Search the digital library catalog

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: 10
Full text (2,99 MB)
This document has many files! More...

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: 6
Full text (4,44 MB)
This document has many files! More...

Abstract: 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.
Keywords: laser cutting optimization, cut surface quality, dross formation, Latin hypercube sampling, feedforward neural network
Published in DKUM: 10.01.2025; Views: 0; Downloads: 23
Full text (3,38 MB)
This document has many files! More...

Abstract: V diplomskem delu je predstavljena prilagoditev orodnega vpenjalnega sistema CNC-stroja za krivljenje cevi, ki sem ga izdelal v času trimesečnega praktičnega usposabljanja v podjetju IMPOTT, d. o. o. CNC-stroj za krivljenje cevi s trnom sem predeloval zato, da se na stroju lahko uporabljajo orodja za krivljenje cevi, ki jih je podjetje uporabljalo že na predhodnem stroju. Problem je v drugačni višini valjčkov, orodij in vpenjalnikov na novem stroju. Ta problem smo rešili s konstrukcijo in izdelavo prilagoditve. Razvoj in izdelava sta se izvedli s pomočjo strojnega parka podjetja: CNC-stružnice Hyundai HiT 30S in CNC-rezkalnega centra VOC-850. Prilagoditev vsebuje konstrukcijo in izdelavo več posameznih delov: podložne plošče, nosilca valjčkov, nosilca vpenjalnika, vodil. Testiranje je pokazalo, da je bila prilagoditev uspešna.
Keywords: krivljenje cevi, vpenjanje orodja za krivljenje cevi, struženje, rezkanje
Published in DKUM: 30.09.2024; Views: 0; Downloads: 27
Full text (7,84 MB)

Abstract: V diplomskem delu smo opisali postopek obdelave materiala z obdelovalno tehnologijo potopne erozije ter postopek rezkanja elektrod. Vsak stroj, ki smo ga uporabili, je v zaključni nalogi opisan, opisane pa so tudi posamezne tehnologije, ki so bile uporabljene pri programiranju programa za potopno erozijo. Pri tej obdelavi smo uporabili dve elektrodi iz različnih materialov. Elektrode iz bakra smo izdelali sami, elektrode iz grafita pa smo naročili že izdelane. Oba načina potopne erozije smo med seboj primerjali iz strojniškega vidika ter iz vidika ekonomike, s tem smo optimizirali obdelovalno tehnologijo. Rezultati so dokazali, da je grafitna elektroda bolj primerna pri obdelavi z nižjo frekvenco ter večji hrapavosti, bakrena elektroda pa ima prednosti pri obdelavi z višjo frekvenco in izdelavo enostavnejših oblik. Težave, ki so nastale pri posamezni tehnologiji obdelovanja, jih rešili in tako pripomogli k optimiziranju proizvodnje.
Keywords: potopna erozija, grafitna elektroda, bakrena elektroda, stroški izdelave
Published in DKUM: 05.10.2023; Views: 314; Downloads: 53
Full text (3,77 MB)