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- สมเกียรติ ตั้งจิตสิตเจริญ
ศ. ดร.สมเกียรติ ตั้งจิตสิตเจริญ
- 8th Floor of Engineering 4 Bldg., Room 802
- +66-2218-6853
- somkiat.ta@eng.chula.ac.th
Overview
Professor Dr. Somkiat Tangjitsitcharoen is currently Head of Advanced Manufacturing and Precision Engineering Research Center at the Industrial Engineering, Chulalongkorn University, Thailand. His research interests include in-process monitoring and optimization of manufacturing processes, micro-machining and micro-assembly, high precision cutting, and intelligent manufacturing system and machine tool.
Education
D.Eng. Mechanical Engineering
Kobe University, Japan, 2004
M.Eng. Industrial Engineering
Chulalongkorn University, Thailand, 1998
B.Eng. Production Engineering
King Mongkut’s Universtiy of Technology Thonburi, Thailand, 1995
Expertise
Manufacturing & Service Systems
Publications
2010
Narongsak Pongsathornwiwat, Somkiat Tangjitsitcharoen
Intelligent monitoring and detection of chatter in ball-end milling process on CNC machining center Conference
2010, ISBN: 978-142447295-6, (Cited by: 8).
@conference{Pongsathornwiwat2010,
title = {Intelligent monitoring and detection of chatter in ball-end milling process on CNC machining center},
author = {Narongsak Pongsathornwiwat and Somkiat Tangjitsitcharoen},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-78651428697&doi=10.1109%2fICCIE.2010.5668378&partnerID=40&md5=59552c36d3903ad904594ab36581ed6d},
doi = {10.1109/ICCIE.2010.5668378},
isbn = {978-142447295-6},
year = {2010},
date = {2010-01-01},
journal = {40th International Conference on Computers and Industrial Engineering: Soft Computing Techniques for Advanced Manufacturing and Service Systems, CIE40 2010},
abstract = {As fully automated and intelligent machine tools are highly developed and expected to be realized in the near future, which can autonomously determine the cutting states regardless of any cutting conditions and can change them automatically as required, it is hence necessary to develop a methodology to identify the cutting states automatically. In ball-end milling process, the chatter is one of the major limitations of productivity in metal cutting. It always affects the surface finish, the dimensional accuracy, the tool life and machine life. The aim of this research is to develop an in-process monitoring system for detection of the chatter regardless of the cutting conditions by using the dynamic cutting force obtained during the cutting. The method proposed introduces three parameters, which are calculated and obtained by taking the ratio of the average variances of the dynamic cutting forces of three force components, to classify the chatter. The new algorithm was developed and implemented on 5-axis CNC machining center to detect the chatter in ball-end milling process. The experimentally obtained results showed that the proposed method can be used efficiently to detect the chatter during the cutting even though the cutting conditions are changed.},
note = {Cited by: 8},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
As fully automated and intelligent machine tools are highly developed and expected to be realized in the near future, which can autonomously determine the cutting states regardless of any cutting conditions and can change them automatically as required, it is hence necessary to develop a methodology to identify the cutting states automatically. In ball-end milling process, the chatter is one of the major limitations of productivity in metal cutting. It always affects the surface finish, the dimensional accuracy, the tool life and machine life. The aim of this research is to develop an in-process monitoring system for detection of the chatter regardless of the cutting conditions by using the dynamic cutting force obtained during the cutting. The method proposed introduces three parameters, which are calculated and obtained by taking the ratio of the average variances of the dynamic cutting forces of three force components, to classify the chatter. The new algorithm was developed and implemented on 5-axis CNC machining center to detect the chatter in ball-end milling process. The experimentally obtained results showed that the proposed method can be used efficiently to detect the chatter during the cutting even though the cutting conditions are changed.
Somkiat Tangjitsitcharoen, Angsumalin Senjuntichai
In-process monitoring and prediction of surface roughness in ball-end milling process Conference
Danube Adria Association for Automation and Manufacturing, DAAAM, 2010, ISSN: 17269679, (Cited by: 5).
