1. Optimization of billet cooling after continuous casting using genetic programming—industrial studyMiha Kovačič, Aljaž Zupanc, Robert Vertnik, Uroš Župerl, 2024, izvirni znanstveni članek Opis: ŠTORE STEEL Ltd. is one of the three steel plants in Slovenia. Continuous cast 180 mm × 180 mm billets can undergo cooling to room temperature using a turnover cooling bed. They can also be cooled down under hoods or heat treated to reduce residual stresses. Additional operations of heat treatment from 36 h up to 72 h and cooling of the billets for 24 h, with limited capacities (with only two heat treatment furnaces and only six hoods), drastically influence productivity. Accordingly, the casting must be carefully planned (i.e., the main thing is casting in sequences), while the internal quality of the billets (i.e., the occurrence of inner defects) may be compromised. Also, the stock of billets can increase dramatically. As a result, it was necessary to consider the abandoning of cooling under hoods and heat treatment of billets. Based on the collected scrap data after ultrasonic examination of rolled bars, linear regression and genetic programming were used for prediction of the occurrence of inner defects. Based on modeling results, cooling under hoods and heat treatment of billets were abandoned at the casting of several steel grades. Accordingly, the casting sequences increased, and the stock of billets decreased drastically while the internal quality of the rolled bars remained the same.
Ključne besede: billet cooling, continuous casting, ultrasonic testing, logistic regression, genetic programming, industrial study, steel making, optimization
Objavljeno v DKUM: 25.11.2024; Ogledov: 0; Prenosov: 6
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2. Development and control of virtual industrial process using Factory IO and MATLABGoran Munđar, Miha Kovačič, Uroš Župerl, 2024, izvirni znanstveni članek Opis: In today's rapidly evolving business landscape, the strategic adoption of virtual manufacturing methods has emerged as a key driver for companies seeking to streamline
operations and expedite product launches in a cost-effective manner. This progressive approach involves the creation of a synthetic and interconnected environment, empowered
by advanced software tools and systems, including Virtual Reality and Simulation technologies, tailored to optimize industrial processes. Our methodology employs a unique
combination of two simulation software tools: Factory I/O for process development and MATLAB for control program implementation. Furthermore, we explore the use of the
Modbus TCP/IP communication protocol as the framework for seamless interaction between these software tools during simulation. This research presents practical insights into
the transformative potential of virtual manufacturing, showcasing its real-world application in enhancing operational efficiency and agility within industrial settings.
Ključne besede: Factory I/O, MATLAB, Modbus TCP/IP, simulation technologies, virtual manufacturing
Objavljeno v DKUM: 19.09.2024; Ogledov: 0; Prenosov: 3
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3. Rapid assessment of steel machinability through spark analysis and data-mining techniquesGoran Munđar, Miha Kovačič, Miran Brezočnik, Krzysztof Stępień, Uroš Župerl, 2024, izvirni znanstveni članek Opis: The machinability of steel is a crucial factor in manufacturing, influencing tool life, cutting
forces, surface finish, and production costs. Traditional machinability assessments are labor-intensive
and costly. This study presents a novel methodology to rapidly determine steel machinability using
spark testing and convolutional neural networks (CNNs). We evaluated 45 steel samples, including
various low-alloy and high-alloy steels, with most samples being calcium steels known for their
superior machinability. Grinding experiments were conducted using a CNC machine with a ceramic
grinding wheel under controlled conditions to ensure a constant cutting force. Spark images captured
during grinding were analyzed using CNN models with the ResNet18 architecture to predict V15
values, which were measured using the standard ISO 3685 test. Our results demonstrate that the
created prediction models achieved a mean absolute percentage error (MAPE) of 12.88%. While
some samples exhibited high MAPE values, the method overall provided accurate machinability
predictions. Compared to the standard ISO test, which takes several hours to complete, our method is
significantly faster, taking only a few minutes. This study highlights the potential for a cost-effective
and time-efficient alternative testing method, thereby supporting improved manufacturing processes.
Ključne besede: steel machinability, spark testing, data mining, machine vision, convolutional neural networks
Objavljeno v DKUM: 12.09.2024; Ogledov: 15; Prenosov: 13
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4. Reduction of surface defects by optimization of casting speed using genetic programming : an industrial case studyMiha Kovačič, Uroš Župerl, Leo Gusel, Miran Brezočnik, 2023, izvirni znanstveni članek Opis: 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.
Ključne besede: continuous casting of steel, surface defects, automatic control, machine learning, modelling, optimisation, prediction, linear regression, genetic programming
Objavljeno v DKUM: 25.03.2024; Ogledov: 284; Prenosov: 18
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5. Odziv mehanskih lastnosti na mikrostrukturne spremembe jekla 16MnCrS5Ana Turnšek, 2017, diplomsko delo Opis: 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.
Ključne besede: 16MnCrS5, natezna trdnost, linearna regresija, raztezek, modeliranje
Objavljeno v DKUM: 03.04.2017; Ogledov: 2732; Prenosov: 297
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6. Prediction of the hardness of hardened specimens with a neural networkMatej Babič, Peter Kokol, Igor Belič, Peter Panjan, Miha Kovačič, Jože Balič, Timotej Verbovšek, 2014, izvirni znanstveni članek Opis: 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.
Ključne besede: fractal dimension, fractal geometry, neural network, prediction, hardness, steel, tool steel, laser
Objavljeno v DKUM: 17.03.2017; Ogledov: 2032; Prenosov: 120
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7. Optimizacija razmestitve kaliber na valjih in pripadajočih dovodnih skrinj za valjanje okroglih jeklenih profilov z uporabo genetskega algoritmaAnemari Gračnar, 2016, magistrsko delo Opis: 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.
Ključne besede: valjanje, optimizacija, razmestitev kaliber, genetski algoritmi
Objavljeno v DKUM: 21.10.2016; Ogledov: 2198; Prenosov: 198
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8. ANALIZA VPLIVA POSTOPKA IZDELAVE JEKLA 30MnVS6 NA POJAV POVRŠINSKIH NAPAK Z UPORABO GENETSKEGA PROGRAMIRANJABeno Jurjovec, 2016, magistrsko delo Opis: 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 €.
Ključne besede: površinske napake na valjancih, modeliranje, linearna regresija, genetsko programiranje, napoved izmeta, valjano jeklo
Objavljeno v DKUM: 08.09.2016; Ogledov: 1878; Prenosov: 165
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10. MATEMATIČNI MODEL REZULTATOV ANALIZ V KOVINSKI INDUSTRIJIUrška Bukovšek, 2014, diplomsko delo Opis: 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.
Ključne besede: matematični model, linearna regresija, nelinearna regresija, pečna žlindra, ponovčna žlindra, stranski produkt
Objavljeno v DKUM: 22.09.2014; Ogledov: 3214; Prenosov: 182
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