Detect Tool Breakage By Using Combination Neural Decision System & Anfis Tool Wear Predictor

Soheil Mohtaram; Mohammad Amin Nikbakht

doi:doi:10.11648/j.ijmea.20130102.15

| Peer-Reviewed

Detect Tool Breakage By Using Combination Neural Decision System & Anfis Tool Wear Predictor

Soheil Mohtaram, Mohammad Amin Nikbakht

Published in International Journal of Mechanical Engineering and Applications (Volume 1, Issue 2)

Received: 23 May 2013 Published: 30 June 2013

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

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 predictor. The ANFIS method uses the relationship between flank wear and the resultant cutting force to estimate tool wear. Therefore, the ANFIS method is used to extract the features of tool states from cutting force signals. A neural network is used in tool condition monitoring system (TCM) as a decision making system to discriminate different malfunction states from measured signal. A series of experiments were conducted to determine the relationship between flank wear and cutting force as well as cutting 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 modeling. By developed tool condition monitoring system (TCM) the machining process can be on-line monitored and stopped for tool change based on a pre-set tool-wear limit.

Published in	International Journal of Mechanical Engineering and Applications (Volume 1, Issue 2)
DOI	10.11648/j.ijmea.20130102.15
Page(s)	59-63
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2013. Published by Science Publishing Group

Keywords

Machining Process, Simulation, Wear Estimation, ANFIS

References

[1]	MULC, T., UDILJAK, T., CUS, F., MILFELNER, M. (2004) Monitoring cutting- tool wear using signals from the control system, Journal of Mechanical Engineering, Vol.50, No.12, pp 568-579
[2]	KUO, R.J. (2003) Multi-sensor integration for on-line tool wear estimation through artificial neural networks and fuzzy neural network, Engineering Applications of Artificial Intelligence, Vol.3, pp 49-261
[3]	ACHICHE, S., BALAZINSKI, M., BARON, L., JEMIELNIAK, K. (2008) Tool wear monitoring using genetically-generated fuzzy knowledge bases, Engineering Applications of Artificial Intelligence, Vol.15, pp 303-314
[4]	KOPAC, J. (2002) Cutting forces and their influence on the economics of machining, Journal of Mechanical Engineering, Vol.48, No 3, pp 72-79.
[5]	IQBAL, A., HE, N., DAR, N.U., LI, L. (2009) Comparison of fuzzy expert system based strategies of offline and online estimation of flank wear in hard milling process, Expert Systems with Applications, Vol.33, pp 61-66
[6]	CHIEN, W.T., TSAI, C.S. (2005) The investigation on the prediction of tool wear and the determination of optimum cutting conditions in machining 17-4PH stainless steel, Journal of Materials Processing Technology, Vol.140, pp 340-345
[7]	A.Chiba, T.Fukao, O.Ichikawa, M. Oshima, M. Takemoto & D.G.Dorrell, (2005), Magnetic bearing and bearing less drives Dreier, M. E., McKeown,W. L. and Scott, H. W. (1996) A fuzzy logic controller to drill small holes.In Chen, C. H. (ed.), Fuzzy Logic and Neural Network Handbook. New York: McGraw-Hill, pp. 22.1–22.8.
[8]	Aspin wall DK, Dewesa RC, Ng EG, Sage C, Soo SL (2007) the influence of cutter orientation and work piece angle on mach inability when high-speed milling Inconel 718 under finishing conditions. Int J Mach Tools Manuf 47:1839–1846.
[9]	Rech J, Kermouche G, Carcia-Rosales C, Khellouki A, Garcia-NavasV (2008) Characterization and modeling of the residual stresses induced by belt finishing on a AISI52100 hardened steel. J Mater Process Techno doi: 10.1016/j.jmatprotec.2007.12.133
[10]	El Mansori M, Sura E, Ghidossi P, Deblaise S, Dal Negro T, Khanfir H (2007) Toward physical description of form and finish performance in dry belt finishing process By a tribo-energetic approach. J Mater Process Technol 182:498–511.
[11]	Axinte DA, Kritmanorot M, Gindy NNZ (2005) Investigations on belt polishing of Heat-resistant titanium alloys. J Mater Process Technol 166:398–404.

