1. Predicting relative density of pure magnesium parts produced by laser powder bed fusion using XGBoostKristijan Šket, Snehashis Pal, Janez Gotlih, Mirko Ficko, Igor Drstvenšek, 2025, original scientific article Abstract: In this work, Laser Powder Bed Fusion (LPBF), an additive manufacturing (AM) process, was optimised to produce pure magnesium components. The focus of the presented work is on the prediction of the relative product density using the machine learning model XGBoost to improve the production process and thus the usability of the material for practical use. Experimental tests with different parameters, laser power, scanning speed and layer thickness, and fixed parameters, track overlapping and hatching distance, were analysed and resulted in relative material densities between 89.29% and 99.975%. The XGBoost model showed high predictive power, achieving an R2 test result of 0.835, a mean absolute error (MAE) of 0.728 and a root mean square error (RMSE) of 0.982. Feature importance analysis showed that the interaction of laser power and scanning speed had the largest influence on the predictions at 35.9%, followed by laser power × layer thickness at 29.0%. The individual contributions were laser power (11.8%), scanning speed (10.7%), scanning speed × layer thickness (9.0%) and layer thickness (3.6%). These results provide a data-based method for LPBF parameter settings that improve manufacturing efficiency and component performance in the aerospace, automotive and biomedical industries and identify optimal parameter regions for a high density, serving as a pre-optimisation stage.
Keywords: additive manufacturing, machine learning, XG Boost, magnesium, relative density
Published in DKUM: 03.11.2025; Views: 0; Downloads: 2
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2. Razvoj lifepo4 baterije za uporabo v električnih golf vozilihŽiga Bačnar, 2025, master's thesis Abstract: Magistrsko delo obravnava razvoj litij-železov-fosfatne (LiFePO4) baterije za električna golf vozila, ki zamenjujejo svinčeno-kislinske sisteme. Vključuje tržno analizo največjih proizvajalcev golf vozil in obstoječih litij-ionskih baterij za določitev ključnih tehničnih zahtev napetosti, kapacitete, velikosti ter zaščite. Na podlagi ugotovitev je razvit koncept baterijskega sistema z 51,2 V nominalne napetosti in 100 Ah kapacitete, ki vključuje konstrukcijo ohišja za sestavne dele baterije. Izvedena je stroškovna analiza materialov in sestave, ki omogoča oceno ekonomske upravičenosti projekta. Delo se zaključi s SWOT analizo, ki izpostavi prednosti, slabosti, priložnosti in tveganja uvedbe baterije na trg.
Keywords: razvoj, konstrukcija, litij-ionska tehnologija, LiFePO₄, baterija, golf vozila
Published in DKUM: 09.10.2025; Views: 0; Downloads: 0
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3. Razvoj pločevinastega držala za gibke cevi za natakanje v odprte posodeMiha Gerečnik, 2025, master's thesis Abstract: V magistrskem delu je predstavljen razvoj pločevinastega držala za gibke cevi, namenjenega pretakanju tekočin v odprte posode. Izhodišče naloge je bila ugotovitev, da na trgu primanjkuje preprostih, zanesljivih in cenovno dostopnih rešitev za pritrditev gibkih cevi na rob odprte posode. Kot glavno področje uporabe je bilo opredeljeno vinogradništvo, kjer je varno pretakanje grozdnega soka in vina pomembna delovna operacija.
Naloga zajema opis procesa razvoja izdelka – od idejne zasnove in oblikovanja do izdelave prototipa ter testiranja. V procesu razvoja izdelka so bile izvedene številne izboljšave, kot so optimizirana oblika izrezov za večjo stabilnost, prilagoditev ušes za vpenjanje na različne robove posod ter vključitev estetskega elementa ob hkratni minimizaciji izdelovalnih stroškov.
Rezultati kažejo, da izdelek združuje funkcionalnost, enostavnost uporabe in stroškovno učinkovitost, hkrati pa ponuja možnosti uporabe tudi v širšem spektru – od kmetijstva do prehrambne industrije. Naloga poleg tehničnega vidika vključuje tudi projektno, ekonomsko in trženjsko analizo, s čimer je prikazan celosten pristop od ideje do potencialne uvedbe izdelka na trg.
