Development of a Neural-Networks Tool-Wear Monitoring System for a Turning Process

888 Ogledov
673 Prenosov
Izvoz citacije: ABNT
ANTIĆ, Aco ;HODOLIČ, Janko ;SOKOVIĆ, Mirko .
Development of a Neural-Networks Tool-Wear Monitoring System for a Turning Process. 
Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 52, n.11, p. 763-776, august 2017. 
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/sl/article/development-of-a-neural-networks-tool-wear-monitoring-system-for-a-turning-process/>. Date accessed: 03 dec. 2020. 
doi:http://dx.doi.org/.
Antić, A., Hodolič, J., & Soković, M.
(2006).
Development of a Neural-Networks Tool-Wear Monitoring System for a Turning Process.
Strojniški vestnik - Journal of Mechanical Engineering, 52(11), 763-776.
doi:http://dx.doi.org/
@article{.,
	author = {Aco  Antić and Janko  Hodolič and Mirko  Soković},
	title = {Development of a Neural-Networks Tool-Wear Monitoring System for a Turning Process},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {52},
	number = {11},
	year = {2006},
	keywords = {neural networks; cutting forces; turning; tool wear monitoring; },
	abstract = {This paper presents the results of developing a tool-wear monitoring system for hard turning in laboratory conditions. The system is based on modern artificial intelligence methods such as neural networks (NNs). One of the most dominant factors influencing the reliability of the turning process is the condition of the tool; thus, systems for monitoring tool conditions have been developed both in practice and in the laboratory. The paper describes research connected to the selection of methods and strategies for determining the tool-wear condition after turning on the basis of a set laboratory system model. The tool monitoring is performed by an indirect method on the basis of cutting force as one of best determiners of tool condition in the online working regime, combined with one of the artificial intelligence methods, i.e. neural networks. The paper also presents the topology of the neural network used for the training.},
	issn = {0039-2480},	pages = {763-776},	doi = {},
	url = {https://www.sv-jme.eu/sl/article/development-of-a-neural-networks-tool-wear-monitoring-system-for-a-turning-process/}
}
Antić, A.,Hodolič, J.,Soković, M.
2006 August 52. Development of a Neural-Networks Tool-Wear Monitoring System for a Turning Process. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 52:11
%A Antić, Aco 
%A Hodolič, Janko 
%A Soković, Mirko 
%D 2006
%T Development of a Neural-Networks Tool-Wear Monitoring System for a Turning Process
%B 2006
%9 neural networks; cutting forces; turning; tool wear monitoring; 
%! Development of a Neural-Networks Tool-Wear Monitoring System for a Turning Process
%K neural networks; cutting forces; turning; tool wear monitoring; 
%X This paper presents the results of developing a tool-wear monitoring system for hard turning in laboratory conditions. The system is based on modern artificial intelligence methods such as neural networks (NNs). One of the most dominant factors influencing the reliability of the turning process is the condition of the tool; thus, systems for monitoring tool conditions have been developed both in practice and in the laboratory. The paper describes research connected to the selection of methods and strategies for determining the tool-wear condition after turning on the basis of a set laboratory system model. The tool monitoring is performed by an indirect method on the basis of cutting force as one of best determiners of tool condition in the online working regime, combined with one of the artificial intelligence methods, i.e. neural networks. The paper also presents the topology of the neural network used for the training.
%U https://www.sv-jme.eu/sl/article/development-of-a-neural-networks-tool-wear-monitoring-system-for-a-turning-process/
%0 Journal Article
%R 
%& 763
%P 14
%J Strojniški vestnik - Journal of Mechanical Engineering
%V 52
%N 11
%@ 0039-2480
%8 2017-08-18
%7 2017-08-18
Antić, Aco, Janko  Hodolič, & Mirko  Soković.
"Development of a Neural-Networks Tool-Wear Monitoring System for a Turning Process." Strojniški vestnik - Journal of Mechanical Engineering [Online], 52.11 (2006): 763-776. Web.  03 Dec. 2020
TY  - JOUR
AU  - Antić, Aco 
AU  - Hodolič, Janko 
AU  - Soković, Mirko 
PY  - 2006
TI  - Development of a Neural-Networks Tool-Wear Monitoring System for a Turning Process
JF  - Strojniški vestnik - Journal of Mechanical Engineering
DO  - 
