| | SLO | ENG | Piškotki in zasebnost

Večja pisava | Manjša pisava

Iskanje po katalogu digitalne knjižnice Pomoč

Iskalni niz: išči po
išči po
išči po
išči po
* po starem in bolonjskem študiju

Opcije:
  Ponastavi


1 - 10 / 18
Na začetekNa prejšnjo stran12Na naslednjo stranNa konec
1.
New computational models for better predictions of the soil-compression index
Ahmet Demir, 2015, izvirni znanstveni članek

Opis: The compression index is one of the important soil parameters that are essential for geotechnical designs. Because laboratory and in-situ tests for determining the compression index (Cc) value are laborious, time consuming and costly, empirical formulas based on soil parameters are commonly used. Over the years a number of empirical formulas have been proposed to relate the compressibility to other soil parameters, such as the natural water content, the liquid limit, the plasticity index, the specific gravity. These empirical formulas provide good results for a specific test set, but cannot accurately or reliably predict the compression index from various test sets. The other disadvantage is that they tend to use a single parameter to estimate the compression index (Cc), even though Cc exhibits spatial characteristics depending on several soil parameters. This study presents the potential for Genetic Expression Programming (GEP) and the Adaptive Neuro-Fuzzy (ANFIS) computing paradigm to predict the compression index from soil parameters such as the natural water content, the liquid limit, the plastic index, the specific gravity and the void ratio. A total of 299 data sets collected from the literature were used to develop the models. The performance of the models was comprehensively evaluated using several statistical verification tools. The predicted results showed that the GEP and ANFIS models provided fairly promising approaches to the prediction of the compression index of soils and could provide a better performance than the empirical formulas.
Ključne besede: compression index, statistical analysis, genetic expression programming, adaptive neuro-fuzzy, empirical equations
Objavljeno: 14.06.2018; Ogledov: 630; Prenosov: 45
.pdf Celotno besedilo (274,65 KB)
Gradivo ima več datotek! Več...

2.
Automated and intelligent programming of cnc machine tools
Afrim Gjelaj, 2014, doktorska disertacija

Opis: Nowadays, many scientists focus on increasing the level of automation, respectively flexibility in manufacturing systems. In addition, automated programming of CNC machine tools has reached a high level of machining operations. However, it is still impossible for a machine to manipulate completely in an autonomous way. Special attention in this doctoral thesis is focused on the automated programming of CNC machine tools regarding artificial intelligence. The purpose of automated programming is to improve quality and to fulfil the requirements of manufacturing industry and provide commercial solutions. This thesis also provides a description of artificial intelligence usage in order to solve optimal tool path-length and tool selection, as well as the preparation of planned technology. Firstly, the automated programming of CNC machine tools enjoys great success when applying artificial intelligence in regard to the machining processes. Choices of path length and tool selection are analysed in great detail in order to ascertain the optimal problems of tool path- length and tool selection. However, in order to achieve automated and intelligent CNC programming of machine tools, their flexibilities are of major importance. Automation today tends to improve and implement manufacturing flexibility at a strategic level. This means increasing the degree of flexibility whilst at the same time increasing the degree of automation regarding CNC machine tools. In addition to the above-mentioned investigated problems, the influences of cutting force (Fc), power cutting (Pc), tool life (T) and surface roughness (Ra) as functions of tool path- length are also analysed. Analytical and mathematical models are optimised using a multi-objective genetic algorithm (MOGA). MOGA enables optimisation by employing two or more equations simultaneously. Another problem for the automated and intelligent CNC programming of machine tools focuses on the application of Discrete Systems (DS). The discrete system in our work focuses on analysing cutting force (Fc) in regard to the turning operation.
Ključne besede: inteligent CNC programming, intelligent manufacturing, discrete system, automated programming, multiobjective genetic algorithm MOGA
Objavljeno: 23.01.2015; Ogledov: 2417; Prenosov: 354
.pdf Celotno besedilo (1,57 MB)

3.
Modeling of forming efficiency using genetic programming
Miran Brezočnik, Jože Balič, Zlatko Kampuš, 2001, izvirni znanstveni članek

