Wyniki wyszukiwania - BazTech

1

Simulation of the preparation laboratory for light microscopy in the form of a 3D educational game

Honysz R.

Journal of Achievements in Materials and Manufacturing Engineering

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2022

|

Vol. 113, nr 2

78--85

EN

Purpose: The purpose of the work was to create an educational game to familiarize the user with the methodology of preparing a material sample for light microscopy. The goal of the game is to obtain a correct preparation of the sample, the microstructure of which can be observed under a light microscope. Design/methodology/approach: The game was developed in the Unity environment. All three-dimensional machine models, along with the necessary virtual environment and the gameplay scenario, were created. Findings: Due to the use of virtual reality, it has become possible to teach students how to use preparation devices without the need for the physical presence of students in the laboratory in an attractive and safe way. Failure to play the game will not damage material, or equipment, there is no risk to the user’s health. Practical implications: The game has been developed and is available in the Department of Engineering Materials and Biomaterials of the Faculty of Mechanical Engineering of the Silesian University of Technology. Originality/value: The form of a 3D game used in this study is an interesting alternative to traditional teaching aids. It can be used not only by students but also teachers and other people who want to broaden their knowledge about the functioning and methods of operation of laboratory equipment.

2

Optimization of Ferrite Stainless Steel Mechanical Properties Prediction with artificial Intelligence Algorithms

Honysz R.

Archives of Metallurgy and Materials

|

2020

|

Vol. 65, iss. 2

749-753

EN

The article presents a computational model build with the use of artificial neural networks optimized by genetic algorithm. This model was used to research and prediction of the impact of chemical elements and heat treatment conditions on the mechanical properties of ferrite stainless steel. Optimization has allowed the development of artificial neural networks, which showed a better or comparable prediction result in comparison to un-optimized networks has reduced the number of input variables and has accelerated the calculation speed. The introduced computational model can be applied in industry to reduce the manufacturing costs of materials. It can also simplify material selection when an engineer must properly choose the chemical elements and adequate plastic and/or heat treatment of stainless steels with required mechanical properties.

3

Prediction optimization of mechanical properties of ferrite stainless steels after forging treatment with use of genetic algorithms

Honysz R.

Archives of Materials Science and Engineering

|

2019

|

Vol. 100, nr 1/2

13--20

EN

Purpose: The paper describes the use of artificial neural networks to research and predict the effect of chemical components and thermal treatment conditions on stainless steel's mechanical characteristics optimized by genetic algorithm. Design/methodology/approach: The quantity of input variables of artificial neural networks has been optimized using genetic algorithms to enhance the prediction quality of artificial neural network and to enhance their efficiency. Then a computational model was trained and evaluated with optimized artificial neural networks. Findings: Optimization, with the exception of tensile strength, has enabled the creation of artificial neural networks, which either showed a better or similar performance from base networks, as well as a decreased amount of input variables As a consequence, noise data is decreased in the computational model built with the use of these networks. Research limitations/implications: Data analysis was required to confirm the relevance of obtaining information used for modelling to use in training procedures for artificial neural networks. Practical implications: Using artificial intelligence enables the multi-faceted growth of stainless steel engineering, even though there is only a relatively small amount of descriptors. Built and optimized computational model building using optimized artificial neural networks enables prediction of mechanical characteristics after normalization of forged ferritic stainless steels. Originality/value: In order to decrease production expenses of products, an introduced model can be obtained in manufacturing industry. It can also simplify the selection of materials if the engineer has to correctly choose chemical elements and appropriate plastics and/or heat processing of stainless steels, having the necessary mechanical characteristics.

4

Wirtualne laboratorium inżynierii materiałowej

Honysz R., Dobrzański L. A.

