@article{Sauperl:2006:MRI, title = {Study of crosslinking efficiency of cotton cellulose by different physical-chemical methods and genetic programming}, author = {Olivera Sauperl and Miran Brezocnik}, journal = {Materials research innovations}, month = {March}, number = {1}, pages = {45--62}, url = {http://www.matrice-technology.com/mri/abstract.php?pid=418}, volume = {10}, year = {2006}, biburl = {http://www.bibsonomy.org/bibtex/22061c0db70e5858d331b8a9e52bdd50d/brazovayeye}, abstract = {We have investigated the crosslinking effect of unmercerised and mercerized cotton cellulose crosslinked with different BTCA mass fractions in the impregnation bath. Crosslinking efficiency was analysed using FT-IR spectroscopy, water retention capacity method, tensiometry and the methylene blue method. On the basis of the experimental data which was obtained with the separate physical-chemical methods, different prediction models for crosslinking efficiency was developed. Modelling was taken out with the genetic programming method. Research shows good accordance of the experimental data with the genetic models.}, issn = {1433-075X}, email = {olivera.sauperl@uni-mb.si}, keywords = {FT-IR algorithms, blue capacity, cellulose, cotton crosslinking, genetic method, methylene programming, retention spectroscopy, tensiometry, water } } @article{Podbregar:2003:R, title = {Predicting defibrillation success by 'genetic' programming in patients with out-of-hospital cardiac arrest}, author = {M. Podbregar and M. Kovacic and A. Podbregar-Mars and M. Brezocnik}, journal = {Resuscitation}, number = {2}, pages = {153--159}, url = {http://www.sciencedirect.com/science/article/B6T19-48D3F91-3/2/1a3ece76d7e7c59fb51615980e9791a6}, volume = {57}, year = {2003}, biburl = {http://www.bibsonomy.org/bibtex/20a842a60bb090ed50bd0317dda73019d/brazovayeye}, abstract = {Background: 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 power of 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.}, owner = {wlangdon}, doi = {doi:10.1016/S0300-9572(03)00030-3}, keywords = {algorithms, genetic programming } } @article{Ficko:2005:JMPT, title = {Prediction of total manufacturing costs for stamping tool on the basis of {CAD}-model of finished product}, author = {M. Ficko and I. Drstvensek and M. Brezocnik and J. Balic and B. Vaupotic}, journal = {Journal of Materials Processing Technology}, month = {15 May}, pages = {1327--1335}, url = {http://www.sciencedirect.com/science/article/B6TGJ-4FJKWTY-D/2/17df3b2567564f2d6c9d8fdcb041d0e9}, volume = {164-165}, year = {2005}, biburl = {http://www.bibsonomy.org/bibtex/270188e2e0c83f44ca2d48500e5ed773f/brazovayeye}, abstract = {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.}, issn = {0924-0136}, owner = {wlangdon}, notes = {AMPT/AMME05 Part 2}, doi = {doi:10.1016/j.jmatprotec.2005.02.013}, keywords = {CAD-model, Intelligent Prediction Stamping, Tool-making, algorithms, costs, genetic of programming, systems } } @article{ficko:2004:JMPT, title = {Designing the layout of single- and multiple-rows flexible manufacturing system by genetic algorithms}, author = {Mirko Ficko and Miran Brezocnik and Joze Balic}, journal = {Journal of Materials Processing Technology}, month = {20 December}, pages = {150--158}, volume = {157-158}, year = {2004}, biburl = {http://www.bibsonomy.org/bibtex/20f804dd2b18e3c7007792654e67bb282/brazovayeye}, abstract = {model of designing of the flexible manufacturing system (FMS) in one or multiple rows with genetic algorithms (GAs). First the reasons for studying the layout of devices in the FMS are discussed. After studying the properties of the FMS and perusing the methods of layout designing the genetic algorithms methods was selected as the most suitable method for designing the FMS. The genetic algorithm model, the most suitable way of coding the solutions into the organisms and the selected evolutionary and genetic operators are presented. In the model, the automated guided vehicles (AGVs) for transport between components of the FMS were used. In this connection, the most favourable number of rows and the sequence of devices in the individual row are established by means of genetic algorithms. In the end the test results of the application made and the analysis are discussed.