Opis: This paper proposes new approach for modeling of various processes in metal-forming industry. As an example, we demonstrate the use of genetic programming (GP) for modeling of forming efficiency. The forming efficiency is a basis for determination of yield stress which is the fundamental characteristic of metallic materials. Several different genetically evolved models for forming efficiency on the basis of experimental data for learning were discovered. The obtained models (equations) differ in size, shape, complexity and precision of solutions. In one run out of many runs of our GP system the well-known equation of Siebel was obtained. This fact leads us to opinion that GP is a very powerful evolutionary optimization method appropriate not only for modeling of forming efficiency but also for modeling of many other processes in metal-forming industry.
Ključne besede: metal forming, yield stress, forming efficiency, mathematical modeling, adaptation, genetic methods, genetic algorithm, genetic programming, artificial intelligence, process optimisation
Objavljeno: 01.06.2012; Ogledov: 1462; Prenosov: 97
URL Povezava na celotno besedilo

4.
Predicting defibrillation success by "genetic" programming in patients with out-of-hospital cardiac arrest
Matej Podbregar, Miha Kovačič, Aleksandra Podbregar-Marš, Miran Brezočnik, 2003, izvirni znanstveni članek

Opis: In some patients with ventricular fibrillation (VF) there may be a better chance of successful defibrillation after a period of chest compression and ventilation before the defibrillation attempt. It is therefore important to know whether a defibrillation attempt will be successful. The predictive powerof a model developed by "genetic" programming (GP) to predict defibrillation success was studied. Methods and Results: 203 defibrillations were administered in 47 patients with out-of-hospital cardiac arrest due to a cardiac cause. Maximal amplitude, a total energy of power spectral density, and the Hurst exponent of the VF electrocardiogram (ECG) signal were included in the model developed by GP. Positive and negative likelihood ratios of the model for testing data were 35.5 and 0.00, respectively. Using a model developed by GP on the complete database, 120 of the 124 unsuccessful defibrillations would have been avoided, whereas all of the 79 successful defibrillations would have been administered. Conclusion: The VF ECG contains information predictive of defibrillation success. The model developed by GP, including data from the time-domain, frequency-domain and nonlinear dynamics, could reduce the incidence of unsuccessful defibrillations.
Ključne besede: optimisation methods, evolutionary optimisation methods, genetic algorithms, genetic programming, defibrillation, cardiac arrest prediction
Objavljeno: 01.06.2012; Ogledov: 1313; Prenosov: 77
URL Povezava na celotno besedilo

5.
A model of data flow in lower CIM levels
Igor Drstvenšek, Ivo Pahole, Jože Balič, 2004, izvirni znanstveni članek

Opis: After years of work in fields of computer-integrated manufacturing (CIM), flexible manufacturing systems (FMS), and evolutionary optimisation techniques, several models of production automation were developed in our laboratories. The last model pools the discoveries that proved their effectiveness in the past models. It is based on the idea of five levels CIM hierarchy where the technological database (TDB) represents a backbone of the system. Further on the idea of work operation determination by an analyse of the production system is taken out of a model for FMS control system, and finally the approach to the optimisation of production is supported by the results of evolutionary based techniques such as genetic algorithms and genetic programming.
Ključne besede: computer integrated manufacturing, flexible manufacturing systems, evolutionary optimisation techniques, production automation, CIM hierarchy, technological databases, production optimisation, genetic algorithms, genetic programming
Objavljeno: 01.06.2012; Ogledov: 1515; Prenosov: 79
URL Povezava na celotno besedilo

6.
Prediction of surface roughness with genetic programming
Miran Brezočnik, Miha Kovačič, Mirko Ficko, 2004, izvirni znanstveni članek

Opis: In this paper we propose genetic programming to predict surface roughness in end-milling. Two independent data sets were obtained on the basis of measurement: training data set and testing data set. Spindle speed, feed rate,depth of cut, and vibrations are used as independent input variables (parameters), while surface roughness as dependent output variable. On the basis of training data set, different models for surface roughness were developed by genetic programming. Accuracy of the best model was proved with the testing data. It was established that the surface roughness is most influenced by the feed rate, whereas the vibrations increase the prediction accuracy.
Ključne besede: end milling, surface roughness, prediction of surface roughness, genetic programming
Objavljeno: 01.06.2012; Ogledov: 1369; Prenosov: 102
URL Povezava na celotno besedilo

7.
Evolutionary approach for cutting forces prediction in milling
Miha Kovačič, Jože Balič, Miran Brezočnik, 2004, izvirni znanstveni članek

