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1

Modelowanie ruchu niejednorodnych brył z wykorzystaniem metod sztucznej inteligencji

Rykała Ł.

Przegląd Mechaniczny

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2018

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nr 5

34--39

PL

W artykule zaprezentowano problem modelowania ruchu niejednorodnych brył poruszających się po równi pochyłej za pomocą równań Lagrange'a II rodzaju z mnożnikami. Zastosowanie wymienionego formalizmu pozwoliło określić przebiegi nieznanych mnożników Lagrange’a, które następnie aproksymowano z wykorzystaniem sztucznych sieci neuronowych. Przedstawiono również wyniki badań numerycznych analizowanego układu.

EN

The article presents the problem of modeling the movement of non-homogeneous solids moving along an inclined plane using Lagrange equations of the second kind with multipliers. The use of the mentioned formalism allows to obtain the courses of unknown Lagrange multipliers, which were then approximated using artificial neural networks. The numerical results of the analyzed system are also presented.

2

Methodology of high-speed steels design using the artificial intelligence tools

Sitek W.

Journal of Achievements in Materials and Manufacturing Engineering

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2010

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Vol. 39, nr 2

115-160

EN

Purpose: The main goal of the research carried out was developing the design methodology for the new high-speed steels with the required properties, including hardness and fracture toughness, as the main properties guaranteeing the high durability and quality of tools made from them. It was decided that hardness and fracture toughness KIc are the criteria used during the high-speed steels design. Design/methodology/approach: In case of hardness, the statistical and neural network models were developed making computation possible of the high-speed steel hardness based solely on the steel chemical composition and its heat treatment parameters, i.e., austenitizing- and tempering temperatures. In this case results of own work on the effect of alloy elements on the secondary hardness effect were used, as well as data contained in catalogues and pertinent standards regarding the high-speed steels. In the second case - high-speed steels fracture toughness, the neural network model was developed, making it possible to compute the KIc factor based on the steel chemical composition and its heat treatment parameters. The developed material models were used for designing the chemical compositions if the new high-speed steel, demonstrating the desired properties, i.e., hardness and fracture toughness. Methodology was developed to this end, employing the evolutionary algorithms, multicriteria optimisation of the high-speed steels chemical composition. Findings: Results of the research carried out confirmed the assumption that using the data from catalogues and from standards is possible, which - would supplement the set of data indispensable to develop the assumed model - improving in this way its adequacy and versatility. Practical implications: Solutions presented in the work, based on using the adequate material models may feature an interesting alternative in designing of the new materials with the required properties. The practical aspect has to be noted, resulting form the developed models, which may successfully replace the above mentioned technological investigations, consisting in one time selection of the chemical composition and heat treatment parameters and experimental verification of the newly developed materials to check of its properties meet the requirements. Originality/value: The presented approach to new materials design, being the new materials design philosophy, assumes the maximum possible limitation of carrying out the indispensable experiments, to take advantage of the existing experimental knowledge resources in the form of databases and most effective computer science tools, including neural networks and evolutionary algorithms. It should be indicated that the materials science knowledge, pertaining oftentimes to the multi-aspect classic problems and described, or - rather - saved in the existing, broadly speaking, databases, features the invaluable source of information which may be used for discovery of the unknown so far relationships describing the material structure - properties relations.

3

Artificial intelligence-based control system for the analysis of metal casting properties

Mares E., Sokolowski J. H.

Journal of Achievements in Materials and Manufacturing Engineering

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2010

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Vol. 40, nr 2

149-154

EN

Purpose: The metal casting process requires testing equipment that along with customized computer software properly supports the analysis of casting component characteristic properties. Due to the fact that this evaluation process involves the control of complex and multi-variable melting, casting and solidification factors, it is necessary to develop dedicated software. Design/methodology/approach: The integration of Statistical Process Control methods and Artificial Intelligence techniques (Case-Based Reasoning) into Thermal Analysis Data Acquisition Software (NI LabView) was developed to analyze casting component properties. The thermal data was tested in terms of accuracy, reliability and timeliness in order to secure metal casting process effectiveness. Findings: Quantitative values were defined as “Low”, “Medium” and “High” to assess the level of improvement in the metal casting analysis by means of the Artificial Intelligence-Based Control System (AIBCS). The traditional process was used as a reference to measure such improvement. As a result, the accuracy, reliability and timeliness were significantly increased to the “High” level. Research limitations/implications: Presently, the AIBCS predicts a limited number of casting properties. Due to its flexible design more properties could be added. Practical implications: The AIBCS has been successfully used at the Ford/Nemak Windsor Aluminum Plant (WAP) to analyze Al casting properties of the engine blocks. Originality/value: The metal casting research community has immensely benefited from these developed information technologies that support the metal casting process.

4

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

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2010

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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

5

Application of artificial neural networks for modelling correlations in age hardenable aluminium alloys

Musharavati F., Hamouda A. S. M.

