TY  - JOUR
AU  - Mirić-Milosavljević, Mira
AU  - Svrzić, Srđan
AU  - Nikolić, Zoran
AU  - Đurković, Marija
AU  - Furtula, Mladen
AU  - Dedić, Aleksandar
PY  - 2024
UR  - https://omorika.sfb.bg.ac.rs/handle/123456789/1504
AB  - This study examines the possible utilization of machine learning and decision-making in the woodworking sector. This refers to the recognition of certain sounds produced during tool idling. The physical and geometric properties of the circular saw blade result in different noises being generated during idling. It was assumed that the respective circular saw blades can be recognized by these noises. The noises of three different circular saw blades were examined while idling at the same speed. In order to obtain useful data for the deep learning process, the coarse signals were subjected to frequency analysis. A total of 240 noise samples were taken for each circular saw blade and later subjected to signal processing. Frequency-power spectra were created using a custom program in Matlab Campus Edition software, such as for the spectrograms. A short Fourier transform was used to create the average spectral density plot using self-made software. The input data for the deep learning network was created in Matlab using a custom program. The GoogleNet deep learning network was used as a data classifier. After training the network, an accuracy of 97.5% was achieved in recognizing circular saw blades.
T2  - BioResources
T1  - Signal Processing and Machine Learning as a Tool for Identifying Idling Noises of Different Circular Saw Blades
EP  - 1756
IS  - 1
SP  - 1744
VL  - 19
DO  - 10.15376/biores.19.1.1744-1756
UR  - conv_1768
ER  - 

@article{
author = "Mirić-Milosavljević, Mira and Svrzić, Srđan and Nikolić, Zoran and Đurković, Marija and Furtula, Mladen and Dedić, Aleksandar",
year = "2024",
abstract = "This study examines the possible utilization of machine learning and decision-making in the woodworking sector. This refers to the recognition of certain sounds produced during tool idling. The physical and geometric properties of the circular saw blade result in different noises being generated during idling. It was assumed that the respective circular saw blades can be recognized by these noises. The noises of three different circular saw blades were examined while idling at the same speed. In order to obtain useful data for the deep learning process, the coarse signals were subjected to frequency analysis. A total of 240 noise samples were taken for each circular saw blade and later subjected to signal processing. Frequency-power spectra were created using a custom program in Matlab Campus Edition software, such as for the spectrograms. A short Fourier transform was used to create the average spectral density plot using self-made software. The input data for the deep learning network was created in Matlab using a custom program. The GoogleNet deep learning network was used as a data classifier. After training the network, an accuracy of 97.5% was achieved in recognizing circular saw blades.",
journal = "BioResources",
title = "Signal Processing and Machine Learning as a Tool for Identifying Idling Noises of Different Circular Saw Blades",
pages = "1756-1744",
number = "1",
volume = "19",
doi = "10.15376/biores.19.1.1744-1756",
url = "conv_1768"
}

Mirić-Milosavljević, M., Svrzić, S., Nikolić, Z., Đurković, M., Furtula, M.,& Dedić, A.. (2024). Signal Processing and Machine Learning as a Tool for Identifying Idling Noises of Different Circular Saw Blades. in BioResources, 19(1), 1744-1756.
https://doi.org/10.15376/biores.19.1.1744-1756
conv_1768

Mirić-Milosavljević M, Svrzić S, Nikolić Z, Đurković M, Furtula M, Dedić A. Signal Processing and Machine Learning as a Tool for Identifying Idling Noises of Different Circular Saw Blades. in BioResources. 2024;19(1):1744-1756.
doi:10.15376/biores.19.1.1744-1756
conv_1768 .

Mirić-Milosavljević, Mira, Svrzić, Srđan, Nikolić, Zoran, Đurković, Marija, Furtula, Mladen, Dedić, Aleksandar, "Signal Processing and Machine Learning as a Tool for Identifying Idling Noises of Different Circular Saw Blades" in BioResources, 19, no. 1 (2024):1744-1756,
https://doi.org/10.15376/biores.19.1.1744-1756 .,
conv_1768 .