@conference{Tangjitsitcharoen20101389,
title = {In-process monitoring and prediction of surface roughness in ball-end milling process},
author = {Somkiat Tangjitsitcharoen and Angsumalin Senjuntichai},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84897851656&partnerID=40&md5=65af7c86d756a612c22b04fc7f9692d3},
issn = {17269679},
year = {2010},
date = {2010-01-01},
journal = {Annals of DAAAM and Proceedings of the International DAAAM Symposium},
pages = {1389 – 1390},
publisher = {Danube Adria Association for Automation and Manufacturing, DAAAM},
abstract = {The objective of this research is to propose a practical model to predict the in-process surface roughness during the ball-end milling process by utilizing the cutting force ratio. The proposed in-process surface roughness model is developed based on the experimentally obtained results by employing the exponential function with five factors of the spindle speed, the feed rate, the tool diameter, the depth of cut, and the cutting force ratio. The multiple regression analysis is utilized to calculate the regression coefficients with the use of the least square method. The prediction interval (PI) of the in- process surface roughness model has been also presented to monitor and control the in-process predicted surface roughness at 95% confident level. All those parameters have their own characteristics to the arithmetic surface roughness and the surface roughness. It is proved by the cutting tests that the proposed and developed in-process surface roughness model can be used to predict the in-process surface roughness by utilizing the cutting force ratio with the highly acceptable prediction accuracy.},
note = {Cited by: 5},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
The objective of this research is to propose a practical model to predict the in-process surface roughness during the ball-end milling process by utilizing the cutting force ratio. The proposed in-process surface roughness model is developed based on the experimentally obtained results by employing the exponential function with five factors of the spindle speed, the feed rate, the tool diameter, the depth of cut, and the cutting force ratio. The multiple regression analysis is utilized to calculate the regression coefficients with the use of the least square method. The prediction interval (PI) of the in- process surface roughness model has been also presented to monitor and control the in-process predicted surface roughness at 95% confident level. All those parameters have their own characteristics to the arithmetic surface roughness and the surface roughness. It is proved by the cutting tests that the proposed and developed in-process surface roughness model can be used to predict the in-process surface roughness by utilizing the cutting force ratio with the highly acceptable prediction accuracy.
Somkiat Tangjitsitcharoen, Angsumalin Senjuntichai
Monitoring of surface roughness in CNC turning process Conference
Danube Adria Association for Automation and Manufacturing, DAAAM, 2010, ISSN: 17269679, (Cited by: 3).
@conference{Tangjitsitcharoen20101391,
title = {Monitoring of surface roughness in CNC turning process},
author = {Somkiat Tangjitsitcharoen and Angsumalin Senjuntichai},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79953869934&partnerID=40&md5=2f631ccc90bcb99b1abb7a72fb027720},
issn = {17269679},
year = {2010},
date = {2010-01-01},
journal = {Annals of DAAAM and Proceedings of the International DAAAM Symposium},
pages = {1391 – 1392},
publisher = {Danube Adria Association for Automation and Manufacturing, DAAAM},
abstract = {In order to realize the intelligent machine tools, the objective of this research is to propose a practical model to predict the in-process surface roughness during the turning process by using the cutting force ratio. The proposed in- process surface roughness model is developed base on the experimentally obtain result by employing the exponential function with six factors of the cutting speed, the feed rate, the rank angle the tool nose radius, the depth of cut, and the cutting force ratio. The multiple regression analysis is utilized to calculate the regression coefficients with the use of the least square method. The prediction accuracy of the in-process surface roughness model has been also presented to monitor and control the in-process predicted surface roughness. It is proved by the cutting tests that the propose and developed in- process surface roughness model can be used to predict the in- process surface roughness by utilizing the cutting force ratio with the highly acceptable prediction accuracy.},
note = {Cited by: 3},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
In order to realize the intelligent machine tools, the objective of this research is to propose a practical model to predict the in-process surface roughness during the turning process by using the cutting force ratio. The proposed in- process surface roughness model is developed base on the experimentally obtain result by employing the exponential function with six factors of the cutting speed, the feed rate, the rank angle the tool nose radius, the depth of cut, and the cutting force ratio. The multiple regression analysis is utilized to calculate the regression coefficients with the use of the least square method. The prediction accuracy of the in-process surface roughness model has been also presented to monitor and control the in-process predicted surface roughness. It is proved by the cutting tests that the propose and developed in- process surface roughness model can be used to predict the in- process surface roughness by utilizing the cutting force ratio with the highly acceptable prediction accuracy.