LAB Laboratoria, Aparatura, Badania

|

2017

|

R. 22, nr 1

10--16

PL

Artykuł opisuje Wirtualne Laboratorium Inżynierii Materiałowej Instytutu Materiałów Inżynierskich i Biomedycznych w Politechnice Śląskiej. Jest to otwarte środowisko naukowo-badawczo-symulacyjne pomocne w realizacji zadań dydaktycznych i naukowych z dziedziny nauki o materiałach. Laboratorium jest zbiorem symulacji i trenażerów odwzorowują budowę, zasadę działania, funkcjonalność i metodykę obsługi sprzętu badawczego zainstalowanego i dostępnego w rzeczywistych laboratoriach naukowych. Zastosowanie wirtualnego sprzętu, który jest praktycznie niezniszczalny, tanie w eksploatacji i łatwy w użyciu zachęca studentów i pracowników naukowych do niezależnych badań i doświadczeń w sytuacjach, gdy możliwości ich realizacji w prawdziwym laboratorium śledcze będą ograniczone ze względu na wysokie materiału kosztów, trudności w dostępie do rzeczywistych urządzeń lub potencjalnego ryzyka jego uszkodzenia.

5

Virtual laboratory methodology in scientific researches and education

Honysz R., Dobrzański L. A.

Journal of Achievements in Materials and Manufacturing Engineering

|

2017

|

Vol. 84, nr 2

76--84

EN

Purpose: This article is presenting the Material Science Virtual Laboratory. Developed laboratory is an open scientific, investigative, simulating and didactic medium helpful in the realisation of the scientific and didactic tasks in the field of material Science. It is implemented in the Institute of Engineering Materials and Biomaterials of Silesian University of Technology in Gliwice, Poland. Design/methodology/approach: The laboratory is a set of testers and training simulators, set in the Virtuality and created in several languages and the programming techniques, which interprets the properties, functionality and manual rules of actual equipment installed and accessible in the real science labs of scientific universities. Findings: Application of the equipment, that is practically imperishable, cheap in exploitation and ease in the use encourages students and scientific workers to independent audits and experiments in places, where the possibilities of their performance in the real investigative laboratory will be restricted because of the high material costs, difficult access to real equipment or the possible peril of his impairment. Research limitations/implications: The proposed solutions allow the utilisation of the developed virtual environment as a new medium in both, the scientific work performed remotely, as well as in education during classes. Practical implications: The usage possibilities of the virtual laboratory are practically unrestricted; it can be a foundation for any surveys, course or training plan. Originality/value: The project of the virtual laboratory corresponds with the global tendency for expand the investigative and academic centres about the possibilities of training and experiments performance with use of the virtual reality. This enriches investigation and training programmes of the new abilities reserved so far exclusively for effecting only on actual equipment.

6

Prediction optimization of mechanical properties of ferrite stainless steels after rolling treatment with use of genetic algorithms

Honysz R.

Journal of Achievements in Materials and Manufacturing Engineering

|

2015

|

Vol. 68, nr 1

17--24

EN

Purpose: The article discusses the use of artificial neural networks for research and prediction of the impact of chemical elements and heat treatment parameters on the mechanical properties of stainless steels optimized by genetic algorithm. Design/methodology/approach: To improve the quality of artificial neural network models and improve their performance the number of input variables of artificial neural networks has been optimized with use of genetic algorithms. Then a computational model build with optimised artificial neural networks were trained and verified. Findings: Optimization, except of tensile strength Rm case, has allowed the development of artificial neural networks, which either showed a better or comparable result from base networks, and also have a reduced number of input variables. As a result, in computational model constructed with use of these networks the noise information is reduced. Research limitations/implications: Data analysis was needed to verify if obtained data used for modelling are relevant to use them in artificial neural networks training processes. Practical implications: The use of artificial intelligence allows the multifaceted development of stainless steels engineering, even if only a small number of descriptors is available. Constructed and optimised computational model build with use of optimised artificial neural networks allows prediction of mechanical properties of rolled ferritic stainless steels after normalization. Originality/value: Introduced model can be obtain in industry to reduce manufacturing costs of materials. It can also simplify material selection, when engineer must properly choose the chemical elements and adequate plastic and/or heat treatment of stainless steels with required mechanical properties.

