Iskanje po katalogu digitalne knjižnice

Opis: 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
Ključne besede: smart production machines, data-driven manufacturing, machine learning algorithms, CNC controller data, geometrical accuracy
Objavljeno v DKUM: 10.03.2025; Ogledov: 0; Prenosov: 8
Celotno besedilo (4,44 MB)
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Opis: Optimizing cutting parameters in fiber laser cutting of austenitic stainless steel is challenging due to the complex interplay of multiple variables and quality metrics. To solve this problem, Latin hypercube sampling was used to ensure a comprehensive and efficient exploration of the parameter space with a smaller number of trials (185), coupled with feedforward neural networks for predictive modeling. The networks were trained with a leave-oneout cross-validation strategy to mitigate overfitting. Different configurations of hidden layers, neurons, and training functions were used. The approach was focused on minimizing dross and roughness on both the top and bottom areas of the cut surfaces. During the testing phase, an average MSE of 0.063 and an average MAPE of 4.68% were achieved by the models. Additionally, an experimental test was performed on the best parameter settings predicted by the models. Initial modelling was conducted for each quality metric individually, resulting in an average percentage difference of 1.37% between predicted and actual results. Grid search was also per formed to determine an optimal input parameter set for all outputs, with predictions achieving an average ac curacy of 98.34%. Experimental validation confirmed the accuracy and robustness of the model predictions, demonstrating the effectiveness of the methodology in optimizing multiple parameters of complex laser cutting processes.
Ključne besede: laser cutting optimization, cut surface quality, dross formation, Latin hypercube sampling, feedforward neural network
Objavljeno v DKUM: 10.01.2025; Ogledov: 0; Prenosov: 23
Celotno besedilo (3,38 MB)
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Opis: Diplomsko delo zajema reševanje problema v sistemu prenosa in distribucije električne energije. Proučevali smo atmosferske vplive in vplive odvzema električne energije iz sistema na temperaturo in poves električnih vodnikov ter napetosti in vibracije v podporni konstrukciji. Za modeliranje smo uporabili metodo genetskih algoritmov, ki temeljijo na darvinističnih izsledkih o razvoju vrst in posnemajo principe naravne selekcije. Razviti modeli omogočajo študij teže posameznega vpliva. Namen dobljenih modelov je določanje najmočnejših vplivov in možnost napovedovanja vrednosti odzivov za daljše časovno obdobje in s tem ocenjevanje življenjske dobe daljnovodov. Rezultat našega dela so napovedni modeli z visoko stopnjo prilagojenosti na učni in testni množici. Modeli podajajo natančne napovedi, predvsem za temperaturo električnih vodnikov in njihov poves. Nižjo stopnjo natančnosti napovedi pri napetostih in vibracijah v stebrih pripisujemo dejstvu, da na te odzive vplivajo tudi obremenitve na konstrukciji in lastnosti materiala.
Ključne besede: električni vodniki, stebri, napetosti, vibracije, genetski algoritmi, modeliranje in optimizacija.
Objavljeno v DKUM: 10.09.2020; Ogledov: 1267; Prenosov: 402
Celotno besedilo (1,20 MB)