2009
Somkiat Tangjitsitcharoen
In-process monitoring and detection of chip formation and chatter for CNC turning Journal Article
In: Journal of Materials Processing Technology, vol. 209, no. 10, pp. 4682 – 4688, 2009, ISSN: 09240136, (Cited by: 93).
@article{Tangjitsitcharoen20094682,
title = {In-process monitoring and detection of chip formation and chatter for CNC turning},
author = {Somkiat Tangjitsitcharoen},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-67349130081&doi=10.1016%2fj.jmatprotec.2008.10.054&partnerID=40&md5=2c7bf7bc5ab556e29886f7852732a13f},
doi = {10.1016/j.jmatprotec.2008.10.054},
issn = {09240136},
year = {2009},
date = {2009-01-01},
journal = {Journal of Materials Processing Technology},
volume = {209},
number = {10},
pages = {4682 – 4688},
abstract = {In order to realize the intelligent machine tool, an in-process monitoring and detection of cutting states is developed for CNC turning machine to check and improve the stability of the processes. The method developed utilizes the power spectrum density, or PSD of dynamic cutting force measured during cutting. Experimental results suggested that there are basically three types of patterns of PSD when the cutting states are the continuous chip formation, the broken chip formation, and the chatter. The broken chip formation is desired to realize safe and reliable machining. The proposed method introduces three ratios, which are calculated from three dynamic cutting force components and obtained by taking the ratio of cumulative power spectrum density for a certain frequency range corresponding to the states of cutting to that of the whole frequency range of each dynamic cutting force component, to classify the cutting states of continuous chip formation, broken chip formation, and chatter. The algorithm was developed to calculate the values of three ratios during the process in order to obtain the proper threshold values for classification of the cutting states. The method developed has been proved by series of cutting tests that the states of cutting are well identified regardless of the cutting conditions. The broken chips are easily obtained by changing the cutting conditions during the processes referring to the algorithm developed. © 2008 Elsevier B.V. All rights reserved.},
note = {Cited by: 93},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In order to realize the intelligent machine tool, an in-process monitoring and detection of cutting states is developed for CNC turning machine to check and improve the stability of the processes. The method developed utilizes the power spectrum density, or PSD of dynamic cutting force measured during cutting. Experimental results suggested that there are basically three types of patterns of PSD when the cutting states are the continuous chip formation, the broken chip formation, and the chatter. The broken chip formation is desired to realize safe and reliable machining. The proposed method introduces three ratios, which are calculated from three dynamic cutting force components and obtained by taking the ratio of cumulative power spectrum density for a certain frequency range corresponding to the states of cutting to that of the whole frequency range of each dynamic cutting force component, to classify the cutting states of continuous chip formation, broken chip formation, and chatter. The algorithm was developed to calculate the values of three ratios during the process in order to obtain the proper threshold values for classification of the cutting states. The method developed has been proved by series of cutting tests that the states of cutting are well identified regardless of the cutting conditions. The broken chips are easily obtained by changing the cutting conditions during the processes referring to the algorithm developed. © 2008 Elsevier B.V. All rights reserved.