}, issn = {0924-0136}, notes = {Special issue {"}Achievements in Mechanical and Materials Engineering Conference{"} Edited by L. A. Dobranski}, doi = {doi:10.1016/j.jmatprotec.2004.09.012}, keywords = {(FMS), Facility Flexible Optimisation, algorithms, genetic layout manufacturing programming, systems } } @article{Ficko:2004:AJME, title = {Genetic algorithms : a useful optimization method for manufacturing problems}, author = {Mirko Ficko and Miha Kovacic and Miran Brezocnik}, journal = {Academic Journal of Manufacturing Engineering}, number = {1}, pages = {21--26}, volume = {2}, year = {2004}, biburl = {http://www.bibsonomy.org/bibtex/27b2ff449545156a356a4021d0f6d0c10/brazovayeye}, abstract = {a very useful method for solving g the manufacturing problems, and optimising the manufacturing process, i.e. the genetic algorithms (GAs). The well-known basic knowledge of the conventional GAs is briefly presented. The second part of the paper discusses an example of optimisation of the design of the flexible manufacturing system (FMS) in one row with GAs. First the reasons for studying the layout of devices in the FMS are discussed. The GA model, the most suitable way of coding the solutions into the organisms and the selected evolutionary and genetic operators are presented. In the model, the automated guided vehicles (AGVs) for transport between components of the FMS were used. In this connection, the most favourable sequence of devices in the row is established by means of GAs. In the end the test results of the application made and the analysis are discussed.}, issn = {1583-7904}, notes = {http://www.eng.utt.ro/auif/rev/issue/no-05/no-05.html University of Maribor, Faculty for Mechanical Engineering, Laboratory for Intelligent Manufacturing Systems}, keywords = {algorithms, facility flexible genetic layout, manufacturing optimisation, programming, systems } } @article{Dobnik-Dubrovski:2002:TRL, title = {Using genetic programming to predict the macroporosity of woven cotton fabrics}, author = {Polona {Dobnik Dubrovski} and Miran Brezocnik}, journal = {Textile research journal}, month = {March}, number = {3}, pages = {187--194}, publisher = {Sage}, url = {http://cat.inist.fr/?aModele=afficheN&cpsidt=13560450}, volume = {72}, year = {2002}, biburl = {http://www.bibsonomy.org/bibtex/25cbec4c1723d540c3bc05c303170c11f/brazovayeye}, abstract = {This paper reports the effect of woven fabric construction on macroporosity properties. The area of a macropore's cross section, equivalent, maximum, and minimum pore diameters, pore density, and open porosity are observed in this research involving woven fabric construction parameters-yarn linear density, fabric tightness, weave type, and denting. Predictive models, determined by genetic programming, are derived to describe the influence of fabric construction. The results show very good agreement between the experimental and predicted values. This work provides guidelines for engineering staple-yarn cotton fabrics in a grey state in terms of macroporosity properties.}, issn = {0040-5175}, email = {mbrezocnik@uni-mb.si}, doi = {doi:10.1177/004051750207200301}, keywords = {algorithms, cotton fabrics, genetic macroporosity, modelling programming, woven } } @article{Brezocnik:2006:AMME, title = {Prediction of steel machinability by genetic programming}, author = {Miran Brezocnik and Miha Kovacic and Matej Psenicnik}, journal = {Journal of achievements in materials and manufacturing engineering}, month = {May-June}, note = {Special Issue of CAM3S'2005}, number = {1-2}, pages = {107--113}, url = {http://157.158.19.167/papers_cams05/1123.pdf}, volume = {16}, year = {2006}, biburl = {http://www.bibsonomy.org/bibtex/2779fd2a01c48db59030e09b300e43a09/brazovayeye}, abstract = {The steels with extra machinability are made according to a special technological process. Such steels can be machined at high cutting speeds. In addition, the resistance of the tools used for machining, is higher than in the case of ordinary steels. It depends on several parameters, particularly on the steel chemical composition, whether the steel will meet the criterion of extra machinability. Special tests for each batch separately show whether the steel has extra machinability or not. In our research, the prediction of machinability of steels, depending on input parameters, was performed by genetic programming and data on the batches of steel already made. The model developed during the simulated evolution was tested also with the testing data set. The results show that the proposed concept can be successfully used in practice.