20.500.12556/DKUM-65203 Prediction of the hardness of hardened specimens with a neural network Napoved trdote kaljenih vzorcev z nevronskimi mrežami In this article we describe the methods of intelligent systems to predict the hardness of hardened specimens. We use the mathematical method of fractal geometry in laser techniques. To optimize the structure and properties of tool steel, it is necessary to take into account the effect of the self-organization of a dissipative structure with fractal properties at a load. Fractal material science researches the relation between the parameters of fractal structures and the dissipative properties of tool steel. This paper describes an application of the fractal dimension in the robot laser hardening of specimens. By using fractal dimensions, the changes in the structure can be determined because the fractal dimension is an indicator of the complexity of the sample forms. The tool steel was hardened with different speeds and at different temperatures. The effect of the parameters of robot cells on the material was better understood by researching the fractal dimensions of the microstructures of hardened specimens. With an intelligent system the productivity of the process of laser hardening was increased because the time of the process was decreased and the topographical property of the material was increased. V tem članku je uporabljena metoda inteligentnih sistemov za napovedovanje trdote kaljenih vzorcev. Uporabljena je matematična metoda fraktalne geometrije v laserski tehniki. Za optimiranje strukture in lastnosti orodnega jekla je treba upoštevati vpliv samoorganizacije strukture z lastnostmi fraktalov. Fraktalna znanost o materialu raziskuje odnos med parametri fraktalne strukture in disipativnimi lastnostmi orodnega jekla. Članek opisuje uporabo fraktalne dimenzije pri robotskem laserskem kaljenju vzorcev. Z uporabo fraktalne dimenzije se lahko določi sprememba v sestavi, ker je fraktalna dimenzija pokazatelj kompleksnosti oblike vzorcev. Orodno jeklo je bilo kaljeno z različnimi hitrostmi z različnih temperatur. Učinek parametrov robotske laserske celice na orodno jeklo se da bolj{e razumeti z raziskovanjem fraktalne dimenzije mikrostrukture kaljenih vzorcev. Z inteligentnimi sistemi je bila povečana produktivnost procesa laserskega kaljenja, ker se zmanjša trajanje procesa in se povečajo topografske lastnosti materialov. fractal dimension fractal geometry neural network prediction hardness steel tool steel laser trdota napovedovanje umetna inteligenca nevronske mreže fraktalna geometrija fraktalna dimenzija kaljeno jeklo kaljenje robotsko lasersko kaljenje orodna jekla true false true Angleški jezik Slovenski jezik Znanstveno delo 2017-03-17 13:45:25 2017-03-17 13:47:26 2022-07-07 03:08:40 2019-07-11 15:10:50 2014 0 0 str. 409-414 št. 3 Letn. 48 maj-jun. 2014 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 1580-2949 004.896:621.785.616.07 106193664 17827094 1580-2949 URN:SI:UM:DK:J1Q7C08G Materiali_in_Tehnologije_2014_Babic_et_al._Prediction_of_the_Hardness_of_Hardened_Specimens_With_a_Neural_Network.pdf Materiali_in_Tehnologije_2014_Babic_et_al._Prediction_of_the_Hardness_of_Hardened_Specimens_With_a_Neural_Network.pdf 1 F51262F532C5ED0E2A6C1C9DDF626E4B c1848e77a79a3e88bde205c429012bb3368138435666665d8c37e4d6d268b79b fff285d6-7c0d-11eb-bb7a-00155d0001ca https://dk.um.si/Dokument.php?lang=slv&id=109953 http://mit.imt.si/Revija/izvodi/mit143/babic.pdf 1 https://dk.um.si/Dokument.php?lang=slv&id=109952 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0