TY  - JOUR
AU  - Svrzić, Srđan
AU  - Đurković, Marija
AU  - Vukicević, Arso
AU  - Nikolić, Zoran
AU  - Mihailović, Vladislava
AU  - Dedić, Aleksandar
PY  - 2024
UR  - https://omorika.sfb.bg.ac.rs/handle/123456789/1514
AB  - Non-contact process monitoring could be a powerful tool to prevent tool misuse, detect wood species, detect tool dullness and reduce electrical energy consumption-all of which could reduce production costs. The aim of this study is to identify recognizable patterns in the sound signals produced during the circular sawing of two different wood species-beech (Fagus moesiaca) and fir (Abies alba)-and to classify them in order to obtain an intelligent machining process capable of recognizing the wood species being machined. These two wood species were selected for this study due to their morphological, physical and mechanical differences. The cutting power was also recorded during the process and measured indirectly via the motor power used. A sound signal can easily be converted into an image (spectrogram), which is suitable as a data basis for the deep learning process. Several neural networks were used to classify the sounds. In order to prepare the raw audio signal for machine learning using image recognition, it was processed in several steps. The relationship between the audio and the recorded cutting power was also investigated and found to be strongly correlated, but only for audio frequencies up to 4500 Hz. Based on the results and further analysis, the classification accuracy for wood species identification varied between 98% for MobileNetV2 and 94% for the InceptionV3 deep learning network.
T2  - European Journal of Wood and Wood Products
T1  - Sound classification and power consumption to sound intensity relation as a tool for wood machining monitoring
DO  - 10.1007/s00107-024-02139-2
UR  - conv_1817
ER  - 

@article{
author = "Svrzić, Srđan and Đurković, Marija and Vukicević, Arso and Nikolić, Zoran and Mihailović, Vladislava and Dedić, Aleksandar",
year = "2024",
abstract = "Non-contact process monitoring could be a powerful tool to prevent tool misuse, detect wood species, detect tool dullness and reduce electrical energy consumption-all of which could reduce production costs. The aim of this study is to identify recognizable patterns in the sound signals produced during the circular sawing of two different wood species-beech (Fagus moesiaca) and fir (Abies alba)-and to classify them in order to obtain an intelligent machining process capable of recognizing the wood species being machined. These two wood species were selected for this study due to their morphological, physical and mechanical differences. The cutting power was also recorded during the process and measured indirectly via the motor power used. A sound signal can easily be converted into an image (spectrogram), which is suitable as a data basis for the deep learning process. Several neural networks were used to classify the sounds. In order to prepare the raw audio signal for machine learning using image recognition, it was processed in several steps. The relationship between the audio and the recorded cutting power was also investigated and found to be strongly correlated, but only for audio frequencies up to 4500 Hz. Based on the results and further analysis, the classification accuracy for wood species identification varied between 98% for MobileNetV2 and 94% for the InceptionV3 deep learning network.",
journal = "European Journal of Wood and Wood Products",
title = "Sound classification and power consumption to sound intensity relation as a tool for wood machining monitoring",
doi = "10.1007/s00107-024-02139-2",
url = "conv_1817"
}

Svrzić, S., Đurković, M., Vukicević, A., Nikolić, Z., Mihailović, V.,& Dedić, A.. (2024). Sound classification and power consumption to sound intensity relation as a tool for wood machining monitoring. in European Journal of Wood and Wood Products.
https://doi.org/10.1007/s00107-024-02139-2
conv_1817

Svrzić S, Đurković M, Vukicević A, Nikolić Z, Mihailović V, Dedić A. Sound classification and power consumption to sound intensity relation as a tool for wood machining monitoring. in European Journal of Wood and Wood Products. 2024;.
doi:10.1007/s00107-024-02139-2
conv_1817 .

Svrzić, Srđan, Đurković, Marija, Vukicević, Arso, Nikolić, Zoran, Mihailović, Vladislava, Dedić, Aleksandar, "Sound classification and power consumption to sound intensity relation as a tool for wood machining monitoring" in European Journal of Wood and Wood Products (2024),
https://doi.org/10.1007/s00107-024-02139-2 .,
conv_1817 .