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1.
Reduction of surface defects by optimization of casting speed using genetic programming : an industrial case study
Miha Kovačič, Uroš Župerl, Leo Gusel, Miran Brezočnik, 2023, original scientific article

Abstract: Štore Steel Ltd. produces more than 200 different types of steel with a continuous caster installed in 2016. Several defects, mostly related to thermomechanical behaviour in the mould, originate from the continuous casting process. The same casting speed of 1.6 m/min was used for all steel grades. In May 2023, a project was launched to adjust the casting speed according to the casting temperature. This adjustment included the steel grades with the highest number of surface defects and different carbon content: 16MnCrS5, C22, 30MnVS5, and 46MnVS5. For every 10 °C deviation from the prescribed casting temperature, the speed was changed by 0.02 m/min. During the 2-month period, the ratio of rolled bars with detected surface defects (inspected by an automatic control line) decreased for the mentioned steel grades. The decreases were from 11.27 % to 7.93 %, from 12.73 % to 4.11 %, from 16.28 % to 13.40 %, and from 25.52 % to 16.99 % for 16MnCrS5, C22, 30MnVS5, and 46MnVS5, respectively. Based on the collected chemical composition and casting parameters from these two months, models were obtained using linear regression and genetic programming. These models predict the ratio of rolled bars with detected surface defects and the length of detected surface defects. According to the modelling results, the ratio of rolled bars with detected surface defects and the length of detected surface defects could be minimally reduced by 14 % and 189 %, respectively, using casting speed adjustments. A similar result was achieved from July to November 2023 by adjusting the casting speed for the other 27 types of steel. The same was predicted with the already obtained models. Genetic programming outperformed linear regression.
Keywords: continuous casting of steel, surface defects, automatic control, machine learning, modelling, optimisation, prediction, linear regression, genetic programming
Published in DKUM: 25.03.2024; Views: 100; Downloads: 5
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2.
Odziv mehanskih lastnosti na mikrostrukturne spremembe jekla 16MnCrS5
Ana Turnšek, 2017, undergraduate thesis

Abstract: Štore Steel je največji proizvajalec vzmetnega jekla v Evropi. Podjetje Štore Steel izdeluje več kot 1400 jekel različnih kvalitet z različnimi kemijskimi sestavami. Med njimi je 16MnCrS5, ki spada v skupino jekel za cementacijo. Le-ta so namenjena za strojno obdelavo različnih delov (npr. palic, plošč, trakov, odkovkov), pri katerih se zahteva kombinacija obrabne odpornosti, žilavosti ter trajno-nihajne trdnosti. Vse te lastnosti lahko povezujemo z natezno trdnostjo, ki je odvisna predvsem od kemične sestave in toplotne obdelave po valjanju. Prav tako je pomemben raztezek. V diplomskem delu je predstavljena metoda napovedovanja natezne trdnosti in raztezka s pomočjo linearne regresije. V napovednem modelu smo uporabili vsebnosti legirnih elementov v jeklu (C, Mn, S in Cr) ter načine toplotne obdelave. Glede na rezultate analize lahko povečamo natezno trdnost in izboljšamo raztezek.
Keywords: 16MnCrS5, natezna trdnost, linearna regresija, raztezek, modeliranje
Published in DKUM: 03.04.2017; Views: 2304; Downloads: 287
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3.
Prediction of the hardness of hardened specimens with a neural network
Matej Babič, Peter Kokol, Igor Belič, Peter Panjan, Miha Kovačič, Jože Balič, Timotej Verbovšek, 2014, original scientific article

Abstract: 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.
Keywords: fractal dimension, fractal geometry, neural network, prediction, hardness, steel, tool steel, laser
Published in DKUM: 17.03.2017; Views: 1803; Downloads: 109
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4.
Optimizacija razmestitve kaliber na valjih in pripadajočih dovodnih skrinj za valjanje okroglih jeklenih profilov z uporabo genetskega algoritma
Anemari Gračnar, 2016, master's thesis

