A Novel Approach to Optimal Cutting Tool Replacement
In metal cutting industries, mathematical/statistical
models are typically used to predict tool replacement time. These
off-line methods usually result in less than optimum replacement
time thereby either wasting resources or causing quality problems.
The few online real-time methods proposed use indirect measurement
techniques and are prone to similar errors. Our idea is based on
identifying the optimal replacement time using an electronic nose to
detect the airborne compounds released when the tool wear reaches
to a chemical substrate doped into tool material during the
fabrication. The study investigates the feasibility of the idea, possible
doping materials and methods along with data stream mining
techniques for detection and monitoring different phases of tool
wear.
[1] Kurada S, Bradley C (1997) A review of machine vision sensors for tool
condition monitoring. Computers in Industry, 34:55-72.
[2] Cho S, Binsaed S, Asfour S (2008), Design of multi-sensor fusionbased
tool condition monitoring system in end milling, International
Journal of Advanced Manufacturing Technology, Submitted.
[3] Tansel IN, Trujillo ME, Bao WY (2001) Acoustic emission-based tool
breakage detector for micro-end milling operations, International Journal
of Modeling and Simulation, 21(1):10-16.
[4] Cho S, Asfour S, Onar A, Kaundinya N (2005) Tool breakage detection
using support vector machine learning in a milling process, International
Journal of Machine Tools and Manufacturing, 45(3), 241-249.
[5] Bhattacharyya P, Senupta D, Mukhopadhaya S (2007) Cutting force
based real-time estimation of tool wear in face milling using a
combination of signal processing techniques, Mechanical Systems and
Signal, 21(6):2665-2683.
[6] Yesilyurt I, Ozturk H (2007) Tool condition monitoring in milling using
vibration analysis, International Journal of Production Research,
45(4):1013-1028.
[7] Ghosh N, Ravi YB, Patra A, Mukhopadhyay S, Paul S, Mohanty AR,
Chattopadhyay AB (2007) Estimation of tool wear during CNC milling
using neural network based sensor fusion, Mechanical Systems and
Signal Processing, 21:466-479.
[8] Norman P, Kaplan A, Rantatalo M, Svenningsson I (2007) Study of a
sensor platform for monitoring machining of aluminum and steel,
Measurement Science Technology, 18:1155-1166.
[9] Persaud K, Dodd GH (1982), Analysis of discrimination mechanisms in
the mammalian olfactory system using a model nose, Nature, 299:352-
355.
[10] Bartlett PN, Blair N, Gardner JW (1993), Electronic nose: principles,
applications and outlook, ASIC, 15e Colloque, Montpellier, 478-486.
[11] Gardner JW, Bartlett PN (1993), A brief history of electronic noses,
Sensors and Actuators B., 18:211-220.
[12] Ghani JA, Choudhury IA, Masjuki HH (2004), Wear mechanism of TiN
coated carbide and uncoated cermets tools at high cutting speed
applications, Journal of Materials Processing Technology, 153-
154:1067-1073.
[13] Liu H, Wang Y & Lu X, "A Method To Choose Kernel Function and its
Parameters for Support Vector Machines", Proceedings of the Fourth
International Conference on Machine Learning and Cybernetics,
Guangzhou, 18-21 August 2005.
[14] Fuke I, Prabhu VV, Cho S, George T, Singh J (2005), Rapid
manufacturing of rhenium components using EB-PVD, Rapid
Prototyping Journal, 11(2):66-73.
[15] Tsai MH, Sun SC, Chiu HT, Tsai CE, Chuang SH (1995), Metalorganic
chemical vapor deposition of tantalum nitride by tertbutylimidortris -
(diethylamido) tantalum for advanced metallization, Applied Physics
Letter, 67:1128-1133.
[16] Witten IH, Frank E (2005), Data Mining: practical machine learning
tools and techniques. 2nd Edition, Morgan Kaufmann, San Francisco,
2005.
[1] Kurada S, Bradley C (1997) A review of machine vision sensors for tool
condition monitoring. Computers in Industry, 34:55-72.
