ČUŠ, Franci ;ŽUPERL, Uroš . Real-Time Cutting Tool Condition Monitoring in Milling. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 57, n.2, p. 142-150, june 2018. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/article/real-time-cutting-tool-condition-monitoring-in-milling/>. Date accessed: 22 dec. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2010.079.
Čuš, F., & Župerl, U. (2011). Real-Time Cutting Tool Condition Monitoring in Milling. Strojniški vestnik - Journal of Mechanical Engineering, 57(2), 142-150. doi:http://dx.doi.org/10.5545/sv-jme.2010.079
@article{sv-jmesv-jme.2010.079,
author = {Franci Čuš and Uroš Župerl},
title = {Real-Time Cutting Tool Condition Monitoring in Milling},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {57},
number = {2},
year = {2011},
keywords = {end-milling; tool condition monitoring (TCM); wear estimation; ANFIS},
abstract = {Reliable tool wear monitoring system is one of the important aspects for achieving a self-adjusting manufacturing system. The original contribution of the research is the developed monitoring system that can detect tool breakage in real time by using a combination of neural decision system and ANFIS tool wear estimator. The principal presumption was that force signals contain the most useful information for determining the tool condition. Therefore, the ANFIS method is used to extract the features of tool states from cutting force signals. ANFIS method seeks to provide a linguistic model for the estimation of tool wear from the knowledge embedded in the artificial neural network. The ANFIS method uses the relationship between flank wear and the resultant cutting force to estimate tool wear. A series of experiments were conducted to determine the relationship between flank wear and cutting force as well as cutting parameters. Speed, feed, depth of cutting, time and cutting forces were used as input parameters and flank wear width and tool state were output parameters. The forces were measured using a piezoelectric dynamometer and data acquisition system. Simultaneously flank wear at the cutting edge was monitored by using a tool maker’s microscope. The experimental force and wear data were utilized to train the developed simulation environment based on ANFIS modelling. The artificial neural network, was also used to discriminate different malfunction states from measured signals. By developed tool monitoring system (TCM) the machining process can be on-line monitored and stopped for tool change based on a pre-set tool-wear limit. The fundamental limitation of research was to develop a singlesensor monitoring system, reliable as commercially available system, but 80% cheaper than multisensor approach.},
issn = {0039-2480}, pages = {142-150}, doi = {10.5545/sv-jme.2010.079},
url = {https://www.sv-jme.eu/article/real-time-cutting-tool-condition-monitoring-in-milling/}
}
Čuš, F.,Župerl, U. 2011 June 57. Real-Time Cutting Tool Condition Monitoring in Milling. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 57:2
%A Čuš, Franci %A Župerl, Uroš %D 2011 %T Real-Time Cutting Tool Condition Monitoring in Milling %B 2011 %9 end-milling; tool condition monitoring (TCM); wear estimation; ANFIS %! Real-Time Cutting Tool Condition Monitoring in Milling %K end-milling; tool condition monitoring (TCM); wear estimation; ANFIS %X Reliable tool wear monitoring system is one of the important aspects for achieving a self-adjusting manufacturing system. The original contribution of the research is the developed monitoring system that can detect tool breakage in real time by using a combination of neural decision system and ANFIS tool wear estimator. The principal presumption was that force signals contain the most useful information for determining the tool condition. Therefore, the ANFIS method is used to extract the features of tool states from cutting force signals. ANFIS method seeks to provide a linguistic model for the estimation of tool wear from the knowledge embedded in the artificial neural network. The ANFIS method uses the relationship between flank wear and the resultant cutting force to estimate tool wear. A series of experiments were conducted to determine the relationship between flank wear and cutting force as well as cutting parameters. Speed, feed, depth of cutting, time and cutting forces were used as input parameters and flank wear width and tool state were output parameters. The forces were measured using a piezoelectric dynamometer and data acquisition system. Simultaneously flank wear at the cutting edge was monitored by using a tool maker’s microscope. The experimental force and wear data were utilized to train the developed simulation environment based on ANFIS modelling. The artificial neural network, was also used to discriminate different malfunction states from measured signals. By developed tool monitoring system (TCM) the machining process can be on-line monitored and stopped for tool change based on a pre-set tool-wear limit. The fundamental limitation of research was to develop a singlesensor monitoring system, reliable as commercially available system, but 80% cheaper than multisensor approach. %U https://www.sv-jme.eu/article/real-time-cutting-tool-condition-monitoring-in-milling/ %0 Journal Article %R 10.5545/sv-jme.2010.079 %& 142 %P 9 %J Strojniški vestnik - Journal of Mechanical Engineering %V 57 %N 2 %@ 0039-2480 %8 2018-06-28 %7 2018-06-28
Čuš, Franci, & Uroš Župerl. "Real-Time Cutting Tool Condition Monitoring in Milling." Strojniški vestnik - Journal of Mechanical Engineering [Online], 57.2 (2011): 142-150. Web. 22 Dec. 2024
