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Numerical analysis of the crack growth in a high loaded bolt connection
Marko Knez, Srečko Glodež, Janez Kramberger, 2007, izvirni znanstveni članek

Opis: The paper deals with the research on the crack growth in a bolt connection of a lug for crane counter weight bars. Counter weight bars are structural elements subjected to very heavy loads and therefore need special attention. The main purpose of this research is to determine the number of load cycles required for a crack to propagate from initial to criticakl crack length, whenthe final failure can be expected to occur. All required material parameters and experimental results were determined in our previous research. The influence of the initial crack siye upon the remaining life of the lug is reserched numericallu by means of FE analysis and analytically by use of the corrected analystical model.
Ključne besede: fracture mechanics, cyclic loading, fatigue crack growth, service life prediction, numerical analysis, bolt connection
Objavljeno: 31.05.2012; Ogledov: 1123; Prenosov: 4
URL Polno besedilo (0,00 KB)

The use of artificial neural networks for colour prediction in textile printing
Darko Golob, Jure Zupan, Đurđica Parac-Osterman, 2008, objavljeni znanstveni prispevek na konferenci

Opis: An attempt of using artificial neural networks for the prediction of dzes in textile printing paste preparation is presented. An existing collection of printed samples served as the basis for neural network training. It consists of 1340 samples printed using either a single dze or a combination of two dzes. First the proper combination of dzes was determined, because in most cases onlz two dzes are combined in the printing paste. Then the necessarz concentration of each dze was predicted. The reflectance value, and the colourvalues L*, a*, b* serve as input data and the known combination and concentrations of dzes for each sample were the targets. Some variations of neural network were tested, as well as various numbers of neurons in the hidden lazer. In addition, the influence of the training set organisation was examined, together with the number of learning epochs on the learning success.
Ključne besede: artificial neural networks, textile printing, colour recipe prediction
Objavljeno: 31.05.2012; Ogledov: 716; Prenosov: 6
URL Polno besedilo (0,00 KB)

Prediction of the ultraviolet protection of cotton woven fabrics dyed with reactive dystuffs
Polona Dobnik-Dubrovski, Miran Brezočnik, 2009, izvirni znanstveni članek

Opis: Textile materials provide a simple and convenient protection against UV radiation. To assign the degree of UV radiation protection of textile materials, the Ultraviolet Protection Factor (UPF) is commonly used. This paper reports the effect of woven fabric construction (yarn fineness, type of weave, relative fabric density), the colour of bi-functional reactive dyestuffs, and Cibacron dyed fabrics on the ultraviolet protection of light summer woven fabrics. A predictive model, determined by genetic programming, was derived to describe the influence of fabric construction. Warp and weft densities, weave factor and CIELab colour components were taken into account by developing the prediction model for UPF. The results show very good agreement between the experimental and predicted values.
Ključne besede: ultraviolet protection factor, woven fabric construction, colour, prediction model, genetic programming
Objavljeno: 31.05.2012; Ogledov: 890; Prenosov: 4
URL Polno besedilo (0,00 KB)

Prediction of total manufacturing costs for stamping tool on the basis of CAD-model of finished product
Mirko Ficko, Igor Drstvenšek, Miran Brezočnik, Jože Balič, Boštjan Vaupotič, 2005, izvirni znanstveni članek

Opis: One of the orientations of the tool-making industry is towards shortening the time from enquiry to the supply of tools. The tool-making shops must prepare within the shortest possible time an offer for the manufacturer of the tool based on the enquiry in the form of the CAD-model of the final product. For preparation of a proper offer, the values of certain technological features occurring in the manufacture of the tool are needed. Most frequently the tool manufacturer is interested in total cost for manufacture of the tool. Because of lack of time for making a detailed analysis the total costs of tool manufacture are predicted by the expert on the basis of the experience gathered during several years of work in this area. In our work, we conceived an intelligent system for predicting of total cost of the tool manufacture. We limited ourselves to tools for manufacture of sheet metal products by stamping; the system is based on the concept of case-based reasoning. On the basis of target and source cases, the system prepares the prediction of costs.The target case is the CAD-model in whose costs we are interested, whereas the source cases are the CAD-model of products, for which the tools had already been made, and the relevant total costs are known. The system first abstracts from CAD-models the geometrical features, and then it calculates the similarities between the source cases and target case. Then the most similar cases are used for preparation of prediction by genetic programming method. The genetic programming method provides the model connecting the individual geometrical features with total costs searched for. In the experimental work, we made a system adapted for predicting of tool costs used for tool manufacture on the basis of a theoretic model. The results show that the quality of predictions made by the intelligent system is comparable to the quality assured by the experienced expert.
Ključne besede: intelligent systems, prediction of costs, tool-making, stamping, CAD model, costs, genetic programming
Objavljeno: 01.06.2012; Ogledov: 719; Prenosov: 8
URL Polno besedilo (0,00 KB)

Prediction of maintenance of sinus rhythm after electrical cardioversion of atrial fibrillation by non-deterministic modelling
Petra Žohar, Miha Kovačič, Miran Brezočnik, Matej Podbregar, 2005, izvirni znanstveni članek

Opis: Atrial fibrillation (AF) is the most common rhythm disorder. Because of the high recurrence rate of AF after cardioversion and because of potential side effects of electrical cardioversion, it is clinically important to predict persistence of sinus rhythm after electrical cardioversion before it is attempted. The aim of our study was the development of a mathematical model by"genetic" programming (GP), a non-deterministic modelling technique, which would predict maintenance of sinus rhythm after electrical cardioversion of persistent AF. PATIENTS AND METHODS: Ninety-seven patients with persistent AF lasting more than 48 h, undergoing the first attempt at transthoracic cardioversion were included in this prospective study. Persistence of AF before the cardioversion attempt, amiodarone treatment, left atrial dimension,mean, standard deviation and approximate entropy of ECG R-R intervals were collected. The data of 53 patients were randomly selected from the database and used for GP modelling; the other 44 data sets were used for model testing. RESULTS: In 23 patients sinus rhythm persisted at 3 months. In the other 21 patients sinus rhythm was not achieved or its duration was less than 3 months. The model developed by GP failed to predict maintenance ofsinus rhythm at 3 months in one patient and in six patients falsely predicted maintenance of sinus rhythm. Positive and negative likelihood ratiosof the model for testing data were 4.32 and 0.05, respectively. Using this model 15 of 21 (71.4%) cardioversions not resulting in sinus rhythm at 3 months would have been avoided, whereas 22 of 23 (95.6%) cardioversions resulting in sinus rhythm at 3 months would have been administered. CONCLUSION: This model developed by GP, including clinical data, ECG data from the time-domain and nonlinear dynamics can predict maintenance of sinus rhythm. Further research is needed to explore its utility in the present or anexpanded form.
Ključne besede: optimisation methods, evolutionary optimisation methods, genetic algorithms, genetic programming, defibrillation, cardiac arrest prediction, atrial fibrillation, electrical cardioversion, prediction
Objavljeno: 01.06.2012; Ogledov: 866; Prenosov: 6
URL Polno besedilo (0,00 KB)

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: 744; Prenosov: 2
URL Polno besedilo (0,00 KB)

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: 508; Prenosov: 2
URL Polno besedilo (0,00 KB)

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: 657; Prenosov: 2
URL Polno besedilo (0,00 KB)

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: 677; Prenosov: 2
URL Polno besedilo (0,00 KB)

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