1. Editorial : special issue of the Faculty of Mechanical Engineering, University of MariborMatej Vesenjak, Matej Borovinšek, Simon Klančnik, 2025, drugi znanstveni članki Opis: This editorial introduces the Special Issue of the Strojniški vestnik - Journal of Mechanical Engineering dedicated to the 30th anniversary of the Faculty of Mechanical Engineering as an independent member of the University of Maribor, and the 50th anniversary of the University of Maribor. The Faculty of Mechanical Engineering is one of the most successful members at the University of Maribor and is recognised for its excellence in education, research and collaboration with industry. Its history of development, from its early beginnings in 1959 to becoming an internationally active and research-driven institution, reflects a continuous commitment to technological progress and societal impact. The Special Issue presents a selection of articles covering applied fluid mechanics, advanced materials and metamaterials, manufacturing science, and biomedical modelling. The collected works combine experimental, numerical, and review-based approaches to address contemporary challenges in mechanical engineering. This publication not only highlights the scientific excellence achieved at the Faculty of Mechanical Engineering, University of Maribor, but also celebrates its enduring mission to connect knowledge, innovation and human creativity in shaping a sustainable and technologically advanced future.
Ključne besede: applied fluid mechanics, computational fluid dynamics (CFD), hydropower systems, advanced materials, metamaterials, triply periodic minimal surfaces (TPMS), biomedical modelling, inverse bioheat problem, intelligent toolpath generation, artificial intelligence in manufacturing
Objavljeno v DKUM: 08.12.2025; Ogledov: 0; Prenosov: 0
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2. Large language models for G-code generation in CNC machining: A comparison of ChatGPT-3.5 and ChatGPT-4oKristijan Šket, David Potočnik, Miran Brezočnik, Mirko Ficko, Simon Klančnik, 2025, izvirni znanstveni članek Opis: This research explores the viability of producing ISO G-code for 3-axis machining with OpenAI's Chat Generative Pre-Trained Transformer models, particularly ChatGPT-3.5 and the newer GPT-4o. G-code (RS-274-D, ISO 6983) converts human directives into commands that machines can understand, controlling toolpaths, spindle velocities, and feed rates to produce particular aspects of an object. Previously, G-code was generated either by hand or through the use of computer-aided manufacturing (CAM) software along with machine-specific post-processors, both of which may require considerable time and expense. This research aimed to assess the practicality and effectiveness of specific large language models (LLMs) in generating G-code. The assessment took place in three distinct phases on a sample component that required 3-axis machining. These phases included: (1) the self-generated production of G-code for the sample component, (2) the examination of the independently generated G-code in the CAM application, and (3) the recognition and justification of mistakes in the G-code. The outcomes indicated varying abilities with promising findings. This method could accelerate and possibly enhance manufacturing workflows by decreasing reliance on expensive CAM software and specialized knowledge.
Ključne besede: generative artificial intelligence, intelligent manufacturing, large language models (LLM), ChatGPT, CNC machining, G-code programming
Objavljeno v DKUM: 28.11.2025; Ogledov: 0; Prenosov: 7
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3. Optimizacije v inženirstvu : reševanje problemov z metahevrističnimi metodami v okolju MATLABJanez Gotlih, Mirko Ficko, 2025, učbenik za višje in visoke šole Opis: Skripta obravnavajo temeljne pristope optimizacije v inženirstvu s poudarkom na uporabi metahevrističnih metod, kot sta genetski algoritem (GA) in algoritem rojev delcev (PSO). Namenjena so študentom in inženirjem, ki želijo razumeti tako teoretično ozadje kot praktično implementacijo optimizacijskih algoritmov v okolju MATLAB. Vključujejo poglavja o enokriterijskih in večkriterijskih optimizacijskih problemih, obravnavajo omejitve, različne ciljne funkcije ter vizualizacijo rezultatov. Vsako poglavje vsebuje strukturirane vaje in naloge za samostojno delo, ki spodbujajo razumevanje delovanja algoritmov, oblikovanje optimizacijskih modelov in interpretacijo rešitev. Poseben poudarek je na razlagi parametrov algoritmov, primerjavi konvergence ter vplivu nastavitev na vedenje optimizacije. Skripta se zaključijo s pregledom značilnih testnih funkcij in primeri Pareto front za večkriterijsko optimizacijo. Zasnovana so tako, da tudi uporabniki brez poglobljenega matematičnega znanja lahko postopoma razvijejo intuicijo za uporabo optimizacijskih pristopov v realnih inženirskih problemih.