Somkiat Tangjitsitcharoen
IN-process monitoring and identification of cutting states based on power spectrum density analysis Conference
vol. 37, 2009, ISSN: 10473025, (Cited by: 0).
@conference{Tangjitsitcharoen200915,
title = {IN-process monitoring and identification of cutting states based on power spectrum density analysis},
author = {Somkiat Tangjitsitcharoen},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-70449638403&partnerID=40&md5=e95e8aba73254d4e343d65a22be4dace},
issn = {10473025},
year = {2009},
date = {2009-01-01},
journal = {Transactions of the North American Manufacturing Research Institution of SME},
volume = {37},
pages = {15 – 24},
abstract = {This paper presents the in-process monitoring and identification of the cutting states in turning process to realize the intelligent machine tools. The developed method utilizes the power spectrum densities, PSDs of the dynamic cutting forces measured during the cutting. The experimentally obtained results suggested that there are basically five types of patterns of PSDs when the cutting states are the continuous chip, the broken chip, the mixed broken chip, the chatter, and the chatter occurred with broken chip. The broken chip is desired to realize the reliable machining. The proposed method introduces three ratios to classify the cutting states, which are calculated by taking the ratios of the integrated PSDs of three dynamic cutting force components for a certain frequency range corresponding to those cutting states. The proposed method has been proved by series of cutting experiments that the states of cutting are well identified regardless of the cutting conditions.},
note = {Cited by: 0},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
This paper presents the in-process monitoring and identification of the cutting states in turning process to realize the intelligent machine tools. The developed method utilizes the power spectrum densities, PSDs of the dynamic cutting forces measured during the cutting. The experimentally obtained results suggested that there are basically five types of patterns of PSDs when the cutting states are the continuous chip, the broken chip, the mixed broken chip, the chatter, and the chatter occurred with broken chip. The broken chip is desired to realize the reliable machining. The proposed method introduces three ratios to classify the cutting states, which are calculated by taking the ratios of the integrated PSDs of three dynamic cutting force components for a certain frequency range corresponding to those cutting states. The proposed method has been proved by series of cutting experiments that the states of cutting are well identified regardless of the cutting conditions.
Somkiat Tangjitsitcharoen, Napassavong Rojanarowan, Pirat Tangpornprasert, Chanyaphan Virulsri
Intelligent control of microassembly process based on in-process monitoring of pressing force Journal Article
In: International Journal of Advanced Manufacturing Technology, vol. 45, no. 1-2, pp. 148 – 155, 2009, ISSN: 14333015, (Cited by: 4).
@article{Tangjitsitcharoen2009148,
title = {Intelligent control of microassembly process based on in-process monitoring of pressing force},
author = {Somkiat Tangjitsitcharoen and Napassavong Rojanarowan and Pirat Tangpornprasert and Chanyaphan Virulsri},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-70350554190&doi=10.1007%2fs00170-009-1946-6&partnerID=40&md5=15ebcf7e7f10f474f9106e8713435d6a},
doi = {10.1007/s00170-009-1946-6},
issn = {14333015},
year = {2009},
date = {2009-01-01},
journal = {International Journal of Advanced Manufacturing Technology},
volume = {45},
number = {1-2},
pages = {148 – 155},
abstract = {To realize an automated and intelligent microassembly process, a method has been developed to monitor the position of the shaft in the plate of the high-precision spindle motor to reduce the shaft high and the shaft low problems. The shaft low occurs when the shaft is stuck in the plate lower than the setting tolerance due to the large tolerance of the shaft and the small tolerance of the plate. While the shaft high appears, the shaft is pressed through the plate over the setting tolerance due to the small tolerance of the shaft and the large tolerance of the plate. The plate is expanded to reduce the shaft low before microassembly by preheating technique at the experimentally simulated temperature. The force sensor is utilized to monitor the pressing force. A method proposed to reduce the shaft high introduces the reference voltage as the threshold value, which is obtained by taking the differentiation of the in-process pressing force. A slope detector is developed to calculate the output voltage and the motor driver of machine is controlled when the obtained output voltage is larger than the reference voltage. It is proved that the shaft high and the shaft low are well controlled and reduced regardless of the geometries of the shaft and the plate. © 2009 Springer-Verlag London Limited.},