Opis: Knowing cutting forces is important for choosing cutting parameters for milling. Traditionally, cutting forces are calculated by equation which includes empirically measured specific cutting forces. In the article modelling of cutting forces with genetic programming is proposed, which imitates principles of living beings. Measurements have been made for two materials (aluminium alloy AlMgSi1 and steel 1.2343) and two different types of milling (conventional milling and STEP milling). For each material and type of milling parameters, tensile strength and hardness of workpiece, tool diameter, cutting depth, spindle speed, feeding and type of milling were monitored, and for each combination of milling parameters cutting forces were measured. On the basis of the experimental data, different models for cutting forces prediction were obtained by genetic programming. Research shows that genetically developed models fit the experimental data.
Ključne besede: milling, simulation, milling cutting forces prediction, genetic programming
Objavljeno: 01.06.2012; Ogledov: 993; Prenosov: 63
URL Povezava na celotno besedilo

8.
Predicting stress distribution in cold-formed material with genetic programming
Miran Brezočnik, Leo Gusel, 2004, izvirni znanstveni članek

Opis: In this paper we propose a genetic programming approach to predict radial stress distribution in cold-formed material. As an example, cylindrical specimens of copper alloy were forward extruded and analysed by the visioplasticity method. They were extruded with different coefficients of friction. The values of three independent variables (i.e., radial and axial position of measured stress node, and coefficient of friction) were collected after each extrusion. These variables influence the value of the dependent variable, i.e., radial stress. On the basis of training data set, various different prediction models for radial stress distribution were developed during simulated evolution. Accuracy of the best models was proved with the testing data set. The research showed that by proposed approach the precise prediction models can be developed; therefore, it is widely used also in other areas in metal-forming industry, where the experimental data on the process are known.
Ključne besede: metal forming, stress distribution, prediction, genetic programming, modelling
Objavljeno: 01.06.2012; Ogledov: 1467; Prenosov: 79
URL Povezava na celotno besedilo

9.
Genetic programming approach for the material flow curve determination of copper alloy - CuCrZr
Leo Gusel, Miran Brezočnik, Rebeka Rudolf, Ivan Anžel, Z. Lazarević, Nebojša Romčević, 2010, izvirni znanstveni članek

Opis: For the control of the forming process it is necessary to know as precisely as possible the flow curve of the formed material. The paper presents the determination of the equation for the flow curve of cooper alloy (CuCrZr) with artificial intelligence approach. The genetic programming method (GP) was used. It is an evolutionary optimization technique based on the Darwinist principles of the evolution of species and the survival of the fittest organisms. The main characteristic of GP is its non- deterministic way of computing. It is probably the most general approach out of evolutionary computation methods. The comparison between the experimental results, analytical solution and the solution obtained genetically clearly shows that the genetic programming method is a very promising approach.
Ključne besede: forming, flow curve, copper alloys, genetic programming
Objavljeno: 01.06.2012; Ogledov: 1135; Prenosov: 18
URL Povezava na celotno besedilo

10.
Evolutionary programming of CNC machines
Miha Kovačič, Miran Brezočnik, Ivo Pahole, Jože Balič, Borut Kecelj, 2005, izvirni znanstveni članek

Opis: The paper proposes a new concept for programming of CNC machines. The concept based on genetic algorithms assures evolutionary generation and optimization of NC programs on the basis of CAD models of manufacturing environment. The structure, undergoing simulated evolution, is the population of NC programs. The NC programs control the machine which performs simple elementary motions. During the evolution the machine movement becomes more and more complex and intelligent solutions emerge gradually as a result of the interaction between machine movements and manufacturing environment. The examples of evolutionary programming of CNC lathe and CNC milling machine tool for different complexities of the blanks and products are presented. The proposed concept showed a high degree of universality, efficiency, and reliability and it can be also simply adopted to other CNC machines.
Ključne besede: manufacturing systems, NC-programming, CNC lathes, simulated evolution, genetic algorithms
Objavljeno: 01.06.2012; Ogledov: 1253; Prenosov: 89
URL Povezava na celotno besedilo

Iskanje izvedeno v 0.16 sek.
Na vrh
Logotipi partnerjev Univerza v Mariboru Univerza v Ljubljani Univerza na Primorskem Univerza v Novi Gorici