1. Predictive modelling of weld bead geometry in wire arc additive manufacturingKristijan Šket, Miran Brezočnik, Timi Karner, Rok Belšak, Mirko Ficko, Tomaž Vuherer, Janez Gotlih, 2025, izvirni znanstveni članek Opis: This study investigates the predictive modelling of weld bead geometry in wire arc additive manufacturing (WAAM) through advanced machine learning methods. While WAAM is valued for its ability to produce large, complex metal parts with high deposition rates, precise control of the weld bead remains a critical challenge due to its influence on mechanical properties and dimensional accuracy. To address this problem, this study utilized machine learning approaches—Ridge regression, Lasso regression and Bayesian ridge regression, Random Forest and XGBoost—to predict the key weld bead characteristics, namely height, width and cross-sectional area. A Design of experiments (DOE) was used to systematically vary the welding current and travelling speed, with 3D weld bead geometries captured by laser scanning. Robust data pre-processing, including outlier detection and feature engineering, improved modelling accuracy. Among the models tested, XGBoost provided the highest prediction accuracy, emphasizing its potential for real-time control of WAAM processes. Overall, this study presents a comprehensive framework for predictive modelling and provides valuable insights for process optimization and the further development of intelligent manufacturing systems.
Ključne besede: wire arc additive manufacturing, WA AM, predictive modelling, machine learning, weld bead geometry, XGBoost
Objavljeno v DKUM: 13.03.2025; Ogledov: 0; Prenosov: 6
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2. Application of machine learning to reduce casting defects from bentonite sand mixtureŽiga Breznikar, Marko Bojinović, Miran Brezočnik, 2024, izvirni znanstveni članek Opis: One of the largest Slovenian foundries (referred to as Company X) primarily focuses on casting moulds for the glass industry. In collaboration with Pro Labor d.o.o., Company X has been systematically gathering defect data since 2021. The analysis revealed that the majority of scrap caused by technological issues is attributed to sand defects. The initial dataset included information on defect occurrences, technological parameters of sand mixture and chemical properties of the cast material. This raw data was refined using data science techniques and statistical methods to support classification. Multiple binary classification models were developed, using sand mixture parameters as inputs, to distinguish between good casting and scrap, with the k-nearest neighbours algorithm. Their performances were evaluated using various classification metrics. Additionally, recommendations were made for development of a real-time industrial application to optimize and regulate pouring temperature in the foundry process. This is based on simulating different pouring temperatures while keeping the other parameters fixed, selecting the temperature that maximizes the likelihood of successful casting
Ključne besede: gravity casting, machine learning, defects, classifier, data science
Objavljeno v DKUM: 11.03.2025; Ogledov: 0; Prenosov: 8
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3. Study of environmental impacts on overhead transmission lines using genetic algorithmsKristijan Šket, Mirko Ficko, Nenad Gubeljak, Miran Brezočnik, 2023, izvirni znanstveni članek Opis: In our study, we explored the complexities of overhead transmission line (OTL) engineering, specifically focusing on their responses to varying atmospheric conditions (ambient temperature, ambient humidity, solar irradiance, ambient pressure, wind speed, wind direction), and electric current usage. Our goal was to comprehend how these independent variables impact critical responses (dependent variables) such as conductor temperature, conductor sag, tower leg stress, and vibrations – parameters crucial for electric distribution. We modelled the target output variable as a polynomial of a certain degree of the input variables. The precise forms of the polynomial were determined using the genetic algorithms (GA). Developed models are essential for quantifying the influence of each input parameter, enriching our understanding of essential system elements. They provide long-term predictions for assessing transmission line lifespan and structural stability, with particularly high precision in forecasting temperature and sag angle. It is important to note that certain engineering parameters, such as material properties and load considerations, were not included in our research, potentially influencing accuracy.