Journal of Achievements in Materials and Manufacturing Engineering

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2010

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Vol. 41, nr 1-2

140-146

EN

Purpose: This paper discusses some of the preliminary results of an ongoing research on the applications of artificial neural networks (ANNs) in modelling, predicting and simulating correlations between mechanical properties of age hardenable aluminium alloys as a function of alloy composition. Design/methodology/approach: Appropriate combinations of inputs and outputs were selected for neural network modelling. Multilayer feedforward networks were created and trained using datasets from public literature. Influences of alloying elements, alloy composition and processing parameters on mechanical properties of aluminium alloys were predicted and simulated using ANNs models.Two sample t-tests were used to analyze the prediction accuracy of the trained ANNs. Findings: Good performances of the neural network models were achieved. The models were able to predict mechanical properties within acceptable margins of error and were able to provide relevant simulated data for correlating alloy composition and processing parameters with mechanical properties. Therefore, ANNs models are convenient and powerful tools that can provide useful information which can be used to identify desired properties in new aluminium alloys for practical applications in new and/or improved aluminium products. Research limitations/implications: Few public data bases are available for modelling properties. Minor contradictions on the experimental values of properties and alloy compositions were also observed. Future work will include further development of simulated data into property charts. Practical implications: Correlations between mechanical properties and alloy compositions can help in identifying a suitable alloy for a new or improved aluminum product application. In addition, availability of simulated structure-process-property data or charts assists in reducing the time and costs of trial and error experimental approaches by providing near-optimal values that can be used as starting point in experimental work. Originality/value: Since the simulated data provides near-optimal values, manufacturers of new and/or improved aluminum alloys can use the simulated data as guidelines for narrowing down extensive experimental work. This in turn reduces the process design cycle times. Designers of new and/or improved aluminum products can also use the simulated data as a guideline for correlating property-application information, which is useful in preliminary design phase.

6

Reconstruction of the heat transfer coefficient on the grounds of experimental data

Słota D.

Journal of Achievements in Materials and Manufacturing Engineering

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2009

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Vol. 34, nr 1

63-70

EN

Purpose: Solidification of pure metal can be modelled by a two-phase Stefan problem, in which the distribution of temperature in the solid and liquid phases is described by the heat conduction equation with initial and boundary conditions. The inverse Stefan problem can be applied to solve design problems in casting process. Design/methodology/approach: In numerical calculations the alternating phase truncation method, the Tikhonov regularization and the genetic algorithm were used. The featured examples of calculations show a very good approximation of the experimental data. Findings: The verification of the method of reconstructing the cooling conditions during the solidification of pure metals. The solution of the problem consists of selecting the heat transfer coefficient on the boundary, so that the temperature in selected points on the boundary of the domain assumes given values. Research limitations/implications: The method requires that it must be possible to describe the sought boundary condition by means of a finite number of parameters. It is not necessary, that the sought boundary condition should be linearly dependent on those parameters. Practical implications: The presented method can be easy applied to solve design problems of different types, e.g. for the design of continuous casting installations (incl. the selection of the length of secondary cooling zones, the number of jets installed in individual zones, etc.). Originality/value: Verification, on the grounds of experimental data, the formerly devised method of determining the heat transfer coefficient during the solidification of pure metals.

7

Computer modelling system of the chemical composition and treatment parameters influence on mechanical properties of structural steels

Dobrzański L. A., Honysz R.

Journal of Achievements in Materials and Manufacturing Engineering

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2009

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Vol. 35, nr 2

138-145

EN

Purpose: This paper presents Neuro-Lab. It is an authorship programme, which use algorithms of artificial intelligence for structural steels mechanical properties estimation. Design/methodology/approach: On the basis of chemical composition, parameters of heat and mechanical treatment and elements of geometrical shape and size this programme has the ability to calculate the mechanical properties of examined steel and introduce them as raw numeric data or in graphic as influence charts. Possible is also to examine the dependence among the selected steel property and chosen input parameters, which describes this property. Findings: There is no necessity of carrying out any additional material tests. The results correlations between calculated and measured values are very good and achieve even the level of 98%. Practical implications: Presented programme can be an effective replace of the real experimental methods of properties determination in laboratory examinations. It can be applied as the enlargement of experimental work. Possible is also the investigation of models coming from new steel species, that wasn’t produced yet. Originality/value: The ability of the mechanical properties estimation of the ready, or foreseen to the use, material is unusually valuable for manufacturers and constructors. This signifies the fulfilment of customer’s quality requirements as well as measurable financial advantages for material manufacturers.

8

Klasyfikator lokalnych uszkodzeń zębów kół przekładni, wykorzystujący logikę rozmytą oraz selekcję widmową

Czech P.

Problemy Eksploatacji

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2008

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nr 1

55-73

PL

W artykule przedstawiono wyniki prób mających na celu budowę klasyfikatora lokalnych uszkodzeń zębów kół przekładni, zbudowanego w oparciu o logikę rozmytą. Obiekt badań stanowiła przekładnia zębata o zębach prostych, pracująca na stanowisku mocy krążącej FZG. Badaniami objęto przekładnie z kołami bez uszkodzeń oraz z lokalnymi uszkodzeniami zębów w postaci pęknięcia u podstawy zęba i wykruszenia wierzchołka zęba. W artykule zaproponowano sposób budowy systemów diagnozujących lokalne uszkodzenia zębów kół. Do tego celu wykorzystano sygnały drganiowe poddane odpowiedniej filtracji oraz przetwarzaniu.