note = {Cited by: 4},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
To realize an automated and intelligent microassembly process, a method has been developed to monitor the position of the shaft in the plate of the high-precision spindle motor to reduce the shaft high and the shaft low problems. The shaft low occurs when the shaft is stuck in the plate lower than the setting tolerance due to the large tolerance of the shaft and the small tolerance of the plate. While the shaft high appears, the shaft is pressed through the plate over the setting tolerance due to the small tolerance of the shaft and the large tolerance of the plate. The plate is expanded to reduce the shaft low before microassembly by preheating technique at the experimentally simulated temperature. The force sensor is utilized to monitor the pressing force. A method proposed to reduce the shaft high introduces the reference voltage as the threshold value, which is obtained by taking the differentiation of the in-process pressing force. A slope detector is developed to calculate the output voltage and the motor driver of machine is controlled when the obtained output voltage is larger than the reference voltage. It is proved that the shaft high and the shaft low are well controlled and reduced regardless of the geometries of the shaft and the plate. © 2009 Springer-Verlag London Limited.
Somkiat Tangjitsitcharoen, Napassavong Rojanarowan, Pirat Tangpornprasert, Chanyaphan Virulsri
Intelligent monitoring of microassembly process of high-precision spindle motor parts Conference
vol. 37, 2009, ISSN: 10473025, (Cited by: 0).
@conference{Tangjitsitcharoen2009451,
title = {Intelligent monitoring of microassembly process of high-precision spindle motor parts},
author = {Somkiat Tangjitsitcharoen and Napassavong Rojanarowan and Pirat Tangpornprasert and Chanyaphan Virulsri},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-70449678689&partnerID=40&md5=438f8a9a4e8789528bf327816bfb7180},
issn = {10473025},
year = {2009},
date = {2009-01-01},
journal = {Transactions of the North American Manufacturing Research Institution of SME},
volume = {37},
pages = {451 – 458},
abstract = {A method has been developed to monitor the microassembly process of the shaft into the plate of the high-precision spindle motor. The plate is expanded to reduce the shaft low problem by using the preheating technique at the experimentally simulated temperature. The finite element method is utilized to simulate the expansion of the plate. The force sensor is utilized to monitor the pressing force. A proposed method to reduce the shaft high problem introduces the reference voltage as the threshold value, which is obtained by taking the differentiation of the in-process pressing force. A slope detector is developed to calculate the output voltage, and the motor driver of the machine is controlled when the obtained output voltage is larger than the reference voltage. It is proved that the shaft high and the shaft low are well controlled and reduced by the proposed preheating technique and the developed slope detector.},
note = {Cited by: 0},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
A method has been developed to monitor the microassembly process of the shaft into the plate of the high-precision spindle motor. The plate is expanded to reduce the shaft low problem by using the preheating technique at the experimentally simulated temperature. The finite element method is utilized to simulate the expansion of the plate. The force sensor is utilized to monitor the pressing force. A proposed method to reduce the shaft high problem introduces the reference voltage as the threshold value, which is obtained by taking the differentiation of the in-process pressing force. A slope detector is developed to calculate the output voltage, and the motor driver of the machine is controlled when the obtained output voltage is larger than the reference voltage. It is proved that the shaft high and the shaft low are well controlled and reduced by the proposed preheating technique and the developed slope detector.
2008
Somkiat Tangjitsitcharoen, Toshimichi Moriwaki
Intelligent monitoring and identification of cutting states of chips and chatter on CNC turning machine Journal Article
In: Journal of Manufacturing Processes, vol. 10, no. 1, pp. 40 – 46, 2008, ISSN: 15266125, (Cited by: 30).