7

Application of Artificial Neural Networks in Modeling of Manufactured Front Metallization Contact Resistance for Silicon Solar Cells

Musztyfaga-Staszuk M., Honysz R.

Archives of Metallurgy and Materials

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2015

|

Vol. 60, iss. 3A

1673--1678

EN

This paper presents the application of artificial neural networks for prediction contact resistance of front metallization for silicon solar cells. The influence of the obtained front electrode features on electrical properties of solar cells was estimated. The front electrode of photovoltaic cells was deposited using screen printing (SP) method and next to manufactured by two methods: convectional (1. co-fired in an infrared belt furnace) and unconventional (2. Selective Laser Sintering). Resistance of front electrodes solar cells was investigated using Transmission Line Model (TLM). Artificial neural networks were obtained with the use of Statistica Neural Network by Statsoft. Created artificial neural networks makes possible the easy modelling of contact resistance of manufactured front metallization and allows the better selection of production parameters. The following technological recommendations for the screen printing connected with co-firing and selective laser sintering technology such as optimal paste composition, morphology of the silicon substrate, co-firing temperature and the power and scanning speed of the laser beam to manufacture the front electrode of silicon solar cells were experimentally selected in order to obtain uniformly melted structure well adhered to substrate, of a small front electrode substrate joint resistance value. The prediction possibility of contact resistance of manufactured front metallization is valuable for manufacturers and constructors. It allows preserving the customers’ quality requirements and bringing also measurable financial advantages.

PL

Artykuł przedstawia zastosowanie sztucznych sieci neuronowych do predykcji rezystancji przedniej metalizacji w krzemowych ogniwach słonecznych. Oceniono wpływ tak wytworzonej elektrody przedniej na własności elektryczne ogniw fotowoltaicznych. Przednią elektrodę ogniw fotowoltaicznych naniesiono metodą sitodruku SP (ang. Screen Printing) i następnie wytwarzano dwoma metodami: konwencjonalną (1. wypalanie w piecu taśmowym) i niekonwencjonalną (2. selektywne spiekanie laserowe). Do wyznaczenia rezystancji elektrod przednich zastosowano metodę linii transmisyjnych TLM (ang. Transmission Line Model). Sztuczne sieci neuronowe zostały opracowane z wykorzystaniem pakietu Statistica Neural Network firmy Statsoft. Opracowane sztuczne sieci neuronowe umożliwią modelowanie rezystancji wytworzonej przedniej metalizacji i ułatwią lepszy dobór parametrów produkcji. Następujące zalecenia technologiczne sitodruku połączonego z wypalaniem w piecu i selektywnym spiekaniem laserowym takie jak optymalny skład pasty, morfologię podłoża krzemowego, temperaturę wypalania oraz moc i prędkość skanowania wiązki laserowej, do wytworzenia przedniej elektrody krzemowych ogniw słonecznych dobrano eksperymentalnie celem uzyskania celem uzyskania jednolicie stopionej struktury dobrze przylegającej do podłoża, małej wartości rezystancji połączenia elektrody przedniej z podłożem. Możliwość estymacji rezystancji przedniej metalizacji jest wartościowa dla producentów i konstruktorów. Pozwala ona na dotrzymanie wymagań klienta i przynosi wymierne zyski.

8

Optimization of chemical composition and heat treatment condition of structural steel

Honysz R., Dobrzański L. A.