[2] Cho S, Binsaed S, Asfour S (2008), Design of multi-sensor fusionbased
tool condition monitoring system in end milling, International
Journal of Advanced Manufacturing Technology, Submitted.
[3] Tansel IN, Trujillo ME, Bao WY (2001) Acoustic emission-based tool
breakage detector for micro-end milling operations, International Journal
of Modeling and Simulation, 21(1):10-16.
[4] Cho S, Asfour S, Onar A, Kaundinya N (2005) Tool breakage detection
using support vector machine learning in a milling process, International
Journal of Machine Tools and Manufacturing, 45(3), 241-249.
[5] Bhattacharyya P, Senupta D, Mukhopadhaya S (2007) Cutting force
based real-time estimation of tool wear in face milling using a
combination of signal processing techniques, Mechanical Systems and
Signal, 21(6):2665-2683.
[6] Yesilyurt I, Ozturk H (2007) Tool condition monitoring in milling using
vibration analysis, International Journal of Production Research,
45(4):1013-1028.
[7] Ghosh N, Ravi YB, Patra A, Mukhopadhyay S, Paul S, Mohanty AR,
Chattopadhyay AB (2007) Estimation of tool wear during CNC milling
using neural network based sensor fusion, Mechanical Systems and
Signal Processing, 21:466-479.
[8] Norman P, Kaplan A, Rantatalo M, Svenningsson I (2007) Study of a
sensor platform for monitoring machining of aluminum and steel,
Measurement Science Technology, 18:1155-1166.
[9] Persaud K, Dodd GH (1982), Analysis of discrimination mechanisms in
the mammalian olfactory system using a model nose, Nature, 299:352-
355.
[10] Bartlett PN, Blair N, Gardner JW (1993), Electronic nose: principles,
applications and outlook, ASIC, 15e Colloque, Montpellier, 478-486.
[11] Gardner JW, Bartlett PN (1993), A brief history of electronic noses,
Sensors and Actuators B., 18:211-220.
[12] Ghani JA, Choudhury IA, Masjuki HH (2004), Wear mechanism of TiN
coated carbide and uncoated cermets tools at high cutting speed
applications, Journal of Materials Processing Technology, 153-
154:1067-1073.
[13] Liu H, Wang Y & Lu X, "A Method To Choose Kernel Function and its
Parameters for Support Vector Machines", Proceedings of the Fourth
International Conference on Machine Learning and Cybernetics,
Guangzhou, 18-21 August 2005.
[14] Fuke I, Prabhu VV, Cho S, George T, Singh J (2005), Rapid
manufacturing of rhenium components using EB-PVD, Rapid
Prototyping Journal, 11(2):66-73.
[15] Tsai MH, Sun SC, Chiu HT, Tsai CE, Chuang SH (1995), Metalorganic
chemical vapor deposition of tantalum nitride by tertbutylimidortris -
(diethylamido) tantalum for advanced metallization, Applied Physics
Letter, 67:1128-1133.
[16] Witten IH, Frank E (2005), Data Mining: practical machine learning
tools and techniques. 2nd Edition, Morgan Kaufmann, San Francisco,
2005.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:59280", author = "Cem Karacal and Sohyung Cho and William Yu", title = "A Novel Approach to Optimal Cutting Tool Replacement", abstract = "In metal cutting industries, mathematical/statistical
models are typically used to predict tool replacement time. These
off-line methods usually result in less than optimum replacement
time thereby either wasting resources or causing quality problems.
The few online real-time methods proposed use indirect measurement
techniques and are prone to similar errors. Our idea is based on
identifying the optimal replacement time using an electronic nose to
detect the airborne compounds released when the tool wear reaches
to a chemical substrate doped into tool material during the
fabrication. The study investigates the feasibility of the idea, possible
doping materials and methods along with data stream mining
techniques for detection and monitoring different phases of tool
wear.", keywords = "Tool condition monitoring, cutting tool replacement,data stream mining, e-Nose.", volume = "3", number = "9", pages = "1093-5", }