Keywords: razvoj izdelka, analiza trga, laserski razrez, upogibanje, stroškovna analiza, trženje
Published in DKUM: 07.10.2025; Views: 0; Downloads: 12
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4. Oblikovno spajanje pločevine brez pritrdilnih elementov : diplomsko deloŽiga Iršič, 2025, undergraduate thesis Abstract: Diplomsko delo obravnava oblikovno spajanja pločevine brez uporabe pritrdilnih elementov. Tovrstno spajanje se zaradi svojih prednosti vse bolj uveljavlja v sodobni industriji in lahko predstavlja alternativo tradicionalnim postopkom spajanja pločevine, kot so varjenje, kovičenje in lepljenje. Tehnologija spajanja brez pritrdilnih elementov temelji na oblikovni mehanski povezavi, ki jo omogoča pločevina z dodanimi geometrijskimi elementi, kot so reže, zavihki in kavlji. Razvoj tovrstnega spajanja sta omogočila postopka numerično krmiljenega laserskega razreza in upogibanja pločevine, ki omogočata izdelavo oblik za oblikovno spajanje pločevine brez pritrdilnih elementov. Predstavljene so nekatere možne izvedbe spojev, ki prikazujejo možnosti uporabe te metode.
Keywords: oblikovno spajanje, pločevina, laserski razrez, upogibanje
Published in DKUM: 02.10.2025; Views: 0; Downloads: 6
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5. Razvoj vpenjalne priprave za rezkanje industrijskega noža za razkroj recikliranih odpadkov : diplomsko deloMatic Orešnik, 2025, undergraduate thesis Abstract: Diplomsko delo obravnava razvoj vpenjalne priprave za rezkanje industrijskega noža, namenjenega razkroju recikliranih odpadkov. Analizirano je obstoječe stanje, izpostavljene so pomanjkljivosti trenutnega vpenjanja ter predstavljeni različni koncepti izboljšav. Koncepte primerjamo z vidika učinkovitosti, preprostosti, ergonomije in stroškov. Sledi razdelava konstrukcijskih risb ter stroškovna analiza, na podlagi katere izberemo optimalno rešitev. V zaključku s simulacijo potrdimo časovni prihranek nove priprave. Delo obravnava aktualen problem iz prakse, ki zahteva hitro zaznavanje in odpravo pomanjkljivosti v proizvodnem procesu.
Keywords: mehanska obdelava, rezkanje, vpenjanje obdelovancev, vpenjalna priprava
Published in DKUM: 26.08.2025; Views: 0; Downloads: 18
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6. Izboljšave kovanja aluminijevega nosilca robotskega prijemala : diplomsko deloTijan Marovt, 2025, undergraduate thesis Abstract: Diplomska naloga je nastala v sodelovanju s podjetjem Marovt, ki zaradi elektrifikacije avtomobilske industrije opravlja tehnološki prehod s kovanja jekla na kovanje lažjih materialov, kot sta aluminij in titan.
V diplomski nalogi je opisan postopek kovanja aluminijastega robotskega prijemala.
Opisan je proces kovanja, obrezovanje in proces termične obdelave aluminijeve zlitine 7075. V načrtovanju procesa kovanja so se s programom Qform opravile simulacije kovanja. Za razrez se uporablja tračna žaga, za predgretje električna tračna peč, pri kovanju pa kovaška stiskalnica. V procesu obrezovanja je prišlo do težav s prijemanjem odkovka na obrezilno orodje in smo jih uspešno odpravili z avtomatskim sistemom mazanja. Za potrebe končne termične obdelave se je investiralo v novo komorno peč, pri kateri smo uspešno določili parametre in potrdili ustreznost procesa s končnimi testi.