Journal of Achievements in Materials and Manufacturing Engineering

|

2014

|

Vol. 64, nr 1

27--33

EN

Purpose: This paper presents the methodology and results of virtual research project involving the optimization of the chemical composition and heat treatment conditions of structural steels. Investigations were performed in virtual environment with use of materials science virtual laboratory. Design/methodology/approach: The first task was to search for such a range of chosen element concentration while keeping the concentration of other elements unchanged in order to satisfy all the conditions for steel mechanical properties defined by a virtual client. Second task of virtual research project consisted in searching for such ranges of temperature and time for hardening and tempering , to ensure that all the conditions for steel properties defined by a virtual client has been met without making changes in the chemical composition of steel. Findings: Virtual investigations results were verified in real investigative laboratory. Results of virtual examinations are presented as raw data and influence charts. Practical implications: The new material design methodology has practical application in the development of materials and modelling of steel descriptors in aim to improve the mechanical properties and specific applications in the production of steel. Presented examples of computer aid in structural steel production shows a potential application possibility of this methodology to support the production of any group of engineering materials. Originality/value: The prediction possibility of the material mechanical properties is valuable for manufacturers and constructors. It ensures the customers quality requirements and brings also measurable financial advantages.

9

Wirtualne symulacje rzeczywistego sprzętu badawczego jako przykład zastosowania wirtualnej rzeczywistości w inżynierii materiałowej

Dobrzański L. A., Honysz R.

Przetwórstwo Tworzyw

|

2013

|

[R.] 19, nr 4 (154)

331--335

PL

Nieustanny rozwój technologii komputerowej i obserwowany wzrost mocy obliczeniowej komputerów otwiera przed inżynierami nowe obszary komputerowej aktywności naukowej. Wirtualną rzeczywistość można skutecznie przystosować do procesów kształcenia i podnoszenia kwalifikacji. Celom tym ma służyć niżej opisana koncepcja wirtualnego laboratorium inżynierii materiałowej. Mimo znacznych problemów z jej opracowaniem, jest to rozwiązanie przyszłościowe dla ośrodków o ograniczonych zasobach sprzętowych. Jako przykład przedstawiono wybrane symulacje rzeczywistego sprzętu badawczego użytego do prowadzenia badań materiałów polimerowych i kompozytowych.

EN

Continuous development of computer technology and the observed increase in computing power opens forengineers’ new areas of scientific activity. Virtual reality can be successfully adapted to education processes of education and upgrading of skills. For these purposes described below material science virtual laboratory concept was developed. Despite significant problems with its development, it is a future-oriented solution for centres with limited hardware resources. As an example, selected simulations of the real research equipment used for testing of polymeric materials and composites materials are presented.

10

Influence analysis of admixtures on mechanical properties of non-alloy structural steels

Honysz R., Dobrzański L.A.

Journal of Achievements in Materials and Manufacturing Engineering

|

2013

|

Vol. 61, nr 2

336--342

EN

Purpose: The paper introduces analysis results of selected alloying elements influence on mechanical properties of alloy structural steels for quenching and tempering. Design/methodology/approach: Investigations were performed in virtual environment with use of materials science virtual laboratory. Virtual investigations results were verified in real investigative laboratory. Findings: Materials researches performed with use of material science virtual laboratory in range of determining the mechanical properties are consistent with the results obtained during the real research in real laboratory. Practical implications: Development of virtual tools, which are simulating the investigative equipment and simulating the research methodology, can serve as a basis for combining aspects of laboratory research, simulation, measurement, and education. Application of these tools will allow the transfer of research and teaching procedures from real laboratory to virtual environment. This will increase the number of experiments conducted in virtual environment and thus, it will increase the efficiency of such researches. Originality/value: Modelling of structural steels mechanical properties is valuable for steel designers and manufacturers, because it is associated with financial benefits, when expensive and time-consuming researches are reduced to necessary minimum.

11

Heat treatment influence on mechanical properties of structural steels for quenching and tempering

Dobrzański L. A., Honysz R.

Journal of Achievements in Materials and Manufacturing Engineering

|

2012

|

Vol. 55, nr 2

461--468

EN

Purpose: This paper introduces analysis results of heat treatment conditions influence on mechanical properties of alloy structural steels for quenching and tempering. Design/methodology/approach: Investigations were performed in virtual environment with use of materials science virtual laboratory. Virtual investigations results were verified in real investigative laboratory. Findings: Performed verification investigations presented in this paper on selected mechanical properties modelling examples of structural steels clearly show the correctness of the developed computational model of structural steel and confirm the possibility of its use in the industrial production, both to predict the properties, as well as to design new types of steel. Practical implications: Results of virtual examinations can be presented as raw data or influence charts Originality/value: The effectiveness of the virtual environment application for the prediction, simulation and modelling of the steel properties is presented.

