Search the digital library catalog

Abstract: Diplomska naloga obravnava razvoj modularne vpenjalne priprave, namenjene hidravličnemu vpenjanju specifičnega obdelovanca. V nalogi je zasnovana priprava z variabilnim hidravličnim vpenjalom ter izvrtinskim vpenjalom, ki omogočata prilagodljivost ter izboljšujeta ponovljivost in učinkovitost vpenjalnega procesa. Teoretični del zajema predstavitev osnovnih principov hidravličnih sistemov in ključnih komponent. Praktični del je osredotočen na konstrukcijo sistema v programski opremi SolidWorks ter analizo njegovega delovanja na izbranem obdelovancu.
Keywords: modularna priprava, hidravlično vpenjanje, izvrtinsko vpenjalo, variabilno vpenjalo, pozicioniranje obdelovanca, vpenjalna tehnika
Published in DKUM: 04.09.2025; Views: 0; Downloads: 8
Full text (2,05 MB)

Abstract: 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.
Keywords: metal workpiece, hardened, deep neural network, acoustic respons, shape prediction
Published in DKUM: 14.08.2025; Views: 0; Downloads: 13
Full text (1,10 MB)
This document has many files! More...

Abstract: In industry, metal workpieces are often heat-treated to improve their mechanical properties, which leads to unwanted deformations and changes in their geometry. Due to their high hardness (60 HRC or more), conventional bending and rolling straightening approaches are not effective, as a failure of the material occurs. The aim of the research was to develop a predictive model that predicts the change in the form of a hardened workpiece as a function of the arbitrary set of strikes that deform the surface plastically. A large-scale laboratory experiment was carried out in which a database of 3063 samples was prepared, based on the controlled application of plastic deformations on the surface of the workpiece and high-resolution capture of the workpiece geometry. The different types of input data, describing, on the one hand, the performed plastic surface deformations on the workpieces, and on the other hand the point cloud of the workpiece geometry, were combined appropriately into a form that is a suitable input for a U-Net convolutional neural network. The U-Net model’s performance was investigated using three statistical indicators. These indicators were: relative absolute error (RAE), root mean squared error (RMSE), and relative squared error (RSE). The results showed that the model had excellent prediction performance, with the mean values of RMSE less than 0.013, RAE less than 0.05, and RSE less than 0.004 on test data. Based on the results, we concluded that the proposed model could be a useful tool for designing an optimal straightening strategy for high-hardness metal workpieces. Our results will open the doors to implementing digital sustainability techniques, since more efficient handling will result in fewer subsequent heat treatments and shorter handling times. An important goal of digital sustainability is to reduce electricity consumption in production, which this approach will certainly do.
Keywords: sustraightening process, hardened workpiece, manufacturing, U-Net convolutional neural network, modeling, point cloud, digital sustainability
Published in DKUM: 02.04.2024; Views: 275; Downloads: 32
Full text (10,52 MB)
This document has many files! More...

Abstract: Tema diplomskega dela je priprava tehnologije za nosilec turbine. V začetku sta predstavljeni obe tehnologiji, ki sta potrebni za izdelavo, in sicer rezkanje ter vrtanje. V nadaljevanju je podrobneje opisana izdelava tehniške dokumentacije, kjer je razvidna izbira materiala, izbira orodij za izdelavo ter tehnološki in delovni postopek. Sledi izdelava namenske vpenjalne priprave z modularnim T-utornim vpenjalnim modulom za prvo in drugo stran obdelave. V zadnjem delu pa je predstavljena obdelava, izbira obdelovalnega stroja, meritve in pa analiza dela, kjer je bila izračunana stroškovna kalkulacija ter pripravljalno zaključni čas ter čas obdelave.
Keywords: tehnološki postopki, rezkanje, vrtanje, modularna vpenjalna priprava
Published in DKUM: 01.03.2024; Views: 246; Downloads: 63
Full text (4,26 MB)

Abstract: V industriji se kovinski obdelovanci pogosto toplotno obdelujejo z namenom poboljšanja njihovih mehanskih lastnosti, pri čemer pa se pojavljajo neželene deformacije njihove geometrije. Zaradi dosežene visoke trdote (60 HRC ali več) klasični pristopi ravnanja z upogibanjem in valjanjem niso učinkoviti, saj se material poruši. V ta namen smo se v okviru doktorske raziskave ukvarjali z analizo vpliva plastičnih površinskih deformacij na spremembe geometrije kaljenih kovinskih obdelovancev. Izveden je bil laboratorijski eksperiment, v katerem smo na podlagi nadzorovanega vnosa površinskih plastičnih deformacij, visokoresolucijskega zajema geometrije obdelovancev, merjenja pospeškov ter zajema zvoka ravnalnih udarcev (udarci, ki plastično deformirajo površino obdelovanca) pripravili bazo podatkov s 3063 vzorci. Dodatno smo zajemali zvočne odzive nadzorovanih udarcev, ki ne deformirajo površine obdelovanca. Z uporabo U-Net nevronske mreže smo razvili model za napovedovanje spremembe geometrije kaljenega kovinskega obdelovanca glede na vnesene plastične površinske deformacije. V nadaljevanju smo predlagali novo arhitekturo globoke konvolucijske mreže za regresijo, ki omogoča dva vhoda različnih podatkovnih tipov in dimenzij (zvok ravnalnega udarca in reprezentacija geometrije obdelovanca z vključenimi podatki o ravnalnih udarcih) ter večdimenzionalni izhod (napovedana sprememba geometrije obdelovanca). Prav tako smo z uporabo globoke nevronske mreže razvili model, ki izključno na podlagi zvoka nadzorovano izvedenih udarcev, ki ne deformirajo površine obdelovanca, učinkovito napove geometrijo kovinskega obdelovanca. Uspešnost razvitih napovednih modelov smo ocenili z relativno absolutno napako (angl. relative absolute error (RAE)), povprečno kvadratno napako (angl. root mean squared error (RMSE)) in relativno kvadratno napako (angl. relative squared error (RSE)). Najuspešnejši model za napovedovanje oblike obdelovanca je imel na testnih podatkih odlično zmogljivost napovedovanja, saj so povprečne vrednosti RAE znašale 0,0499, RMSE 0,0129 in RSE 0,0040. Pri vključitvi zvoka v napovedovalni model so vrednosti RAE znašale 0,0739, RMSE 0,0185 in RSE 0,0075. Pri napovedi oblike obdelovanca samo iz zvoka pa so povprečne vrednosti RAE znašale 0,7439, RMSE 0,1744 in RSE 0,5638.
Keywords: proces ravnanja, kaljeni obdelovanec, proizvodnja, konvolucijska nevronska mreža, modeliranje, oblak točk, zvok
Published in DKUM: 06.10.2023; Views: 632; Downloads: 0