Abstract: Optimizacija je v današnjem konkurenčnem in hitro odzivnem okolju bistvena za doseganje najboljših rezultatov in uspešno poslovanje. V podjetju Štore Steel d.o.o. nenehno stremijo k izboljšavam in povečanju produktivnosti posameznih proizvodnih obratov. V tem magistrskem delu se optimizacija nanaša na valjarno, in sicer na proces valjanja okroglih profilov jeklenih palic. Pri valjanju za preoblikovanje obdelovanca uporabljamo valjarska ogrodja, v katera so vstavljeni valji. Valji imajo po svojem obodu postružene oblike-kalibre, s katerimi z natančnim vodenjem valjanca neposredno v kalibro dajemo valjancu novo obliko in dimenzijo prereza. Vodenje se izvaja s skrinjami, ki so montirane na ogrodjih, pred in za valjem. Pri prehodu valjanja iz ene dimenzije na drugo se v sistemu pojavi prilagoditev posameznih valjev, tako da bomo z novo postavitvijo dosegli želeno dimenzijo. Ob tem se pojavi tudi prestavitev skrinj, tako da vodijo valjanec v zahtevano kalibro. Z analizo valjanja, opreme, s katero ga izvajamo, in planov, ki ji v proizvodnem procesu upoštevamo, smo iskali optimizacijo razmestitve kaliber na valjih ob montaži več skrinj na ogrodje. S tem smo želeli zmanjšati število menjav skrinj in zastoje, ki jih menjava povzroči. Z analizo valjev, kaliber in pripadajočih skrinj smo ugotovili glavne pogoje za optimizacijo. Za iskanje rešitve smo uporabili genetski algoritem. Cilj magistrskega dela je bil zmanjšati število menjav za 20 %, s predstavljeno optimizacijo pa se je število menjav zmanjšalo za 36,3 %. S tem je zastavljeni cilj dosežen. 
Keywords: valjanje, optimizacija, razmestitev kaliber, genetski algoritmi
Published in DKUM: 21.10.2016; Views: 1964; Downloads: 187
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5.
ANALIZA VPLIVA POSTOPKA IZDELAVE JEKLA 30MnVS6 NA POJAV POVRŠINSKIH NAPAK Z UPORABO GENETSKEGA PROGRAMIRANJA
Beno Jurjovec, 2016, master's thesis

Abstract: V magistrskem delu je predstavljeno napovedovanje izmeta jeklenih valjancev po pregledu na kontrolni liniji. Osredotočili smo se na izmet zaradi površinskih napak, na luščenih okroglih valjancih, pri kvaliteti 30MnVS6. Beležili smo kemično sestavo taline, toplotni tok, hitrost litja med odlivanjem jekla na trožilni napravi za kontinuirano odlivanje jekla in procent izmeta zaradi površinskih napak, v obdobju od septembra 2014 do maja 2015. Na podlagi zbranih podatkov sta bila izdelana modela s pomočjo linearne regresije in genetskega programiranja. Model za napovedovanje izmeta s pomočjo sistema za genetsko programiranje je 1,57-krat boljši od modela dobljenega s pomočjo linearne regresije. Izsledki raziskave so v praksi uporabljeni od sredine leta 2015. Izmet je pri kvaliteti 30MnVS6 za 3,09-krat manjši. Tako znaša letni prihranek, pri količini 12.000 t jeklenih valjancev iz 30MnVS6, 460.000 €.
Keywords: površinske napake na valjancih, modeliranje, linearna regresija, genetsko programiranje, napoved izmeta, valjano jeklo
Published in DKUM: 08.09.2016; Views: 1664; Downloads: 155
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6.
7.
MATEMATIČNI MODEL REZULTATOV ANALIZ V KOVINSKI INDUSTRIJI
Urška Bukovšek, 2014, undergraduate thesis

Abstract: Namen diplomskega dela je izdelati matematične modele na podlagi rezultatov analiz pečne in ponovčne žlindre v podjetju Štore Steel d.o.o. ter preveriti njihovo uporabnost. V prvem delu smo vzorcem pečne in ponovčne žlindre, s pomočjo volumetrične, gravimetrične in fotometrične metode, določili vsebnost MgO, CaO, MnO, Al2O3, FeO in SiO2 v masnih odstotkih. Analizne metode smo izvajali po standardnih navodilih, ki jih uporabljajo v podjetju. V drugem delu smo na podlagi dobljenih rezultatov izvedenih analiz, v programu Microsoft Excel izdelali matematične modele linearne in nelinearne regresije za vsako žlindro posebej. Ugotovili smo, da bi lahko ob analitično določeni vsebnosti ene komponente v vzorcu žlindre, matematično določili vrednosti drugih komponent in tako prihranili na času izvajanja analiz ter stroških. Za najbolj ustrezne so se izkazali modeli ponovčne žlindre, saj so se dobljeni rezultati zelo približali realnim meritvam. Pri modelih pečne žlindre pa so bila odstopanja večja, zato smo nekatere modele ovrgli.
Keywords: matematični model, linearna regresija, nelinearna regresija, pečna žlindra, ponovčna žlindra, stranski produkt
Published in DKUM: 22.09.2014; Views: 3016; Downloads: 173
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8.
9.
Predicting defibrillation success by "genetic" programming in patients with out-of-hospital cardiac arrest
Matej Podbregar, Miha Kovačič, Aleksandra Podbregar-Marš, Miran Brezočnik, 2003, original scientific article

Abstract: 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.
Keywords: optimisation methods, evolutionary optimisation methods, genetic algorithms, genetic programming, defibrillation, cardiac arrest prediction
Published in DKUM: 01.06.2012; Views: 1891; Downloads: 95
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10.
Prediction of surface roughness with genetic programming
Miran Brezočnik, Miha Kovačič, Mirko Ficko, 2004, original scientific article

Abstract: 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.
Keywords: end milling, surface roughness, prediction of surface roughness, genetic programming
Published in DKUM: 01.06.2012; Views: 1995; Downloads: 129
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