12

Informative technologies in the material products designing

Dobrzański L., Honysz R.

Archives of Materials Science and Engineering

|

2012

|

Vol. 55, nr 1

37-44

EN

Purpose: The purpose of materials products designing is to optimize their functional properties in terms of technological, economic and ecological aspects. Design/methodology/approach: Materials science is an example of a field, in which informative technologies used to understand and anticipate the construction of materials and their properties has a significant success. Findings: Innovation and development of new informative technologies and the widespread use of modern materials will be essential for promoting economic development in the near future by application of entirely new, interdisciplinary field of science: computational materials science. Practical implications: The use of informative technologies allows exploring in a short time and at low expense, many solutions for the design of the mechanical properties of materials and their simulation beyond the standardized range. Originality/value: The most important benefit of material designing is the ability of suitable selection of material (or its manufacturing) for various applications with use of informative technologies.

13

Application of artificial intelligence methods in PVD and CVD coatings properties modelling

Dobrzański L.A., Staszuk M., Honysz R.

Archives of Materials Science and Engineering

|

2012

|

Vol. 58, nr 2

152--157

EN

Purpose: The aim of this paper is the presentation of developed computational model build with use of artificial neural networks. This model describes the influence of PVD and CVD coatings properties on the cutting edge durability from sialon tool ceramics covered with these layers. Design/methodology/approach: Obtained model has the ability to compute the durability of the PVD and CVD coatings coated on sialon tool ceramics blades determined in technological cutting trials of grey cast iron, basing on PVD and CVD coatings microhardness, thickness, grain size and their adhesion to the substrate. Findings: Results of researches, performed with use of computational model, revealed, that the greatest influence on the durability of coated sialon tool ceramics blades have the adhesion to the substrate. Smaller influence on blades durability has the size of grains. Minor influence on the cutting tool from other properties was obtained. Practical implications: Achieved results indicates, that the best coating’s adhesion to the substrate for coating material selection and design of PVD and VD coatings deposition process should have priority in implementation. Originality/value: Obtainment and utilisation of computational model builded with use of artificial intelligence methods

14

Artificial intelligence and virtual environment application for materials design methodology

Dobrzański L.A., Honysz R.

Journal of Machine Engineering

|

2011

|

Vol. 11, No. 1-2

102--119

EN

The purpose of this study is to develop a methodology for material design. This methogology will enabling the selection of production descriptors to ensure the required mechanical properties of structural steels specified by the designer of machinery and equipment. The selection is performed by using a computational model developed with use of artificial intelligence methods and virtual environment. The model is designed to provide impact examinations of these factors on the mechanical properties of steel only in the computing environment. Virtual computing environment allows full usage of the developed intelligent model of non-alloy and alloy structural steel properties and provides an easy, intuitive and user-friendly way to designate these properties for products after heat and plastic treatment. Also, very easy is the determination of chemical composition, treatment conditions and geometric dimensions on the basis of the steels mechanical properties. The proposed solutions allow the usage of developed virtual environment as a new medium in both, the scientific work performed remotely, as well as in education during classes. It is also possible the extension of this model to other groups of materials, not just for steel.

15

Virtual examinations of alloying elements influence on alloy structural steels mechanical properties

Dobrzański L. A., Honysz R.

