Wyniki wyszukiwania - Biblioteka Nauki

1

Cyfrowy bliźniak

100%

Wilk-Kołodziejczyk D. , Jaśkowiec K.

Przegląd Odlewnictwa

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2022

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tom Vol. 72, nr 11-12

586--594

PL

Definicja cyfrowego bliźniaka (ang. digital twin DT) odnosi się do cyfrowej repliki fizycznych obiektów, procesów i systemów. Jest to połączenie fizycznego obiektu oraz jego cyfrowego odwzorowania w sposób jak najbardziej kompleksowy i wierny. Do stworzenia takiego narzędzia potrzebne są nie tylko dane techniczne zawarte w specyfikacji, ale również złożone modele zachowań, które pozwalają na wykonanie symulacji i przewidywania wyników wykonania procesów. W artykule przedstawiono przykłady rozwiązań które mogą być podstawą do tego aby taki cyfrowy bliźniak stworzyć. Szczególnie drugi z przykładów jest cenny, ponieważ jest to przykład wdrożonego rozwiązania, którego kontynuacja może doprowadzić do opracowania takiego bliźniaka.

EN

The definition of digital twin (DT) is related to digital replicas of physical objects, processes and systems. This is the connection of the physical object and its digital reproduction in the most systematic and credible way. In order to create such tool not only the technical data, contained in the specification, are needed but also complex behavior models allowing to perform simulations and to expect the results of the performed processes are necessary. Some examples of solutions, which can be the bases of creating such digital twin, are presented in the paper. The second example is especially valuable, since this is the example of the already implemented solution the continuation of which can lead to the development of such twin.

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Prediction of Selected Mechanical Properties in Austempered Ductile Iron with Different Wall Thickness by the Decision Support Systems

70%

Jaśkowiec K. , Opaliński A. , Kustra P. , Jach D. , Wilk-Kołodziejczyk D.

Archives of Foundry Engineering

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2023

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tom Vol. 23, iss. 2

137--144

EN

The structure of Austempered Ductile Iron (ADI) is depend of many factors at individual stages of casting production. There is a rich literature documenting research on the relationship between heat treatment and the resulting microstructure of cast alloy. A significant amount of research is conducted towards the use of IT tools for indications production parameters for thin-walled castings, allowing for the selection of selected process parameters in order to obtain the expected properties. At the same time, the selection of these parameters should make it possible to obtain as few defects as possible. The input parameters of the solver is chemical composition Determined by the previous system module. Target wall thickness and HB of the product determined by the user. The method used to implement the solver is the method of Particle Swarm Optimization (PSO). The developed IT tool was used to determine the parameters of heat treatment, which will ensure obtaining the expected value for hardness. In the first stage, the ADI cast iron heat treatment parameters proposed by the expert were used, in the next part of the experiment, the settings proposed by the system were used. Used of the proposed IT tool, it was possible to reduce the number of deficiencies by 3%. The use of the solver in the case of castings with a wall thickness of 25 mm and 41 mm allowed to indication of process parameters allowing to obtain minimum mechanical properties in accordance with the PN-EN 1564:2012 standard. The results obtained by the solver for the selected parameters were verified. The indicated parameters were used to conduct experimental research. The tests obtained as a result of the physical experiment are convergent with the data from the solver.

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Rezystywność staliwa aluminiowego

70%

Jaśkowiec K. , Bacior M. , Grudzień-Rakoczy M. , Pirowski Z. , Bitka A.

Journal of Applied Materials Engineering

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2019

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tom Vol. 59, no. 3

113--126

PL

W pracy przedstawiono wyniki badań dwóch gatun-ków staliwa (oznaczonych dalej jako Stop 1 oraz Stop 2). Stop 1 zawiera między innymi: 0,25% C, 11,5% Al, 5,85% Cr, 1,97% Mo. W przypadku Stopu 2 zawartość węgla wynosiła 0,035%, natomiast aluminium 13,5%, pierwiastki takie, jak: Cr, Ni, Sb, B, nie przekroczyły 1% udziału w składzie chemicznym tego stopu. Badania rezystywności Stopu 1 przeprowadzono podczas badania odporności na zmęczenie cieplne, natomiast Stop 2 badano na przygotowanym stanowisku, realizując pomiar rezystancji metodą czteropunktową.Dla Stopu 1 przeprowadzono pomiary rezystancji w funkcji czasu, wydłużenia oraz temperatury. Dla porównania przedstawiono wyniki badań innych stopów badanych na tym stanowisku w ramach realizacji innych prac B+R.Korzystając z materiału uzyskanego podczas badania lejności Stopu 2, określono wpływ warunków krzepnięcia na rezystywność. Przedstawiono wyniki badań kalorymetrycznych tego stopu, określając temperaturę przemian fazowych dla różnych warunków krzepnięcia Stopu 2.

EN

The paper presents the results of investigations of two cast steel grades (further referred to as Alloy 1 and Al-loy 2). Alloy 1 contains, among others, 0.25% C, 11.5% Al, 5.85% Cr, 1.97% Mo, in Alloy 2 was obtained 0.035% C, 13.5% Al, elements such as Cr, Ni , Sb, B, did not exceed 1% share in the chemical composition of this alloy. Resistivity tests of Alloy 1 were carried out during the test of resistance to thermal fatigue, while Alloy 2 was tested on a prepared test stand and the resistance was measured using a four-point method.For Alloy 1, the resistance was measured as a function of time, elongation and temperature. For comparison, the results of tests of other alloys conducted on this stand within the framework of other R&D works are presented.Using the material obtained during the test for castability of Alloy 2, the influence of solidification conditions on resistivity was determined. The results of calorimetric tests of this alloy are presented, determining the phase transition temperature for various solidification conditions of Alloy 2.