Ključne besede: metahevristične metode, genetski algoritem (GA), algoritem rojev delcev (PSO), eno- in večkriterijska optimizacija, MATLAB, inženirske aplikacije
Objavljeno v DKUM: 11.11.2025; Ogledov: 0; Prenosov: 1
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4. Strojno učenje za inženirje : koncepti, primeri in uporaba v okolju MATLABJanez Gotlih, Miran Brezočnik, 2025, drugo učno gradivo Opis: Skripta obravnavajo strojno učenje z vidika uporabe v inženirstvu, pri čemer temeljne koncepte povezujejo s praktičnimi primeri v okolju MATLAB. Predstavljeni so štirje temeljni pristopi strojnega učenja: nadzorovano učenje, nenadzorovano učenje, učenje z okrepitvijo in prenosno učenje. Za vsak pristop so podani temeljni koncepti, konkretni primeri uporabe ter naloge za samostojno delo. Poseben poudarek je na uporabi orodij, kot so Regression Learner, Classification Learner, Deep Network Designer in Reinforcement Learning Designer, s pomočjo katerih študenti razvijajo modele na podatkih, ki izvirajo iz realnih inženirskih primerov. Med njimi so obraba orodja, vibracije strojev, balansiranje sistemov in prepoznavanje predmetov. Skripta vključujejo tudi eksperimentalne podatkovne množice in praktične napotke za učenje, validacijo in izboljšavo modelov. Namenjena so študentom tehniških smeri ter vsem, ki želijo usvojiti uporabo metod strojnega učenja za reševanje konkretnih inženirskih problemov.
Ključne besede: strojno učenje, nadzorovano učenje, nenadzorovano učenje, učenje z okrepitvijo, prenosno učenje, MATLAB, inženirske aplikacije
Objavljeno v DKUM: 10.11.2025; Ogledov: 0; Prenosov: 8
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5. Temporal and statistical insights into multivariate time series forecasting of corn outlet moisture in industrial continuous-flow drying systemsMarko Simonič, Simon Klančnik, 2025, izvirni znanstveni članek Opis: Corn drying is a critical post-harvest process to ensure product quality and compliance with moisture standards. Traditional optimization approaches often overlook dynamic interactions between operational parameters and environmental factors in industrial continuous flow drying systems. This study integrates statistical analysis and deep learning to predict outlet moisture content, leveraging a dataset of 3826 observations from an operational dryer. The effects of inlet moisture, target air temperature, and material discharge interval on thermal behavior of the system were evaluated through linear regression and t-test, which provided interpretable insights into process dependencies. Three neural network architectures (LSTM, GRU, and TCN) were benchmarked for multivariate time-series forecasting of outlet corn moisture, with hyperparameters optimized using grid search to ensure fair performance comparison. Results demonstrated GRU’s superior performance in the context of absolute deviations, achieving the lowest mean absolute error (MAE = 0.304%) and competitive mean squared error (MSE = 0.304%), compared to LSTM (MAE = 0.368%, MSE = 0.291%) and TCN (MAE = 0.397%, MSE = 0.315%). While GRU excelled in average prediction accuracy, LSTM’s lower MSE highlighted its robustness against extreme deviations. The hybrid methodology bridges statistical insights for interpretability with deep learning’s dynamic predictive capabilities, offering a scalable framework for real-time process optimization. By combining traditional analytical methods (e.g., regression and t-test) with deep learning-driven forecasting, this work advances intelligent monitoring and control of industrial drying systems, enhancing process stability, ensuring compliance with moisture standards, and indirectly supporting energy efficiency by reducing over drying and enabling more consistent operation.
Ključne besede: advanced drying technologies, continuous flow drying, time-series forecasting, LSTM, GRU, TCN, deep learning, statistical analysis, optimization of the drying process
Objavljeno v DKUM: 03.11.2025; Ogledov: 0; Prenosov: 3
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6. Generating test cases for automotive requirement testingusing rag : magistrsko deloMatic Krepek, 2025, magistrsko delo Opis: The automotive industry is increasingly confronted with challenges in managing complex requirements and test cases arising from the integration of advanced electronic systems, software functionalities, and compliance with international standards. Conventional manual validation of requirements is time-consuming, error-prone, and resource-intensive, underscoring the need for more efficient and reliable approaches. This thesis investigates the automation of test case generation through the application of Retrieval-Augmented Generation (RAG) in combination with Large Language Models (LLMs). A complete RAG workflow was implemented in Python, incorporating LangChain, LangGraph, Ollama, and ChromaDB to facilitate indexing, retrieval, and generation. The system was trained and evaluated on datasets comprising automotive requirements and test cases, with experiments examining embedding quality, retrieval strategies, prompt engineering techniques, and generative model parameters. The results demonstrate that RAG is capable of generating high-quality, contextually relevant test cases on consumer-grade hardware, thereby significantly enhancing efficiency, consistency, and productivity relative to manual methods. Furthermore, the findings suggest that RAG-based systems are best positioned as complementary tools that support, rather than replace, human engineers. This research provides a foundation for future work on hybrid retrieval methods, advanced embedding techniques, and the integration of more powerful LLMs into requirement and test case management processes.
