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Number of results: 9
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Abstract

Stereological description of dispersed microstructure is not an easy task and remains the subject of continuous research. In its practical aspect, a correct stereological description of this type of structure is essential for the analysis of processes of coagulation and spheroidisation, or for studies of relationships between structure and properties. One of the most frequently used methods for an estimation of the density Nv and size distribution of particles is the Scheil - Schwartz – Saltykov method. In this article, the authors present selected methods for quantitative assessment of ductile iron microstructure, i.e. the Scheil - Schwartz – Saltykov method, which allows a quantitative description of three-dimensional sets of solids using measurements and counts performed on two-dimensional cross-sections of these sets (microsections) and quantitative description of three-dimensional sets of solids by X-ray computed microtomography, which is an interesting alternative for structural studies compared to traditional methods of microstructure imaging since, as a result, the analysis provides a three-dimensional imaging of microstructures examined.
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Authors and Affiliations

B. Mrzygłód
P. Matusiewicz
A. Tchórz
I. Olejarczyk-Wożeńska
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Abstract

A mathematical model of austenite - bainite transformation in austempered ductile cast iron has been presented. The model is based on a model developed by Bhadeshia [1, 2] for modelling the bainitic transformation in high-silicon steels with inhibited carbide precipitation. A computer program has been developed that calculates the incubation time, the transformation time at a preset temperature, the TTT diagram and carbon content in unreacted austenite as a function of temperature. Additionally, the program has been provided with a module calculating the free energy of austenite and ferrite as well as the maximum driving force of transformation. Model validation was based on the experimental research and literature data. Experimental studies included the determination of austenite grain size, plotting the TTT diagram and analysis of the effect of heat treatment parameters on the microstructure of ductile iron. The obtained results show a relatively good compatibility between the theoretical calculations and experimental studies. Using the developed program it was possible to examine the effect of austenite grain size on the rate of transformation.

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Authors and Affiliations

I. Olejarczyk-Wożeńska
M. Głowacki
H. Adrian
B. Mrzygłód
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Abstract

This article discusses the results of studies using the developed artificial neural networks in the analysis of the occurrence of the four main mechanisms destroying the selected forging tools subjected to five different surface treatment variants (nitrided layer, pad welded layer and three hybrid layers, i.e. AlCrTiSiN, Cr/CrN and Cr/AlCrTiN). Knowledge of the forging tool durability, needed in the process of artificial neural network training, was included in the set of training data (about 800 records) derived from long-term comprehensive research carried out under industrial conditions. Based on this set, neural networks with different architectures were developed and the results concerning the intensity of the occurrence of thermal-mechanical fatigue, abrasive wear, mechanical fatigue and plastic deformation were generated for each type of the applied treatment relative to the number of forgings, pressure, friction path and temperature.

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Authors and Affiliations

M. Hawryluk
Barbara Mrzygłód
ORCID: ORCID
Z. Gronostajski
ORCID: ORCID
M. Głowacki
Izabela Olejarczyk-Wożeńska
ORCID: ORCID
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Abstract

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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Authors and Affiliations

Barbara Mrzygłód
ORCID: ORCID
A. Łukaszek-Sołek
1
ORCID: ORCID
Izabela Olejarczyk-Wożeńska
ORCID: ORCID
K. Pasierbiewicz
1
ORCID: ORCID

  1. AGH University of Science and Technology, Faculty of Metals Engineering and Industrial Computer Science, Cracow, Poland
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Abstract

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.
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Authors and Affiliations

Łukasz Sztangret
1
ORCID: ORCID
Izabela Olejarczyk-Wożeńska
1
ORCID: ORCID
Krzysztof Regulski
1
ORCID: ORCID
Grzegorz Gumienny
2
ORCID: ORCID
Barbara Mrzygłód
1
ORCID: ORCID

  1. AGH University of Science and Technology, Poland
  2. Lodz University of Technology, Poland

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