Ključne besede: Overhead Transmission Lines (OTL), machine learning, modelling, optimization, genetic algorithms (GA)
Objavljeno v DKUM: 10.03.2025; Ogledov: 0; Prenosov: 3
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4. A holistic approach to cooling system selection and injection molding process optimization based on non-dominated sortingJanez Gotlih, Miran Brezočnik, Snehashis Pal, Igor Drstvenšek, Timi Karner, Tomaž Brajlih, 2022, izvirni znanstveni članek Opis: This study applied a holistic approach to the problem of controlling the temperature of critical areas of tools using conformal cooling. The entire injection molding process is evaluated at the tool design stage using four criteria, one from each stage of the process cycle, to produce a tool with effective cooling that enables short cycle times and ensures good product quality. Tool manufacturing time and cost, as well as tool life, are considered in the optimization by introducing a novel tool-efficiency index. The multi-objective optimization is based on numerical simulations. The simulation results show that conformal cooling effectively cools the critical area of the tool and provides the shortest cycle times and the lowest warpage, but this comes with a trade-off in the tool-efficiency index. By using the tool-efficiency index with non-dominated sorting, the number of relevant simulation cases could be reduced to six, which greatly simplifies the decision regarding the choice of cooling system and process parameters. Based on the study, a tool with conformal cooling channels was made, and a coolant inlet temperature of 20 °C and a flow rate of 5 L/min for conformal and 7.5–9.5 L/min for conventional cooling channels were selected for production. The simulation results were validated by experimental measurements.
Ključne besede: conformal cooling, injection molding, tooling, additive manufacturing, numerical simulation, non-dominated sorting
Objavljeno v DKUM: 05.12.2024; Ogledov: 0; Prenosov: 5
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5. Uporaba strojnega učenja za identifikacijo glavnih parametrov bentonitne peščene mešanice za zmanjšanje izmeta ulitkov: razvoj modela znanja za klasifikacijo izmeta : diplomsko deloŽiga Breznikar, 2024, diplomsko delo Opis: V diplomskem delu najprej obravnavamo teoretične osnove gravitacijskega litja in strojnega učenja. Nato smo na podlagi podatkov iz Podjetja X razvili spletno aplikacijo za interaktiven prikaz izmeta ulitkov in klasifikator za oceno njihove kakovosti. Za razvoj modela znanja klasifikacije smo uporabili SQL Server Management Studio in Visual Studio Code ter programska jezika MS SQL (Microsoft Structured Query Language) in Python. Izhodiščne podatke smo preuredili in analizirali z uporabo metod podatkovne znanosti in statističnih metod. Podatke o izmetu je bilo treba vizualno prikazati in ustvariti model znanja, naučen na razpoložljivih podatkih, ki lahko napove, če bo prišlo do izmeta. Delo se deli na dva segmenta. Prvi segment zajema opis postopka izdelave in prikaz spletne aplikacije. Drugi segment zajema opis priprave podatkov za namene klasifikatorja, predprocesiranje, optimizacijo in analizo klasifikatorja. Na koncu dela podajamo tudi napotke za nadaljnje delo in izboljšave.
Ključne besede: gravitacijsko litje, strojno učenje, izmet, klasifikator, podatkovna znanost
Objavljeno v DKUM: 09.10.2024; Ogledov: 0; Prenosov: 32
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6. Izboljšave procesnih nastavitev pri postopku potopne elektroerozije v podjetju Talum d.d. : diplomsko deloRok Gojkošek, 2024, diplomsko delo Opis: V diplomskem delu je obravnavana tematika nekonvencionalnega postopka potopne elektroerozije in izboljšave procesnih nastavitev postopka v podjetju. Med postopkom potopne elektroerozije pride do odnašanja materiala zaradi kombinacije delovanja električne in toplotne energije. Dielektrična tekočina pa poskrbi, da med delovanjem odnaša material iz reže. S pomočjo eksperimenta smo pridobili podatke, ki so nam pomagali pri predvidevanju oziroma določevanju trajanja erodiranja za poljubno velike elektrode za izvedbo nadaljnjih obdelav.
