk-Wożeńska Izabela, Opaliński Andrzej, Mrzygłód Barbara, Regulski Krzysztof, Kurowski Wojciech

Computer Methods in Materials Science

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2022

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Vol. 22, No. 3

125--136

EN

The paper presents the application of heuristic optimization methods in identifying the parameters of a model for bainite transformation time in ADI (Austempered Ductile Iron). Two algorithms were selected for parameter optimization – Particle Swarm Optimization and Evolutionary Optimization Algorithm. The assumption of the optimization process was to obtain the smallest normalized mean square error (objective function) between the time calculated on the basis of the identified parameters and the time derived from the experiment. As part of the research, an analysis was also made in terms of the effectiveness of selected methods, and the best optimization strategies for the problem to be solved were selected on their basis.

5

Modelling of Plastic Flow Behaviour of Metals in the Hot Deformation Process Using Artificial Intelligence Methods

Mrzygłód Barbara, Łukaszek-Sołek Aneta, Olejarczyk-Wożeńska Izabela, Pasierbiewicz Karolina

Archives of Foundry Engineering

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2022

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Vol. 22, iss. 3

41--52

EN

Hot deformation of metals is a widely used process to produce end products with the desired geometry and required mechanical properties. To properly design the hot forming process, it is necessary to examine how the tested material behaves during hot deformation. Model studies carried out to characterize the behaviour of materials in the hot deformation process can be roughly divided into physical and mathematical simulation techniques. The methodology proposed in this study highlights the possibility of creating rheological models for selected materials using methods of artificial intelligence, such as neuro-fuzzy systems. The main goal of the study is to examine the selected method of artificial intelligence to know how far it is possible to use this method in the development of a predictive model describing the flow of metals in the process of hot deformation. The test material was Inconel 718 alloy, which belongs to the family of austenitic nickel-based superalloys characterized by exceptionally high mechanical properties, physicochemical properties and creep resistance. This alloy is hardly deformable and requires proper understanding of the constitutive behaviour of the material under process conditions to directly enable the optimization of deformability and, indirectly, the development of effective shaping technologies that can guarantee obtaining products with the required microstructure and desired final mechanical properties. To be able to predict the behaviour of the material under non-experimentally tested conditions, a rheological model was developed using the selected method of artificial intelligence, i.e. the Adaptive Neuro-Fuzzy Inference System (ANFIS). The source data used in these studies comes from a material experiment involving compression of the tested alloy on a Gleeble 3800 thermo-mechanical simulator at temperatures of 900, 1000, 1050, 1100, 1150oC with the strain rates of 0.01 - 100 s-1 to a constant true strain value of 0.9. To assess the ability of the developed model to describe the behaviour of the examined alloy during hot deformation, the values of yield stress determined by the developed model (ANFIS) were compared with the results obtained experimentally. The obtained results may also support the numerical modelling of stress-strain curves.

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Formalizacja zapisów wiedzy metalurgicznej na potrzeby systemówekspertowych

Mrzygłód Barbara

Hutnik, Wiadomości Hutnicze

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2020

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Vol. 87, nr 4

69--74

PL

Celem pracy było opracowanie metodologii formalizacji wiedzy metalurgicznej na potrzeby wykorzystania jej do tworzenia komputerowych reprezentacji wiedzy dla systemów ekspertowych. Osiągniecie celu wymagało rozwiązania problemów: identyfikacji źródeł wiedzy, pozyskiwania wiedzy, integracji wiedzy, doboru formalnej metody reprezentacji wiedzy a także opracowania jej komputerowej reprezentacji. Jako formę reprezentacji wiedzy wykorzystano sztuczne sieci neuronowe i wskazano na możliwość ich wykorzystania do wspomagania dwóch procesów metalurgicznych, tj. procesu wytwarzania materiałów odlewanych z żeliwa sferoidalnego oraz procesu kucia matrycowego, jednego z procesów przeróbki plastycznej metali.

EN

The aim of the study was to develop a methodology to formalize metallurgical expertise for the purpose of using it to create computer representations of knowledge for expert systems. Achieving the goal required solving problems: identifying knowledge sources, acquiring knowledge, integrating knowledge, choosing a formal method of knowledge representation, and developing its computer representation. As a form of knowledge representation, artificial neural networks were used and the possibility of their use was indicated to support two metallurgical processes, i.e. the process of manufacturing ductile iron cast materials and matrix forging process, one of the metal forming processes.

7

Medical data exploration based on the heterogeneous data sources aggregation system

Opaliński Andrzej, Regulski Krzysztof, Mrzygłód Barbara, Głowacki Mirosław, Kania Aleksander, Nastałek Paweł, Celejewska-Wójcik Natalia, Bochenek Grażyna, Sładek Krzysztof

Annals of Computer Science and Information Systems

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2019

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Vol. 18

591--597

EN

The paper presents the implementation and use of the IT system implemented in the Department of Pulmonology of The University Hospital in Cracow. The system integrates data from heterogeneous sources of therapy, diagnosis and medical test results of patients with Obstructive Sleep Apnea (OSA). The article presents the main architectural assumptions of the system, as well as an example of data mining analyzes based on the data served by the system. The example of the research aims to present the possibilities offered by the integration of clinical data in telemedicine and the diagnosis of patients with sleep disordered breathing that may lead to certain comorbidities and premature death.