Ključne besede: automotive requirements validation, test case generation, large Language Models, Retrieval-Augmented Generation
Objavljeno v DKUM: 01.10.2025; Ogledov: 0; Prenosov: 0
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7. Optično sortiranje plastičnega granulataPatrik Kožar, Marko Simonič, 2025, diplomsko delo Opis: Diplomsko delo obravnava razvoj in izvedbo optičnega sortirnega sistema za razvrščanje granulata po barvi in velikosti. Glavni cilj je bil izdelati preprost, a učinkovit prototip, ki z uporabo strojnega vida zazna posamezne delce na tekočem traku in jih s pomočjo pnevmatskih ventilov usmeri v ustrezne zbiralnike. Sistem temelji na Beckhoffovem krmilniku in TwinCAT Vision okolju, kjer smo implementirali algoritem za zajem slike, določanje lege delcev in časovno usklajeno proženje ventilov. Preizkusi so pokazali, da je sistem pravilno razvrstil približno 73 % vseh granul, kar potrjuje delovanje prototipa in daje dobro osnovo za nadaljnji razvoj.
Ključne besede: sortirnik, optični, granulat, strojni vid, Beckhoff
Objavljeno v DKUM: 22.09.2025; Ogledov: 0; Prenosov: 5
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9. Klasifikacija krompirja s pomočjo globokih nevronskih mrež s podporo barvne in termovizijske analize : magistrsko deloTaja Pec, 2025, magistrsko delo Opis: V magistrskem delu obravnavamo klasifikacijo krompirja v tri kakovostne razrede – gnili, krmni in jedilni – z uporabo naprednih globokih nevronskih mrež. Model smo razvili v programskem jeziku Python z uporabo ogrodja TensorFlow. Primerjali smo učinkovitost treh sodobnih arhitektur konvolucijskih nevronskih mrež: EfficientNet, DenseNet in Xception, ter na podlagi rezultatov izbrali najbolj primerno za našo podatkovno bazo. DenseNet201 je izstopal kot najbolj natančen in stabilen model, DenseNet121 pa je ponujal najboljše ravnovesje med natančnostjo in računsko zahtevnostjo. Preizkusili smo tudi termovizijsko kamero za preučevanje možnosti zaznave gnilobe na podlagi temperaturnih razlik krompirja in opisali omejitve te metode. Cilj raziskave je razvoj inteligentnega, cenovno dostopnega sistema za avtomatsko sortiranje krompirja, ki bi povečal produktivnost kmetijske proizvodnje ob minimalnih stroških.
Ključne besede: globoko učenje, konvolucijske nevronske mreže, klasifikacija krompirja, TensorFlow, termovizijska analiza
Objavljeno v DKUM: 22.09.2025; Ogledov: 0; Prenosov: 13
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10. Hardened workpiece shape prediction using acoustic responses and deep neural networkJernej Hernavs, Tadej Peršak, Miran Brezočnik, Simon Klančnik, 2025, izvirni znanstveni članek Opis: This study proposes a novel approach to predict the shape of hardened metal workpieces using acoustic responses processed by a deep convolutional neural network (CNN), aiming to advance automated straightening in manufacturing. Tool steel 1.2379 workpieces of varying widths (24 mm, 90 mm, 200 mm) were struck using a custom-built device, with acoustic responses captured and transformed into scalograms via Continuous Wavelet Transform (CWT). A 40-layer CNN predicted 5×9 shape matrices, validated by 3D scans. The dataset (219 shape states, 3396 recordings) was evaluated using leaveone-workpiece-out cross-validation, comparing the CNN against baseline models (linear regression, random forest, shallow CNN, XGBoost). CNN achieved competitive accuracy, demonstrating the feasibility of acoustic-based shape prediction. As a non-invasive, cost-efective complement to 3D scanning, this method ofers innovative potential for multi-modal quality control systems in manufacturing.
Ključne besede: metal workpiece, hardened, deep neural network, acoustic respons, shape prediction
Objavljeno v DKUM: 14.08.2025; Ogledov: 0; Prenosov: 9
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