Journal of Achievements in Materials and Manufacturing Engineering

|

2011

|

Vol. 49, nr 2

251--258

EN

Purpose: The paper introduces analysis results of selected alloying elements influence on mechanical properties of alloy structural steels for quenching and tempering. Design/methodology/approach: Investigations were performed in virtual environment with use of materials science virtual laboratory. Virtual investigations results were verified in real investigative laboratory. Findings: Materials researches performed with use of material science virtual laboratory in range of determining the mechanical properties are consistent with the results obtained during the real research in real laboratory. Practical implications: Development of virtual tools, which are simulating the investigative equipment and simulating the research methodology, can serve as a basis for combining aspects of laboratory research, simulation, measurement, and education. Application of these tools will allow the transfer of research and teaching procedures from real laboratory to virtual environment. This will increase the number of experiments conducted in virtual environment and thus, it will increase the efficiency of such researches. Originality/value: Modelling of structural steels mechanical properties is valuable for steel designers and manufacturers, because it is associated with financial benefits, when expensive and time-consuming researches are reduced to necessary minimum.

16

Analysis of alloying elements influence on properties of structural steel with use of material science virtual laboratory

Dobrzański L., Honysz R.

Czasopismo Techniczne. Mechanika

|

2011

|

R. 108, z. 4-M/1

65-72

EN

The paper introduces analysis results of selected alloying elements influence on mechanical properties of alloy structural steels for quenching and tempering. Investigations were performed in virtual environment with use of materials science virtual laboratory. Virtual investigations results were verified in real investigative laboratory.

PL

W artykule przedstawiono przykładowe wyniki analizy wpływu wybranych pierwiastków stopowych na własności mechaniczne stopowych stali konstrukcyjnych do ulepszania cieplnego. Badania zostały wykonane w przestrzeni wirtualnej z wykorzystaniem wirtualnego laboratorium inżynierii materiałowej. Wyniki wirtualnych badań zweryfikowano w rzeczywistym laboratorium.

17

The idea of material science virtual laboratory

Dobrzański L. A., Honysz R.

Journal of Achievements in Materials and Manufacturing Engineering

|

2010

|

Vol. 42, nr 1-2

196-203

EN

Purpose: This article was written to describe the Material Science Virtual Laboratory. Presented laboratory is an open scientific, investigative, simulating and didactic medium helpful in the realisation of the scientific and didactic tasks in the field of material Science. This laboratory is implemented in the Institute of Engineering Materials and Biomaterials of Silesian University of Technology in Gliwice, Poland. Design/methodology/approach: The laboratory is an aggregate of testers and training simulators, placed in the virtual reality and created in various languages and the programming techniques, which represents the properties, functionality and manual principles of real equipment installed and accessible in the real laboratories of scientific universities. Findings: Application of the equipment, that is practically imperishable, cheap in exploitation and easy in the use encourages students and scientific workers to independent audits and experiments in situations, where the possibilities of their execution in the real investigative laboratory will be limited because of the high material costs, difficult access to real equipment or the possible risk of his damage. Practical implications: The use possibilities of the virtual laboratory are practically unrestricted; it can be a base for any studies, course or training programme. Originality/value: The project of the virtual laboratory corresponds with the global tendency for expand the investigative and academic centres about the possibilities of training and experiments performance with use of the virtual reality. This enriches investigation and education programmes of the new abilities reserved so far exclusively for effecting only on real equipment

18

Application of artificial neural networks in modelling of quenched and tempered structural steels mechanical properties

Dobrzański L. A., Honysz R.

Journal of Achievements in Materials and Manufacturing Engineering

|

2010

|

Vol. 40, nr 1

50-57

EN

Purpose: This paper presents the application of artificial neural networks for mechanical properties prediction of structuralal steels after quenching and tempering processes. Design/methodology/approach: On the basis of input parameters, which are chemical composition, parameters of mechanical and heat treatment and dimensions of elements, steels’ mechanical properties : yield stress, tensile strength stress, elongation, area reduction, impact strength and hardness are predicted. Findings: Results obtained in the given ranges of input parameters indicates on very good ability of artificial neural networks to values prediction of described mechanical properties for steels after quenching and tempering processes. The uniform distribution of descriptive vectors in all, training, validation and testing sets, indicates on good ability of the networks to results generalisation. Practical implications: Artificial neural networks, created during modelling, allows easy prediction of steels properties and allows the better selection of both chemical composition and the processing parameters of investigated materials. It’s possible to obtain steels, which are qualitatively better, cheaper and more optimised under customers needs. Originality/value: The prediction possibility of the material mechanical properties is valuable for manufacturers and constructors. It allows the preservation of customers quality requirements and brings also measurable financial advantages

19

Artificial intelligence and virtual environment application for materials design methodology

Dobrzański L. A., Honysz R.