Keywords: kovanje aluminija, termična obdelava aluminija, izboljšava industrijskega procesa
Published in DKUM: 08.07.2025; Views: 0; Downloads: 30
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7. Zasnova vpenjalnega sistema za obdelavo kovaških obreznih orodij z žično erozijo : diplomsko deloMaks Mikša, 2024, undergraduate thesis Abstract: Diplomsko delo se ukvarja z izboljšanjem procesa žične elektro erozijske obdelave (WEDM) v podjetju Marovt d.o.o.. Glavni cilj naloge je konstruiranje in izdelava namenske vpenjalne naprave, za hitrejše in hkrati natančnejše vpenjanje obdelovancev. Naprava odpravlja potrebo po večkratnem centriranju in merjenju, kar znatno skrajšuje čas obdelave in povečuje učinkovitost. Vpenjalna naprava je izdelana iz nerjavečega jekla. Rezultati diplomskega dela so izboljšanje delavnih procesov, zmanjšanje proizvodnih stroškov in povečanje konkurenčnosti podjetja.
Keywords: žična elektro erozijska obdelava, kovanje, obrezovanje, vpenjalni sistemi, mehanska obdelava
Published in DKUM: 11.04.2025; Views: 0; Downloads: 42
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8. Predicting corn moisture content in continuous drying systems using LSTM neural networksMarko Simonič, Mirko Ficko, Simon Klančnik, 2025, original scientific article Abstract: As we move toward Agriculture 4.0, there is increasing attention and pressure on the productivity of food production and processing. Optimizing efficiency in critical food processes such as corn drying is essential for long-term storage and economic viability. By using innovative technologies such as machine learning, neural networks, and LSTM modeling, a predictive model was implemented for past data that include various drying parameters and weather conditions. As the data collection of 3826 samples was not originally intended as a dataset for predictive models, various imputation techniques were used to ensure integrity. The model was implemented on the imputed data using a multilayer neural network consisting of an LSTM layer and three dense layers. Its performance was evaluated using four objective metrics and achieved an RMSE of 0.645, an MSE of 0.416, an MAE of 0.352, and a MAPE of 2.555, demonstrating high predictive accuracy. Based on the results and visualization, it was concluded that the proposed model could be a useful tool for predicting the moisture content at the outlets of continuous drying systems. The research results contribute to the further development of sustainable continuous drying techniques and demonstrate the potential of a data-driven approach to improve process efficiency. This method focuses on reducing energy consumption, improving product quality, and increasing the economic profitability of food processing
Keywords: drying, moisture prediction, big data, artificial intelligence, LSTM
Published in DKUM: 21.03.2025; Views: 0; Downloads: 13
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9. 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, original scientific article Abstract: 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.
Keywords: wire arc additive manufacturing, WA AM, predictive modelling, machine learning, weld bead geometry, XGBoost
Published in DKUM: 13.03.2025; Views: 0; Downloads: 10
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10. Enhancing manufacturing precision: Leveraging motor currents data of computer numerical control machines for geometrical accuracy prediction through machine learningLucijano Berus, Jernej Hernavs, David Potočnik, Kristijan Šket, Mirko Ficko, 2024, original scientific article Abstract: Direct verification of the geometric accuracy of machined parts cannot be performed simultaneously with active machining operations, as it usually requires subsequent inspection with measuring devices such as coordinate measuring machines (CMMs) or optical 3D scanners. This sequential approach increases production time and costs. In this study, we propose a novel indirect measurement method that utilizes motor current data from the controller of a Computer Numerical Control (CNC) machine in combination with machine learning algorithms to predict the geometric accuracy of machined parts in real-time. Different machine learning algorithms, such as Random Forest (RF), k-nearest neighbors (k-NN), and Decision Trees (DT), were used for predictive modeling. Feature extraction was performed using Tsfresh and ROCKET, which allowed us to capture the patterns in the motor current data corresponding to the geometric features of the machined parts. Our predictive models were trained and validated on a dataset that included motor current readings and corresponding geometric measurements of a mounting rail later used in an engine block. The results showed that the proposed approach enabled the prediction of three geometric features of the mounting rail with an accuracy (MAPE) below 0.61% during the learning phase and 0.64% during the testing phase. These results suggest that our method could reduce the need for post-machining inspections and measurements, thereby reducing production time and costs while maintaining required quality standards
Keywords: smart production machines, data-driven manufacturing, machine learning algorithms, CNC controller data, geometrical accuracy
Published in DKUM: 10.03.2025; Views: 0; Downloads: 14
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