Study of Adaptive Model Parameter Estimation for Milling Tool Wear

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Izvoz citacije: ABNT
XU, Chuangwen ;XU, Ting ;ZHU, Qi ;ZHANG, Hongyan .
Study of Adaptive Model Parameter Estimation for Milling Tool Wear. 
Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 57, n.7-8, p. 568-578, june 2018. 
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/sl/article/study-of-adaptive-model-parameter-estimation-for-milling-tool-wear/>. Date accessed: 06 jul. 2020. 
doi:http://dx.doi.org/10.5545/sv-jme.2009.138.
Xu, C., Xu, T., Zhu, Q., & Zhang, H.
(2011).
Study of Adaptive Model Parameter Estimation for Milling Tool Wear.
Strojniški vestnik - Journal of Mechanical Engineering, 57(7-8), 568-578.
doi:http://dx.doi.org/10.5545/sv-jme.2009.138
@article{sv-jmesv-jme.2009.138,
	author = {Chuangwen  Xu and Ting  Xu and Qi  Zhu and Hongyan  Zhang},
	title = {Study of Adaptive Model Parameter Estimation for Milling Tool Wear},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {57},
	number = {7-8},
	year = {2011},
	keywords = {milling power; adaptive estimation model; tool wear; model parameters; information fusion},
	abstract = {In a modern machining system, tool wear monitoring systems are needed to get higher quality production. In precision machining processes, especially surface quality of the manufactured part can be related to tool wear. This increases industrial interest for in-process tool wear monitoring systems. For the modern unmanned manufacturing process, an integrated system composed of sensors, signal processing interface and intelligent decision making model are required. In this study, a new method for on-line tool wear monitoring is presented under varying cutting conditions. The proposed method uses wear feature extraction based on process modeling and parameter estimation. An adaptive estimation model of milling tool wear in variable cutting parameters is built based entirely on milling power. The adaptive model traces the properties of cutting process by combining process state signal, cutting conditions, power model. The tool wear feature is obtained from the estimated parameters of the model and carried on in the theoretical and experimental study. Experiment results have proved that changes of the parameters in the cutting power model significantly indicate tool wear independently of varying cutting conditions and it makes tool wear a recognized process with high precision.},
	issn = {0039-2480},	pages = {568-578},	doi = {10.5545/sv-jme.2009.138},
	url = {https://www.sv-jme.eu/sl/article/study-of-adaptive-model-parameter-estimation-for-milling-tool-wear/}
}
Xu, C.,Xu, T.,Zhu, Q.,Zhang, H.
2011 June 57. Study of Adaptive Model Parameter Estimation for Milling Tool Wear. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 57:7-8
%A Xu, Chuangwen 
%A Xu, Ting 
%A Zhu, Qi 
%A Zhang, Hongyan 
%D 2011
%T Study of Adaptive Model Parameter Estimation for Milling Tool Wear
%B 2011
%9 milling power; adaptive estimation model; tool wear; model parameters; information fusion
%! Study of Adaptive Model Parameter Estimation for Milling Tool Wear
%K milling power; adaptive estimation model; tool wear; model parameters; information fusion
%X In a modern machining system, tool wear monitoring systems are needed to get higher quality production. In precision machining processes, especially surface quality of the manufactured part can be related to tool wear. This increases industrial interest for in-process tool wear monitoring systems. For the modern unmanned manufacturing process, an integrated system composed of sensors, signal processing interface and intelligent decision making model are required. In this study, a new method for on-line tool wear monitoring is presented under varying cutting conditions. The proposed method uses wear feature extraction based on process modeling and parameter estimation. An adaptive estimation model of milling tool wear in variable cutting parameters is built based entirely on milling power. The adaptive model traces the properties of cutting process by combining process state signal, cutting conditions, power model. The tool wear feature is obtained from the estimated parameters of the model and carried on in the theoretical and experimental study. Experiment results have proved that changes of the parameters in the cutting power model significantly indicate tool wear independently of varying cutting conditions and it makes tool wear a recognized process with high precision.
%U https://www.sv-jme.eu/sl/article/study-of-adaptive-model-parameter-estimation-for-milling-tool-wear/
%0 Journal Article
%R 10.5545/sv-jme.2009.138
%& 568
%P 11
%J Strojniški vestnik - Journal of Mechanical Engineering
%V 57
%N 7-8
%@ 0039-2480
%8 2018-06-29
%7 2018-06-29
Xu, Chuangwen, Ting  Xu, Qi  Zhu, & Hongyan  Zhang.
"Study of Adaptive Model Parameter Estimation for Milling Tool Wear." Strojniški vestnik - Journal of Mechanical Engineering [Online], 57.7-8 (2011): 568-578. Web.  06 Jul. 2020
TY  - JOUR
AU  - Xu, Chuangwen 
AU  - Xu, Ting 
AU  - Zhu, Qi 
AU  - Zhang, Hongyan 
PY  - 2011
TI  - Study of Adaptive Model Parameter Estimation for Milling Tool Wear
JF  - Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2009.138
KW  - milling power; adaptive estimation model; tool wear; model parameters; information fusion
N2  - In a modern machining system, tool wear monitoring systems are needed to get higher quality production. In precision machining processes, especially surface quality of the manufactured part can be related to tool wear. This increases industrial interest for in-process tool wear monitoring systems. For the modern unmanned manufacturing process, an integrated system composed of sensors, signal processing interface and intelligent decision making model are required. In this study, a new method for on-line tool wear monitoring is presented under varying cutting conditions. The proposed method uses wear feature extraction based on process modeling and parameter estimation. An adaptive estimation model of milling tool wear in variable cutting parameters is built based entirely on milling power. The adaptive model traces the properties of cutting process by combining process state signal, cutting conditions, power model. The tool wear feature is obtained from the estimated parameters of the model and carried on in the theoretical and experimental study. Experiment results have proved that changes of the parameters in the cutting power model significantly indicate tool wear independently of varying cutting conditions and it makes tool wear a recognized process with high precision.
UR  - https://www.sv-jme.eu/sl/article/study-of-adaptive-model-parameter-estimation-for-milling-tool-wear/
@article{{sv-jme}{sv-jme.2009.138},
	author = {Xu, C., Xu, T., Zhu, Q., Zhang, H.},
	title = {Study of Adaptive Model Parameter Estimation for Milling Tool Wear},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {57},
	number = {7-8},
	year = {2011},
	doi = {10.5545/sv-jme.2009.138},
	url = {https://www.sv-jme.eu/sl/article/study-of-adaptive-model-parameter-estimation-for-milling-tool-wear/}
}
TY  - JOUR
AU  - Xu, Chuangwen 
AU  - Xu, Ting 
AU  - Zhu, Qi 
AU  - Zhang, Hongyan 
PY  - 2018/06/29
TI  - Study of Adaptive Model Parameter Estimation for Milling Tool Wear
JF  - Strojniški vestnik - Journal of Mechanical Engineering; Vol 57, No 7-8 (2011): Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2009.138
KW  - milling power, adaptive estimation model, tool wear, model parameters, information fusion
N2  - In a modern machining system, tool wear monitoring systems are needed to get higher quality production. In precision machining processes, especially surface quality of the manufactured part can be related to tool wear. This increases industrial interest for in-process tool wear monitoring systems. For the modern unmanned manufacturing process, an integrated system composed of sensors, signal processing interface and intelligent decision making model are required. In this study, a new method for on-line tool wear monitoring is presented under varying cutting conditions. The proposed method uses wear feature extraction based on process modeling and parameter estimation. An adaptive estimation model of milling tool wear in variable cutting parameters is built based entirely on milling power. The adaptive model traces the properties of cutting process by combining process state signal, cutting conditions, power model. The tool wear feature is obtained from the estimated parameters of the model and carried on in the theoretical and experimental study. Experiment results have proved that changes of the parameters in the cutting power model significantly indicate tool wear independently of varying cutting conditions and it makes tool wear a recognized process with high precision.
UR  - https://www.sv-jme.eu/sl/article/study-of-adaptive-model-parameter-estimation-for-milling-tool-wear/
Xu, Chuangwen, Xu, Ting, Zhu, Qi, AND Zhang, Hongyan.
"Study of Adaptive Model Parameter Estimation for Milling Tool Wear" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 57 Number 7-8 (29 June 2018)