@article{Tangjitsitcharoen200840,
title = {Intelligent monitoring and identification of cutting states of chips and chatter on CNC turning machine},
author = {Somkiat Tangjitsitcharoen and Toshimichi Moriwaki},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-64249108187&doi=10.1016%2fj.manpro.2008.01.001&partnerID=40&md5=d419085720a1ba89dfa38d09c6f12e98},
doi = {10.1016/j.manpro.2008.01.001},
issn = {15266125},
year = {2008},
date = {2008-01-01},
journal = {Journal of Manufacturing Processes},
volume = {10},
number = {1},
pages = {40 – 46},
publisher = {Elsevier BV},
abstract = {To realize an intelligent machine tool, which can autonomously determine the cutting states and can change them automatically as required due to changes in the environmental conditions, a method has been developed to monitor and identify the states of cutting for CNC turning based on a pattern recognition technique. The method proposed introduces three parameters to classify the cutting states of continuous chip formation, broken chip formation, and chatter. Among the states of cutting, the broken chip formation is required for the stable and reliable machining process. The three parameters are calculated and obtained by taking the ratio of the average variances of the dynamic components of three cutting forces. The algorithm was developed to calculate the values of three parameters during the process to obtain the reference feature spaces and determine the proper threshold values for classification of the cutting states. A tool dynamometer is developed, and implemented to the CNC turning machine to monitor the turning process. It is proved by a series of cutting experiments that the states of cutting are well identified by the method developed and proposed regardless of the cutting conditions. The algorithm is proposed to obtain the broken chips by changing the cutting conditions during the process. © 2008 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.},
note = {Cited by: 30},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
To realize an intelligent machine tool, which can autonomously determine the cutting states and can change them automatically as required due to changes in the environmental conditions, a method has been developed to monitor and identify the states of cutting for CNC turning based on a pattern recognition technique. The method proposed introduces three parameters to classify the cutting states of continuous chip formation, broken chip formation, and chatter. Among the states of cutting, the broken chip formation is required for the stable and reliable machining process. The three parameters are calculated and obtained by taking the ratio of the average variances of the dynamic components of three cutting forces. The algorithm was developed to calculate the values of three parameters during the process to obtain the reference feature spaces and determine the proper threshold values for classification of the cutting states. A tool dynamometer is developed, and implemented to the CNC turning machine to monitor the turning process. It is proved by a series of cutting experiments that the states of cutting are well identified by the method developed and proposed regardless of the cutting conditions. The algorithm is proposed to obtain the broken chips by changing the cutting conditions during the process. © 2008 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.
2006
Toshimichi Moriwaki, Somkiat Tangjitsitcharoen, Toshiroh Shibasaka
Development of sequential optimization method for CNC turning based on in-process tool wear monitoring Journal Article
In: JSME International Journal, Series C: Mechanical Systems, Machine Elements and Manufacturing, vol. 48, no. 4, pp. 769 – 774, 2006, ISSN: 1347538X, (Cited by: 1; All Open Access, Bronze Open Access).