Ključne besede: potopna elektroerozija, procesni parametri, optimizacija, trajanje obdelave, elektroda, meritve
Objavljeno v DKUM: 07.10.2024; Ogledov: 0; Prenosov: 20
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7. 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: 20
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8. Predelava robotske celice za poliranje izdelkov v avtomobilski industrijiŽak Fijavž, 2024, diplomsko delo Opis: Diplomska naloga zajema predelavo obstoječe robotske celice pri kateri smo po željah naročnika izvedli projekt poliranja novih izdelkov v avtomobilski industriji.
Izvedli smo snovanje, razvoj in konstruiranje robotske celice s prijemali, montažo komponent, programiranje in zagon. Poseben poudarek naloge je bil na sestavi prijemal. Namen predelave je bil, da izdelamo celico s katero dosegamo ustrezno ponovljivost in kvalitetno spoliranih kosov. Po zagonu smo izvedi še manjše modifikacije in podali predloge za izboljšave.
Ključne besede: robotizacija, avtomatizacija, poliranje, robotska celica, montaža
Objavljeno v DKUM: 11.07.2024; Ogledov: 101; Prenosov: 74
Celotno besedilo (12,56 MB) |
9. Izboljšava tehnološke priprave za izdelavo ohišja reduktorja : diplomsko deloRoko Oletić, 2022, diplomsko delo Opis: Sodobna računalniška tehnogija in strojna oprema omogočata učinkovitejše delo in razbremenitev delavcev, kar pomeni zmanjšanje napak ter ugodnejšo delovno atmosfero. Posledica je boljše obratovanje podjetja in ustvarjanje večjega dobička.
V diplomskem delu je obravnavana izboljšava tehnološke priprave za izdelavo in optimizacija mehanske obdelave ohišja reduktorja.
Najprej opisana problematika izdelave izdelka, nato pa podani predlogi za izboljšave in ocena kakšne so prednosti iznesene izboljšave.
Predlogi izboljšav se nanašajo predvsem na strojno pripravo posnetij za varjenje sestavin zvarjene konstrukcije reduktorja, zmanjšanje dodatkov za mehansko obdelavo in na spremembe v tehnologiji mehanske obdelave, kjer bi bilo mogoče uporabiti drugačna orodja ali celo postopke obdelave.
Ključne besede: tehnološka priprava za izdelavo, izdelava, CAD/CAM, numerično krmljeni stroji, reduktor
Objavljeno v DKUM: 09.07.2024; Ogledov: 95; Prenosov: 22
Celotno besedilo (3,78 MB) |
10. Modeliranje in izdelava dušilca zvoka za strelno orožjeLuka Šatan, 2024, magistrsko delo Opis: Cilj magistrske naloge je bil izdelati dušilec zvoka za strelno orožje modela M70 s kalibrom 7,62 × 39 mm. Prvotno je bilo treba analizirati in proučiti, do sedaj že znane študije in pregledati že izdelane dušilce. Na podlagi teh smo v programskem okolju SolidWorks oblikovali štiri različne oblike ekspanzijskih komor, na katerih smo na to v istem programskem okolju izvedli analizo zračnega tlaka in akustičnih moči. Po končani analizi smo komore in ostale potrebne sklope dušilca izvozili v obliki STEP formata in dušilec fizično izdelali na CNC obdelovalnem stroju. Za izdelane dušilce smo izvedli tudi meritve jakosti zvoka na terenu. Na koncu smo določili najprimernejši dušilec za obravnavano strelno orožje.
Ključne besede: strelno orožje, dušilec zvoka, hrup, modeliranje, izdelava, SolidWorks
Objavljeno v DKUM: 30.05.2024; Ogledov: 168; Prenosov: 43
Celotno besedilo (4,22 MB) |
