TANIKIĆ, Dejan ;MANIĆ, Miodrag ;DEVEDŽIĆ, Goran ;STEVIĆ, Zoran . Modelling Metal cutting Parameters Using Intelligent Techniques. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 56, n.1, p. 52-62, october 2017. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/sl/article/modelling-metal-cutting-parameters-using-intelligent-techniques/>. Date accessed: 19 nov. 2024. doi:http://dx.doi.org/.
Tanikić, D., Manić, M., Devedžić, G., & Stević, Z. (2010). Modelling Metal cutting Parameters Using Intelligent Techniques. Strojniški vestnik - Journal of Mechanical Engineering, 56(1), 52-62. doi:http://dx.doi.org/
@article{.,
author = {Dejan Tanikić and Miodrag Manić and Goran Devedžić and Zoran Stević},
title = {Modelling Metal cutting Parameters Using Intelligent Techniques},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {56},
number = {1},
year = {2010},
keywords = {metal cutting process; artificial neural networks; neuro-fuzzy model; },
abstract = {Cutting temperature, which depends on many factors, has a significant and, mostly negative influence on cutting process parameters. On the other hand, the quality of the machined surface is one of the most important qualitative indicators of a cutting process. Both parameters cannot be omitted in the modeling of metal cutting. Due to the high complexity of the process itself, it is almost impossible to encompass all the relevant factors and their influence within a mathematical formula. In such cases, it is much more efficient to use and process data obtained through experiments. Nowadays, systems that are based on artificial intelligence are often used for this purpose. The paper presents the application of the artificial neural networks and hybrid, neuro-fuzzy model in the prediction of a workpiece temperature and surface roughness. The approach is based on the thermographic method and infra red camera imaging system.},
issn = {0039-2480}, pages = {52-62}, doi = {},
url = {https://www.sv-jme.eu/sl/article/modelling-metal-cutting-parameters-using-intelligent-techniques/}
}
Tanikić, D.,Manić, M.,Devedžić, G.,Stević, Z. 2010 October 56. Modelling Metal cutting Parameters Using Intelligent Techniques. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 56:1
%A Tanikić, Dejan %A Manić, Miodrag %A Devedžić, Goran %A Stević, Zoran %D 2010 %T Modelling Metal cutting Parameters Using Intelligent Techniques %B 2010 %9 metal cutting process; artificial neural networks; neuro-fuzzy model; %! Modelling Metal cutting Parameters Using Intelligent Techniques %K metal cutting process; artificial neural networks; neuro-fuzzy model; %X Cutting temperature, which depends on many factors, has a significant and, mostly negative influence on cutting process parameters. On the other hand, the quality of the machined surface is one of the most important qualitative indicators of a cutting process. Both parameters cannot be omitted in the modeling of metal cutting. Due to the high complexity of the process itself, it is almost impossible to encompass all the relevant factors and their influence within a mathematical formula. In such cases, it is much more efficient to use and process data obtained through experiments. Nowadays, systems that are based on artificial intelligence are often used for this purpose. The paper presents the application of the artificial neural networks and hybrid, neuro-fuzzy model in the prediction of a workpiece temperature and surface roughness. The approach is based on the thermographic method and infra red camera imaging system. %U https://www.sv-jme.eu/sl/article/modelling-metal-cutting-parameters-using-intelligent-techniques/ %0 Journal Article %R %& 52 %P 11 %J Strojniški vestnik - Journal of Mechanical Engineering %V 56 %N 1 %@ 0039-2480 %8 2017-10-24 %7 2017-10-24
Tanikić, Dejan, Miodrag Manić, Goran Devedžić, & Zoran Stević. "Modelling Metal cutting Parameters Using Intelligent Techniques." Strojniški vestnik - Journal of Mechanical Engineering [Online], 56.1 (2010): 52-62. Web. 19 Nov. 2024
TY - JOUR AU - Tanikić, Dejan AU - Manić, Miodrag AU - Devedžić, Goran AU - Stević, Zoran PY - 2010 TI - Modelling Metal cutting Parameters Using Intelligent Techniques JF - Strojniški vestnik - Journal of Mechanical Engineering DO - KW - metal cutting process; artificial neural networks; neuro-fuzzy model; N2 - Cutting temperature, which depends on many factors, has a significant and, mostly negative influence on cutting process parameters. On the other hand, the quality of the machined surface is one of the most important qualitative indicators of a cutting process. Both parameters cannot be omitted in the modeling of metal cutting. Due to the high complexity of the process itself, it is almost impossible to encompass all the relevant factors and their influence within a mathematical formula. In such cases, it is much more efficient to use and process data obtained through experiments. Nowadays, systems that are based on artificial intelligence are often used for this purpose. The paper presents the application of the artificial neural networks and hybrid, neuro-fuzzy model in the prediction of a workpiece temperature and surface roughness. The approach is based on the thermographic method and infra red camera imaging system. UR - https://www.sv-jme.eu/sl/article/modelling-metal-cutting-parameters-using-intelligent-techniques/
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author = {Tanikić, D., Manić, M., Devedžić, G., Stević, Z.},
title = {Modelling Metal cutting Parameters Using Intelligent Techniques},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {56},
number = {1},
year = {2010},
doi = {},
url = {https://www.sv-jme.eu/sl/article/modelling-metal-cutting-parameters-using-intelligent-techniques/}
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TY - JOUR AU - Tanikić, Dejan AU - Manić, Miodrag AU - Devedžić, Goran AU - Stević, Zoran PY - 2017/10/24 TI - Modelling Metal cutting Parameters Using Intelligent Techniques JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 56, No 1 (2010): Strojniški vestnik - Journal of Mechanical Engineering DO - KW - metal cutting process, artificial neural networks, neuro-fuzzy model, N2 - Cutting temperature, which depends on many factors, has a significant and, mostly negative influence on cutting process parameters. On the other hand, the quality of the machined surface is one of the most important qualitative indicators of a cutting process. Both parameters cannot be omitted in the modeling of metal cutting. Due to the high complexity of the process itself, it is almost impossible to encompass all the relevant factors and their influence within a mathematical formula. In such cases, it is much more efficient to use and process data obtained through experiments. Nowadays, systems that are based on artificial intelligence are often used for this purpose. The paper presents the application of the artificial neural networks and hybrid, neuro-fuzzy model in the prediction of a workpiece temperature and surface roughness. The approach is based on the thermographic method and infra red camera imaging system. UR - https://www.sv-jme.eu/sl/article/modelling-metal-cutting-parameters-using-intelligent-techniques/
Tanikić, Dejan, Manić, Miodrag, Devedžić, Goran, AND Stević, Zoran. "Modelling Metal cutting Parameters Using Intelligent Techniques" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 56 Number 1 (24 October 2017)
Strojniški vestnik - Journal of Mechanical Engineering 56(2010)1, 52-62
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
Cutting temperature, which depends on many factors, has a significant and, mostly negative influence on cutting process parameters. On the other hand, the quality of the machined surface is one of the most important qualitative indicators of a cutting process. Both parameters cannot be omitted in the modeling of metal cutting. Due to the high complexity of the process itself, it is almost impossible to encompass all the relevant factors and their influence within a mathematical formula. In such cases, it is much more efficient to use and process data obtained through experiments. Nowadays, systems that are based on artificial intelligence are often used for this purpose. The paper presents the application of the artificial neural networks and hybrid, neuro-fuzzy model in the prediction of a workpiece temperature and surface roughness. The approach is based on the thermographic method and infra red camera imaging system.