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/article/study-of-adaptive-model-parameter-estimation-for-milling-tool-wear/>. Date accessed: 20 dec. 2024. 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/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/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. 20 Dec. 2024
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/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/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/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)
Strojniški vestnik - Journal of Mechanical Engineering 57(2011)7-8, 568-578
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
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.