}, issn_ = {Y505-3994 invalid checksum}, size = {7 pages}, notes = {http://www.journalamme.org/ http://157.158.19.167/index.php?id=69 Formerly Proceedings of Achievements in Mechanical and Materials Engineering. (1.123) Intelligent Manufacturing Systems Laboratory, University of Maribor, Faculty of Mechanical Engineering, Smetanova ulica 17, SI-2000 Maribor, Slovenia}, keywords = {Extra Modelling Steel algorithms, genetic machinability, programming, } } @article{brezocnik:2005:MMP, title = {Comparison Between Genetic Algorithm and Genetic Programming Approach for Modeling the Stress Distribution}, author = {Miran Brezocnik and Miha Kovacic and Leo Gusel}, journal = {Materials and Manufacturing Processes}, number = {3}, pages = {497--508}, url = {http://journalsonline.tandf.co.uk/openurl.asp?genre=article&issn=1042-6914&volume=20&issue=3&spage=497}, volume = {20}, year = {2005}, biburl = {http://www.bibsonomy.org/bibtex/209b7f1294b1d13141b76e0b06c1fbbb4/brazovayeye}, abstract = {We compare genetic algorithm (GA) and genetic programming (GP) for system modelling in metal forming. As an example, the radial stress distribution in a cold-formed specimen (steel X6Cr13) was predicted by GA and GP. First, cylindrical workpieces were forward extruded and analysed by the visioplasticity method. After each extrusion, the values of independent variables (radial position of measured stress node, axial position of measured stress node, and coefficient of friction) were collected. These variables influence the value of the dependent variable, radial stress. On the basis of training data, different prediction models for radial stress distribution were developed independently by GA and GP. The obtained models were tested with the testing data. The research has shown that both approaches are suitable for system modeling. However, if the relations between input and output variables are complex, the models developed by the GP approach are much more accurate.}, issn = {1042-6914}, notes = {A1 Laboratory for Intelligent Manufacturing Systems, University of Maribor, Faculty of Mechanical Engineering, Maribor, Slovenia A2 Laboratory for Material Forming, University of Maribor, Faculty of Mechanical Engineering, Maribor, Slovenia}, doi = {doi:10.1081/AMP-200053541}, keywords = {Metal Stress System algorithms, distribution, forming, genetic modelling programming, } } @inproceedings{Brezocnik:2005:RIM, title = {Cost estimation for punch dies by genetic programming}, author = {Miran Brezocnik and Bostjan Vaupotic and Janez Fridrih and Ivo Pahole}, booktitle = {RIM 2005 / 5th International scientific conference on Production engineering}, editor = {Milan Jurkovic and Vlatko Dolecek}, month = {14-17 September}, pages = {167--172}, publisher = {Faculty of Technical Engineering, Bihac, Bosnia and Hercegovina}, year = {2005}, biburl = {http://www.bibsonomy.org/bibtex/22bf2820d9258d3c47a4b96973fae9c04/brazovayeye}, abstract = {The paper presents a new approach for cost estimation of punch dies used in metal-forming industry. In the modern business world fast and accurate information is the principal advantage in securing orders and establishing the company's renowned. Often, the offer for the manufacturing and supply of the tool must be sent within a short time. However, precise preparation of the offer requires much work. The paper presents an approach ensuring fast determination of the relatively precise cost estimate of the punch dies on the basis of the tool input parameters (e.g., outside dimensions, number of blades, number of directions of cutting). The proposed approach is based on the evolutionary searching for the adequate general equation describing the influence of the tool input parameters on punch die manufacturing cost. Evolutionary development of the equation was performed by the genetic programming and the base of the punch dies already made.}, email = {mbrezocnik@uni-mb.si}, isbn = {9958-9262-0-2}, keywords = {algorithms, cost dies, estimation genetic programming, punch } } @article{Brezocnik:2003:MMP, title = {Integrated genetic programming and genetic algorithm approach to predict surface roughness}, author = {Miran Brezocnik and Miha Kovacic}, journal = {Materials and Manufacturing Processes}, month = {May}, number = {3}, pages = {475--491}, volume = {18}, year = {2003}, biburl = {http://www.bibsonomy.org/bibtex/2674d3cdfe0b606cbfd1c9ad58a270965/brazovayeye}, abstract = {we propose a new integrated genetic programming and genetic algorithm approach to predict surface roughness in end-milling. Four independent variables, spindle speed, feed rate, depth of cut, and vibrations, were measured. Those variables influence the dependent variable (i.e., surface roughness). On the basis of training data set, different models for surface roughness were developed by genetic programming. The floating-point constants of the best model were additionally optimised by a genetic algorithm. Accuracy of the model was proved on the testing data set. By using the proposed approach, more accurate prediction of surface roughness was reached than if only modelling by genetic programming had been carried out. It was also established that the surface roughness is most influenced by the feed rate, whereas the vibrations increase the prediction accuracy.}, doi = {doi:10.1081/AMP-120022023}, keywords = {Manufacturing Milling Surface algorithms, genetic programming, roughness, systems, } }