@article{Moriwaki2006769,
title = {Development of sequential optimization method for CNC turning based on in-process tool wear monitoring},
author = {Toshimichi Moriwaki and Somkiat Tangjitsitcharoen and Toshiroh Shibasaka},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-33745183770&doi=10.1299%2fjsmec.48.769&partnerID=40&md5=86b8481ae5e5c1d724af863a9849bb17},
doi = {10.1299/jsmec.48.769},
issn = {1347538X},
year = {2006},
date = {2006-01-01},
journal = {JSME International Journal, Series C: Mechanical Systems, Machine Elements and Manufacturing},
volume = {48},
number = {4},
pages = {769 – 774},
abstract = {A system and procedures are developed to optimize the cutting speed for CNC turning. The current amount of tool wear is estimated based on the in-process cutting force measurement by applying the method developed and reported previously. Once the tool wear is estimated for the different cutting speeds, the coefficients of the Taylor's tool life equation are determined or successively modified based on the estimated tool wear data. The optimum cutting speed is obtained by referring to the criteria of either the minimum production cost or the maximum production rate. The system developed is applied to actual turning of carbon steel with coated carbide tools, and it has been proved that the system runs satisfactory. The method developed here can be readily applied to unknown combinations of the work material and die tool, as it searches the optimum cutting conditions automatically while the process is going on. Copyright © 2006 by The Japan Society of Mechanical Engineers.},
note = {Cited by: 1; All Open Access, Bronze Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A system and procedures are developed to optimize the cutting speed for CNC turning. The current amount of tool wear is estimated based on the in-process cutting force measurement by applying the method developed and reported previously. Once the tool wear is estimated for the different cutting speeds, the coefficients of the Taylor’s tool life equation are determined or successively modified based on the estimated tool wear data. The optimum cutting speed is obtained by referring to the criteria of either the minimum production cost or the maximum production rate. The system developed is applied to actual turning of carbon steel with coated carbide tools, and it has been proved that the system runs satisfactory. The method developed here can be readily applied to unknown combinations of the work material and die tool, as it searches the optimum cutting conditions automatically while the process is going on. Copyright © 2006 by The Japan Society of Mechanical Engineers.
2004
Toshimichi Moriwaki, Toshiroh Shibasaka, Somkiat Tangjitsitcharoen
Development of in-process tool wear monitoring system for CNC turning Journal Article
In: JSME International Journal, Series C: Mechanical Systems, Machine Elements and Manufacturing, vol. 47, no. 3, pp. 933 – 938, 2004, ISSN: 13447653, (Cited by: 17; All Open Access, Bronze Open Access).
@article{Moriwaki2004933,
title = {Development of in-process tool wear monitoring system for CNC turning},
author = {Toshimichi Moriwaki and Toshiroh Shibasaka and Somkiat Tangjitsitcharoen},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-8744248296&doi=10.1299%2fjsmec.47.933&partnerID=40&md5=0f4e68b9381739a9271149c2766cb694},
doi = {10.1299/jsmec.47.933},
issn = {13447653},
year = {2004},
date = {2004-01-01},
journal = {JSME International Journal, Series C: Mechanical Systems, Machine Elements and Manufacturing},
volume = {47},
number = {3},
pages = {933 – 938},
abstract = {The aim of this research is to develop an in-process tool wear monitoring system for CNC turning machine. The exponential decay function is employed to represent the relation between the nominal specific cutting resistance and feed rate. An index value α in the exponential decay function is defined to estimate the flank wear, which is equivalent to the rate of increase in the nominal specific cutting resistance at zero feed rate as compared to that at infinite feed rate. In order to obtain the characteristic value α, the additional cutting cycles is proposed here to alter the feed rate deliberately during the normal cutting cycle to measure the cutting forces and identify the rate of increase in the nominal specific cutting resistance at smaller feed rates. Series of cutting tests were carried out to estimate the flank wear, and it is proved that the index mentioned above can be a good measure of tool wear, even though the depths of cut, the cutting speeds and the cutting tools, as well as the work materials are different.},
note = {Cited by: 17; All Open Access, Bronze Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The aim of this research is to develop an in-process tool wear monitoring system for CNC turning machine. The exponential decay function is employed to represent the relation between the nominal specific cutting resistance and feed rate. An index value α in the exponential decay function is defined to estimate the flank wear, which is equivalent to the rate of increase in the nominal specific cutting resistance at zero feed rate as compared to that at infinite feed rate. In order to obtain the characteristic value α, the additional cutting cycles is proposed here to alter the feed rate deliberately during the normal cutting cycle to measure the cutting forces and identify the rate of increase in the nominal specific cutting resistance at smaller feed rates. Series of cutting tests were carried out to estimate the flank wear, and it is proved that the index mentioned above can be a good measure of tool wear, even though the depths of cut, the cutting speeds and the cutting tools, as well as the work materials are different.