TY - JOUR AU - Čuš, Franci AU - Župerl, Uroš PY - 2011 TI - Real-Time Cutting Tool Condition Monitoring in Milling JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2010.079 KW - end-milling; tool condition monitoring (TCM); wear estimation; ANFIS N2 - Reliable tool wear monitoring system is one of the important aspects for achieving a self-adjusting manufacturing system. The original contribution of the research is the developed monitoring system that can detect tool breakage in real time by using a combination of neural decision system and ANFIS tool wear estimator. The principal presumption was that force signals contain the most useful information for determining the tool condition. Therefore, the ANFIS method is used to extract the features of tool states from cutting force signals. ANFIS method seeks to provide a linguistic model for the estimation of tool wear from the knowledge embedded in the artificial neural network. The ANFIS method uses the relationship between flank wear and the resultant cutting force to estimate tool wear. A series of experiments were conducted to determine the relationship between flank wear and cutting force as well as cutting parameters. Speed, feed, depth of cutting, time and cutting forces were used as input parameters and flank wear width and tool state were output parameters. The forces were measured using a piezoelectric dynamometer and data acquisition system. Simultaneously flank wear at the cutting edge was monitored by using a tool maker’s microscope. The experimental force and wear data were utilized to train the developed simulation environment based on ANFIS modelling. The artificial neural network, was also used to discriminate different malfunction states from measured signals. By developed tool monitoring system (TCM) the machining process can be on-line monitored and stopped for tool change based on a pre-set tool-wear limit. The fundamental limitation of research was to develop a singlesensor monitoring system, reliable as commercially available system, but 80% cheaper than multisensor approach. UR - https://www.sv-jme.eu/article/real-time-cutting-tool-condition-monitoring-in-milling/
@article{{sv-jme}{sv-jme.2010.079},
author = {Čuš, F., Župerl, U.},
title = {Real-Time Cutting Tool Condition Monitoring in Milling},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {57},
number = {2},
year = {2011},
doi = {10.5545/sv-jme.2010.079},
url = {https://www.sv-jme.eu/article/real-time-cutting-tool-condition-monitoring-in-milling/}
}
TY - JOUR AU - Čuš, Franci AU - Župerl, Uroš PY - 2018/06/28 TI - Real-Time Cutting Tool Condition Monitoring in Milling JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 57, No 2 (2011): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2010.079 KW - end-milling, tool condition monitoring (TCM), wear estimation, ANFIS N2 - Reliable tool wear monitoring system is one of the important aspects for achieving a self-adjusting manufacturing system. The original contribution of the research is the developed monitoring system that can detect tool breakage in real time by using a combination of neural decision system and ANFIS tool wear estimator. The principal presumption was that force signals contain the most useful information for determining the tool condition. Therefore, the ANFIS method is used to extract the features of tool states from cutting force signals. ANFIS method seeks to provide a linguistic model for the estimation of tool wear from the knowledge embedded in the artificial neural network. The ANFIS method uses the relationship between flank wear and the resultant cutting force to estimate tool wear. A series of experiments were conducted to determine the relationship between flank wear and cutting force as well as cutting parameters. Speed, feed, depth of cutting, time and cutting forces were used as input parameters and flank wear width and tool state were output parameters. The forces were measured using a piezoelectric dynamometer and data acquisition system. Simultaneously flank wear at the cutting edge was monitored by using a tool maker’s microscope. The experimental force and wear data were utilized to train the developed simulation environment based on ANFIS modelling. The artificial neural network, was also used to discriminate different malfunction states from measured signals. By developed tool monitoring system (TCM) the machining process can be on-line monitored and stopped for tool change based on a pre-set tool-wear limit. The fundamental limitation of research was to develop a singlesensor monitoring system, reliable as commercially available system, but 80% cheaper than multisensor approach. UR - https://www.sv-jme.eu/article/real-time-cutting-tool-condition-monitoring-in-milling/
Čuš, Franci, AND Župerl, Uroš. "Real-Time Cutting Tool Condition Monitoring in Milling" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 57 Number 2 (28 June 2018)
Strojniški vestnik - Journal of Mechanical Engineering 57(2011)2, 142-150
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
Reliable tool wear monitoring system is one of the important aspects for achieving a self-adjusting manufacturing system. The original contribution of the research is the developed monitoring system that can detect tool breakage in real time by using a combination of neural decision system and ANFIS tool wear estimator. The principal presumption was that force signals contain the most useful information for determining the tool condition. Therefore, the ANFIS method is used to extract the features of tool states from cutting force signals. ANFIS method seeks to provide a linguistic model for the estimation of tool wear from the knowledge embedded in the artificial neural network. The ANFIS method uses the relationship between flank wear and the resultant cutting force to estimate tool wear. A series of experiments were conducted to determine the relationship between flank wear and cutting force as well as cutting parameters. Speed, feed, depth of cutting, time and cutting forces were used as input parameters and flank wear width and tool state were output parameters. The forces were measured using a piezoelectric dynamometer and data acquisition system. Simultaneously flank wear at the cutting edge was monitored by using a tool maker’s microscope. The experimental force and wear data were utilized to train the developed simulation environment based on ANFIS modelling. The artificial neural network, was also used to discriminate different malfunction states from measured signals. By developed tool monitoring system (TCM) the machining process can be on-line monitored and stopped for tool change based on a pre-set tool-wear limit. The fundamental limitation of research was to develop a singlesensor monitoring system, reliable as commercially available system, but 80% cheaper than multisensor approach.