Archives of Materials Science and Engineering

|

2010

|

Vol. 45, nr 2

69-105

EN

Purpose: The purpose of this study is to develop a methodology for material design, enabling the selection of the chemical elements concentration, heat and plastic treatment conditions and geometrical dimensions to ensure the required mechanical properties of structural steels specified by the designer of machinery and equipment as the basis for the design of material components manufactured from these steels, by using a computational model developed with use of artificial intelligence methods and virtual environment. The model is designed to provide impact examinations of these factors on the mechanical properties of steel only in the computing environment. Design/methodology/approach: A virtual research environment built with use of computational model describing relationships between chemical composition, heat and plastic treatment conditions and product geometric dimensions and mechanical properties of the examined group of steel was developed and practical applied. This model enables the design of new structural steel by setting the values of mechanical properties based on material production descriptors and allows the selection of production descriptors on the basis of the mechanical properties without the need for additional tests or experimental studies in reality. Findings: Virtual computing environment allows full usage of the developed intelligent model of non-alloy and alloy structural steel properties and provides an easy, intuitive and user-friendly way to designate manufacturing descriptors and mechanical properties for products. Research limitations/implications:The proposed solutions allow the usage of developed virtual environment as a new medium in both, the scientific work performed remotely, as well as in education during classes. Practical implications: The new material design methodology has practical application in the development of materials and modelling of steel descriptors in aim to improve the mechanical properties and specific applications in the production of steel. Presented examples of computer aid in structural steel production showing a potential application possibility of this methodology to support the production of any group of engineering materials. Originality/value: The prediction possibility of the material mechanical properties is valuable for manufacturers and constructors. It ensures the customers quality requirements and brings also measurable financial advantages.

20

Artificial intelligence and virtual environment application for materials design methodology

Dobrzański L.A., Honysz R.

Archives of Computational Materials Science and Surface Engineering

|

2010

|

Vol. 2, nr 4

201-212

EN

Purpose: The purpose of this study is to develop a methodology for material design, enabling the selection of the chemical elements concentration, heat and plastic treatment conditions and geometrical dimensions to ensure the required mechanical properties of structural steels specified by the designer of machinery and equipment as the basis for the design of material components manufactured from these steels, by using a computational model developed with use of artificial intelligence methods and virtual environment. The model is designed to provide impact examinations of these factors on the mechanical properties of steel only in the computing environment. Design/methodology/approach: A virtual research environment built with use of computational model describing relationships between chemical composition, heat and plastic treatment conditions, product geometric dimensions and mechanical properties of the examined group of steels was developed and practical applied. This model enables the design of new structural steel by setting the values of mechanical properties based on material production descriptors and allows the selection of production descriptors on the basis of the mechanical properties without the need for additional tests or experimental studies in reality. Findings: Virtual computing environment allows full usage of the developed intelligent model of non-alloy and alloy structural steel properties and provides an easy, intuitive and user-friendly way to designate manufacturing descriptors and mechanical properties for products. Research limitations/implications:The proposed solutions allow the usage of developed virtual environment as a new medium in both, the scientific work performed remotely, as well as in education during classes. Practical implications: The new material design methodology has practical application in the development of materials and modelling of steel descriptors in aim to improve the mechanical properties and specific applications in the production of steel. Presented examples of computer aid in structural steel production showing a potential application possibility of this methodology to support the production of any group of engineering materials. Originality/value: The prediction possibility of the material mechanical properties is valuable for manufacturers and constructors. It ensures the customers quality requirements and brings also measurable financial advantages.