4

Właściwości termofizyczne staliwa 30MCDB64-M oraz 30NSCDV86-M w stanie odlewanym

70%

Homa M. , Pirowski Z. , Turalska P. , Bitka A. , Jaśkowiec K.

Journal of Applied Materials Engineering

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2019

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tom Vol. 59, no. 2

71--79

PL

W pracy przedstawiono wyniki kompleksowych badań właściwości termofizycznych staliwa 30MCDB64-M oraz 30NSCDV86-M w stanie odlewanym, w tym przewodnictwo temperaturowe, przewodność cieplną, ciepło właściwe, rozszerzalność cieplną oraz zmianę gęstości w funkcji temperatury. Pomiary przewodnictwa temperaturowego wykonano za pomocą Laserowej Analizy Impulsowej (LFA) w zakresie temperatury 25−1000°C. Badania kalorymetryczne w temperaturze od 25°C do 1300°C przeprowadzono metodą skaningowej kalorymetrii różnicowej (DSC), podczas gdy zmiany rozszerzalności cieplnej, współczynnika rozszerzalności cieplnej oraz pomiar zmiany gęstości w funkcji temperatury wyznaczono metodą dylatometryczną (DIL) w zakresie temperatury od 25°C do 900°C. Zmianę przewodności cieplnej w funkcji temperatury wyznaczono za pomocą algorytmów matematycznych oprogramowania Netzsch LFA Analysis 4.8.5.

EN

This paper presents the results of complex investigations into the thermophysical properties of 30MCDB64-M and 30NSCDV86-M cast steel, including temperature conductivity, thermal conductivity, specific heat, thermal expansion and the change of density in the function of temperature. Measurements of temperature conductivity were performed using Laser Flash Analysis (LFA) in the temperature range 25−1000°C. Calorimetric investigations at temperatures from 25°C to 1300°C were performed by Differential Scanning Calorimetry (DSC), while changes in thermal expansion, thermal expansion coefficient and the measurement of density change in the temperature function were determined by the dilatometric method (DIL) in the temperature range from 25°C to 900°C. Changes in thermal conductivity in the temperature function were determined using mathematical algorithms of the Netzsch LFA Analysis 4.8.5 software.

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

61%

Regulski K. , Wilk-Kołodziejczyk D. , Kluska-Nawarecka S. , Szymczak T. , Gumienny G. , Jaśkowiec K.

Archives of Civil and Mechanical Engineering

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2019

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tom 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.

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Selection of casting production parameters with the use of machine learning and data supplementation methods in order to obtain products with the assumed parameters

51%

Wilk-Kołodziejczyk D. , Pirowski Z. , Bitka A. , Wróbel K. , Śnieżyński B. , Doroszewski M. , Jaśkowiec K. , Małysza M.

Archives of Civil and Mechanical Engineering

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2023

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tom Vol. 23, no. 2

art. no. e73, 2023

EN

The main purpose of the research, presented in this publication, was to develop methodology for the construction of predictive models which allow the selection of material production parameters for the material-technological conversion process. The development of prototype modules based on information-decision system allows an initial assessment of the level of feasibility of undertaking this type of operation. Algorithms 1, 2, 3 presented in the article were used to complete the missing data. The result of the algorithm enabled the creation of a data table that specifies the operation of the predictive models indicated in chapter 3 of this article. Entire work is presented with regard to the background of the ADI cast iron production process to locate the requirement where to apply the developed methods in the field of predictive algorithms and data completion algorithms. On the basis of developed methods and predictive algorithms, trial castings were operated.

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Developing a Methodology for Building the Knowledge Base and Application Procedures Supporting the Process of Material and Technological Conversion

51%

Wilk-Kołodziejczyk D. , Jaśkowiec K. , Bitka A. , Pirowski Z. , Grudzień-Rakoczy M. , Chrzan K. , Małysza M. , Doroszewski M.

Archives of Metallurgy and Materials

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2022

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tom Vol. 67, iss. 3

1085--1091

EN

The article presents the developed IT solutions supporting the material and technological conversion process in terms of the possibility of using the casting technology of selected alloys to produce products previously manufactured with the use of other methods and materials. The solutions are based on artificial intelligence, machine learning and statistical methods. The prototype module of the information and decision-making system allows for a preliminary assessment of the feasibility of this type of procedure. Currently, the selection of the method of manufacturing a product is based on the knowledge and experience of the technologist and constructor. In the described approach, this process is supported by the proprietary module of the information and decision-making system, which, based on the accumulated knowledge, allows for an initial assessment of the feasibility of a selected element in a given technology. It allows taking into account a large number of intuitive factors, as well as recording expert knowledge with the use of formal languages. Additionally, the possibility of searching for and collecting data on innovative solutions, supplying the knowledge base, should be taken into account. The developed and applied models should allow for the effective use and representation of knowledge expressed in linguistic form. In this solution, it is important to use methods that support the selection of parameters for the production of casting. The type, number and characteristics of data have an impact on the effectiveness of solutions in terms of classification and prediction of data and the relationships detected.