Wyniki wyszukiwania - BazTech

1

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

|

2023

|

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

Comparing deterministic and statistical approaches for predicting “short can” defects in aluminium beverage can production

Baran Wojciech, Regulski Krzysztof, Kąc Sławomir, Milenin Andrij

Computer Methods in Materials Science

|

2023

|

Vol. 23, No. 2

29--38

EN

In the production of beverage cans, “short can” defects in the form of material discontinuities can occur during the deep drawing of cylindrical thin-walled aluminium products. These defects have a significant impact on production efficiency and scrap generation, and their occurrence is influenced by material and process properties. To determine the main influence of material on defect occurrence, two approaches were used: deterministic analysis of mechanical properties and microstructure, as well as statistical processing of production data using decision tree models. The latter approach was found to be more efficient, and a numerical tool was developed based on this approach to predict and reduce defect occurrence in the production process.

3

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

|

2023

|

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.

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

|

2022

|

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

Eksploracja danych i uczenie maszynowe w aspektach pozyskiwania wiedzy o produkcji i przetwarzaniu metali na potrzeby Przemysłu 4.0

Regulski Krzysztof

Przegląd Odlewnictwa

|

2021

|

Vol. 71, nr 1-2

18--23

PL

Metodyka automatycznego odkrywania wiedzy o procesach wytwarzania i przetwarzania metali obejmuje problemy związane z (1) akwizycją danych i integracją ich w aspekcie dalszej eksploracji, (2) doborem i adaptacją metod uczenia maszynowego – indukcji reguł, predykcji zmiennych ilościowych i jakościowych, (3) formalizacją wiedzy w odpowiednich reprezentacjach: regułowej, zbiorów rozmytych, zbiorów przybliżonych czy wreszcie logiki deskrypcyjnej oraz (4) integracją wiedzy w repozytoriach opisanych modelami semantycznymi czyli ontologiami. Autor przedstawił możliwość osiągnięcia równowagi pomiędzy wygodą użytkowania a precyzją w przypadku pozyskiwania wiedzy z małych zbiorów. Badania wykazały, że drzewa decyzyjne są wygodnym narzędziem odkrywania wiedzy i dobrze radzą sobie z problemami silnie nieliniowymi, a wprowadzenie dyskretyzacji poprawia ich działanie. Zastosowanie metod analizy skupień umożliwiło też wyciąganie bardziej ogólnych wniosków, przez co udowodniono tezę, że granulacja informacji pozwala znaleźć wzorce nawet w małych zbiorach danych. Opracowano w ramach badań procedurę postępowania w analizie małych zbiorów danych eksperymentalnych dla modeli multistage, multivariate & multivariable, co może w znacznym stopniu uprościć takie badania w przyszłości.

EN

The methodology of automatic knowledge discovery about metal production and processing processes includes problems related to (1) data acquisition and integration in the aspect of further exploration, (2) selection and adaptation of machine learning methods – rule induction, quantitative and qualitative variable prediction, (3) formalization knowledge in appropriate representations: rule, fuzzy sets, rough sets and finally descriptive logic and (4) integration of knowledge in repositories described by semantic models or ontologies. The author presented the possibility of achieving a balance between ease of use and precision when acquiring knowledge from small collections. Research has shown that decision trees are a convenient tool for discovering knowledge and that they deal well with strongly non-linear problems, and the introduction of discretization improves their operation. The use of cluster analysis methods also made it possible to draw more general conclusions, which proved the thesis that granulation of information allows finding patterns even in small data sets. As part of the research, a procedure was developed for analyzing small experimental data sets for multistage, multivariate & multivariable models, which can greatly simplify such research in the future.

6

Eksploracja danych i uczenie maszynowe w aspektach pozyskiwania wiedzy o produkcji i przetwarzaniu metali na potrzeby Przemysłu 4.0

Regulski Krzysztof

Hutnik, Wiadomości Hutnicze

|

2020

|

Vol. 87, nr 4

75--81

PL

Metodyka automatycznego odkrywania wiedzy o procesach wytwarzania i przetwarzania metali obejmuje problemy związane z (1) akwizycją danych i integracją ich w aspekcie dalszej eksploracji, (2) doborem i adaptacją metod uczenia maszynowego ― indukcji reguł, predykcji zmiennych ilościo wych i jakościowych, (3) formalizacją wiedzy w odpowiednich reprezentacjach: regułowej, zbiorów rozmytych, zbiorów przybliżonych czy wreszcie logiki deskrypcyjnej oraz (4) integracją wiedzy w repozytoriach opisanych modelami semantycznymi, czyli ontologiami. Autor przedstawił możliwość osiągnięcia równowagi pomiędzy wygodą użytkowania a precyzją w przypadku pozyskiwania wiedzy z małych zbiorów. Badania wykazały, że drzewa decyzyjne są wygodnym narzędziem odkrywania wiedzy i dobrze radzą sobie z problemami silnie nieliniowymi, a wprowadzenie dyskretyzacji poprawia ich działanie. Zastosowanie metod analizy skupień umożliwiło też wyciąganie bardziej ogólnych wniosków, przez co udowodniono tezę, że granulacja informacji pozwala znaleźć wzorce nawet w małych zbiorach danych. Opracowano w ramach badań procedurę postępowania w analizie małych zbiorów danych eksperymentalnych dla modeli multistage, multivariate & multivariable, co może w znacznym stopniu uprościć takie badania w przyszłości.

EN

The methodology of automatic knowledge discovery about metal production and processing processes includes problems related to (1) data acquisition and integration in the aspect of further exploration, (2) selection and adaptation of machine learning methods - rule induction, quantitative and qualitative variable prediction, (3) formalization knowledge in appropriate representations: rule, fuzzy sets, rough sets and finally descriptive logic, and (4) integration of knowledge in repositories described by semantic models or ontologies. The author presented the possibility of achieving a balance between ease of use and precision when acquiring knowledge from small collections. Research has shown that decision trees are a convenient tool for discovering knowledge and that they deal well with strongly non-linear problems, and the introduction of discretization improves their operation. The use of cluster analysis methods also made it possible to draw more general conclusions, which proved the thesis that granulation of information allows finding patterns even in small data sets. As part of the research, a procedure was developed for analyzing small experimental data sets for multistage, multivariate & multivariable models, which can greatly simplify such research in the future.

7

Multistage discretization and clustering in multivariable classification of the impact of alloying elements on properties of hypoeutectic silumin

Regulski Krzysztof, Wilk-Kołodziejczyk Dorota, Kluska-Nawarecka Stanisława, Szymczak Tomasz, Gumienny Grzegorz, Jaśkowiec Krzysztof

Archives of Civil and Mechanical Engineering

|

2019

|

Vol. 19, no. 1

114--126

EN

The properties of hypoeutectic Al–Si alloy (silumin) with the addition of elements such as Cr, Mo, V and W are described. Changes in silumin microstructure under the impact of these elements result in a change of the mechanical properties. The research includes presentation of procedure for the acquisition of knowledge about these changes directly from experimental results using mixed data mining techniques. The procedure for analyzing small sets of experimental data for multistage, multivariate and multivariable models has been developed. Its use can greatly simplify such research in the future. An interesting achievement is the development of a voting procedure based on the results of classification trees and cluster analysis.

8

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

|

2019

|

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.