EN

The present paper presents the results of an experimental application of a fuzzy logic system as a classifier of the degree of cracking root and chipping tip of the tooth in a gear wheel. The input data for the classifier was in the form of a matrix composed of statistical measures, obtained from fast Fourier transform analysis. In order to create a foundation for knowledge, a stand testing was done. The experimental tests were conducted in the system operating as circulating power test rigs. As the result, the method of standard construction for diagnostic systems based on fuzzy logic was also worked out by means of defining the ways of filtrating and analysing of signals. Additionally, the procedure of building the fuzzy logic system used to classify the state of an object was researched.

9

Klasyfikator lokalnych uszkodzeń zębów kół przekładni, wykorzystujący sieci neuronowe MLP oraz ciągłą transformatę falkową

Czech P.

Problemy Eksploatacji

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2007

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nr 4

61-81

PL

W artykule przedstawiono wyniki prób mających na celu budowę klasyfikatora lokalnych uszkodzeń zębów kół przekładni, opartego na sztucznych sieciach neuronowych. W badaniach wykorzystano sieci neuronowe typu perceptron wielowarstwowy (MLP). Obiekt badań stanowiła przekładnia zębata o zębach prostych, pracująca na stanowisku mocy krążącej FZG. Badaniami objęto przekładnie z kołami bez uszkodzeń oraz z lokalnymi uszkodzeniami zębów w postaci pęknięcia u podstawy zęba i wykruszenia wierzchołka zęba. W artykule zaproponowano budowę deskryptorów lokalnych uszkodzeń zębów kół wykorzystując do tego celu sygnały drganiowe poddane odpowiedniej filtracji oraz przetwarzaniu.

EN

The paper presents the results of an experimental application of an artificial neural network as a classifier of the degree of the cracking root and the chipping tip of the tooth in a gear wheel. The neural classifier was based on the artificial neural network of an MLP type (Multi-Layer Perceptions). The input data for the classifier was in the form of a matrix composed of statistical measures, obtained from continuous wavelet analysis. In order to create a basis of knowledge, a stand testing was done. The experimental tests were conducted in the system operating as circulating power test rigs. As a result, the method of standard construction for diagnostic systems based on artificial intelligence was also worked out by means of defining the ways of filtrating and analysing of signals and diagnostic measurements. Additionally, the choice of the architecture and algorithm of teaching artificial neural networks used to classify the state of an object was researched.

10

Metody regulacji kąta przegłębienia pojazdu podwodnego transportującego ładunek

Szymak P.

Polish Hyperbaric Research

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2007

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Nr 1(18)

17-31

PL

W przypadku wykorzystania pojazdu podwodnego do przenoszenia różnego rodzaju ładunków można zaobserwować efekt niepożądanego przegłębienia robota, co ma niekorzystny wpływ na proces sterowania jego ruchem. W referacie przedstawiono wyniki działania konwencjonalnych i rozmytych regulatorów kata przegłębienia pojazdu podwodnego, dostrajanych przy wykorzystaniu metod klasycznych oraz metod sztucznej inteligencji. Prace realizowano w Instytucie Podstaw Techniki Wydziału Mechaniczno-Elektrycznego Akademii Marynarki Wojennej w Gdyni.

EN

In the case of using an underwater vehicle to transfer different kind of loads, an effect of undesirable robot's trim might be observed, which has disadvantageous influence on a control process of its movement. In the paper, results of action of conventional and fuzzy underwater vehicle's trim controllers, tuned with the assistance of classical and artificial intelligence methods have been presented. Researches were carried out in The Electrotechnical and Electronic Department, The Naval University in Gdynia.

11

Artificial Intelligence Approaches to Fault Diagnosis for Dynamic Systems

Patton R. J., Lopez-Toribio C. J., Uppal F. J.

International Journal of Applied Mathematics and Computer Science

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1999

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Vol. 9, no 3

471-518

EN

Recent approaches to fault detection and isolation (FDI) for dynamic systems using methods of integrating quantitative and qualitative model information, based upon artificial intelligence (AI) techniques are surveyed. In this study, the use of AI methods is considered an important extension to the quantitative model-based approach for residual generation in FDI. When quantitative models are not readily available, a correctly trained artificial neural network (ANN) can be used as a non-linear dynamic model of the system. However, the neural network does not easily provide insight into model behaviour; the model is explicit rather than implicit in form. This main difficulty can be overcome using qualitative modelling or rule-based inference methods. For example, fuzzy logic can be used together with state-space models or neural networks to enhance FDI diagnostic reasoning capabilities. The paper discusses the properties of several methods of combining quantitative and qualitative system information and their practical value for fault diagnosis of real process systems.