Avtorji

Inštitucije

  • Lanzhou Polytechnic College, Qilihequ, Gongjiawan 1, Lanzhou, Gansu 1
  • School of Electronic Engineering, Jilin University 2

Informacije o papirju

Strojniški vestnik - Journal of Mechanical Engineering 57(2011)7-8, 568-578

https://doi.org/10.5545/sv-jme.2009.138

In a modern machining system, tool wear monitoring systems are needed to get higher quality production. In precision machining processes, especially surface quality of the manufactured part can be related to tool wear. This increases industrial interest for in-process tool wear monitoring systems. For the modern unmanned manufacturing process, an integrated system composed of sensors, signal processing interface and intelligent decision making model are required. In this study, a new method for on-line tool wear monitoring is presented under varying cutting conditions. The proposed method uses wear feature extraction based on process modeling and parameter estimation. An adaptive estimation model of milling tool wear in variable cutting parameters is built based entirely on milling power. The adaptive model traces the properties of cutting process by combining process state signal, cutting conditions, power model. The tool wear feature is obtained from the estimated parameters of the model and carried on in the theoretical and experimental study. Experiment results have proved that changes of the parameters in the cutting power model significantly indicate tool wear independently of varying cutting conditions and it makes tool wear a recognized process with high precision.

milling power; adaptive estimation model; tool wear; model parameters; information fusion