KW  - neural networks; cutting forces; turning; tool wear monitoring; 
N2  - This paper presents the results of developing a tool-wear monitoring system for hard turning in laboratory conditions. The system is based on modern artificial intelligence methods such as neural networks (NNs). One of the most dominant factors influencing the reliability of the turning process is the condition of the tool; thus, systems for monitoring tool conditions have been developed both in practice and in the laboratory. The paper describes research connected to the selection of methods and strategies for determining the tool-wear condition after turning on the basis of a set laboratory system model. The tool monitoring is performed by an indirect method on the basis of cutting force as one of best determiners of tool condition in the online working regime, combined with one of the artificial intelligence methods, i.e. neural networks. The paper also presents the topology of the neural network used for the training.
UR  - https://www.sv-jme.eu/sl/article/development-of-a-neural-networks-tool-wear-monitoring-system-for-a-turning-process/
@article{{}{.},
	author = {Antić, A., Hodolič, J., Soković, M.},
	title = {Development of a Neural-Networks Tool-Wear Monitoring System for a Turning Process},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {52},
	number = {11},
	year = {2006},
	doi = {},
	url = {https://www.sv-jme.eu/sl/article/development-of-a-neural-networks-tool-wear-monitoring-system-for-a-turning-process/}
}
TY  - JOUR
AU  - Antić, Aco 
AU  - Hodolič, Janko 
AU  - Soković, Mirko 
PY  - 2017/08/18
TI  - Development of a Neural-Networks Tool-Wear Monitoring System for a Turning Process
JF  - Strojniški vestnik - Journal of Mechanical Engineering; Vol 52, No 11 (2006): Strojniški vestnik - Journal of Mechanical Engineering
DO  - 
KW  - neural networks, cutting forces, turning, tool wear monitoring, 
N2  - This paper presents the results of developing a tool-wear monitoring system for hard turning in laboratory conditions. The system is based on modern artificial intelligence methods such as neural networks (NNs). One of the most dominant factors influencing the reliability of the turning process is the condition of the tool; thus, systems for monitoring tool conditions have been developed both in practice and in the laboratory. The paper describes research connected to the selection of methods and strategies for determining the tool-wear condition after turning on the basis of a set laboratory system model. The tool monitoring is performed by an indirect method on the basis of cutting force as one of best determiners of tool condition in the online working regime, combined with one of the artificial intelligence methods, i.e. neural networks. The paper also presents the topology of the neural network used for the training.
UR  - https://www.sv-jme.eu/sl/article/development-of-a-neural-networks-tool-wear-monitoring-system-for-a-turning-process/
Antić, Aco, Hodolič, Janko, AND Soković, Mirko.
"Development of a Neural-Networks Tool-Wear Monitoring System for a Turning Process" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 52 Number 11 (18 August 2017)

Avtorji

Inštitucije

  • University of Novi Sad, Faculty of Technical Sciences, Serbia
  • University of Novi Sad, Faculty of Technical Sciences
  • University of Ljubljana, Faculty of Mechanical Engineering, Slovenia

Informacije o papirju

Strojniški vestnik - Journal of Mechanical Engineering 52(2006)11, 763-776

This paper presents the results of developing a tool-wear monitoring system for hard turning in laboratory conditions. The system is based on modern artificial intelligence methods such as neural networks (NNs). One of the most dominant factors influencing the reliability of the turning process is the condition of the tool; thus, systems for monitoring tool conditions have been developed both in practice and in the laboratory. The paper describes research connected to the selection of methods and strategies for determining the tool-wear condition after turning on the basis of a set laboratory system model. The tool monitoring is performed by an indirect method on the basis of cutting force as one of best determiners of tool condition in the online working regime, combined with one of the artificial intelligence methods, i.e. neural networks. The paper also presents the topology of the neural network used for the training.

neural networks; cutting forces; turning; tool wear monitoring;