Cite This Article

Plain Text BibTeX RIS

APA Style

Soheil Mohtaram, Mohammad Amin Nikbakht. (2013). Detect Tool Breakage By Using Combination Neural Decision System & Anfis Tool Wear Predictor. International Journal of Mechanical Engineering and Applications, 1(2), 59-63. https://doi.org/10.11648/j.ijmea.20130102.15

Copy | Download

ACS Style

Soheil Mohtaram; Mohammad Amin Nikbakht. Detect Tool Breakage By Using Combination Neural Decision System & Anfis Tool Wear Predictor. Int. J. Mech. Eng. Appl. 2013, 1(2), 59-63. doi: 10.11648/j.ijmea.20130102.15

Copy | Download

AMA Style

Soheil Mohtaram, Mohammad Amin Nikbakht. Detect Tool Breakage By Using Combination Neural Decision System & Anfis Tool Wear Predictor. Int J Mech Eng Appl. 2013;1(2):59-63. doi: 10.11648/j.ijmea.20130102.15

Copy | Download

@article{10.11648/j.ijmea.20130102.15,
  author = {Soheil Mohtaram and Mohammad Amin Nikbakht},
  title = {Detect Tool Breakage By Using Combination Neural Decision System & Anfis Tool Wear Predictor},
  journal = {International Journal of Mechanical Engineering and Applications},
  volume = {1},
  number = {2},
  pages = {59-63},
  doi = {10.11648/j.ijmea.20130102.15},
  url = {https://doi.org/10.11648/j.ijmea.20130102.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmea.20130102.15},
  abstract = {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 predictor. The ANFIS method uses the relationship between flank wear and the resultant cutting force to estimate tool wear. Therefore, the ANFIS method is used to extract the features of tool states from cutting force signals. A neural network is used in tool condition monitoring system (TCM) as a decision making system to discriminate different malfunction states from measured signal. A series of experiments were conducted to determine the relationship between flank wear and cutting force as well as cutting 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 modeling. By developed tool condition monitoring system (TCM) the machining process can be on-line monitored and stopped for tool change based on a pre-set tool-wear limit.},
 year = {2013}
}

Copy | Download

TY  - JOUR
T1  - Detect Tool Breakage By Using Combination Neural Decision System & Anfis Tool Wear Predictor
AU  - Soheil Mohtaram
AU  - Mohammad Amin Nikbakht
Y1  - 2013/06/30
PY  - 2013
N1  - https://doi.org/10.11648/j.ijmea.20130102.15
DO  - 10.11648/j.ijmea.20130102.15
T2  - International Journal of Mechanical Engineering and Applications
JF  - International Journal of Mechanical Engineering and Applications
JO  - International Journal of Mechanical Engineering and Applications
SP  - 59
EP  - 63
PB  - Science Publishing Group
SN  - 2330-0248
UR  - https://doi.org/10.11648/j.ijmea.20130102.15
AB  - 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 predictor. The ANFIS method uses the relationship between flank wear and the resultant cutting force to estimate tool wear. Therefore, the ANFIS method is used to extract the features of tool states from cutting force signals. A neural network is used in tool condition monitoring system (TCM) as a decision making system to discriminate different malfunction states from measured signal. A series of experiments were conducted to determine the relationship between flank wear and cutting force as well as cutting 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 modeling. By developed tool condition monitoring system (TCM) the machining process can be on-line monitored and stopped for tool change based on a pre-set tool-wear limit.
VL  - 1
IS  - 2
ER  -

Copy | Download

Author Information

Soheil Mohtaram

Mechanical engineering, Islamic azad university science & Research branch, Yazd, Iran
Mohammad Amin Nikbakht

Mechatronic engineering, Islamic azad university of khomeinishahr, Isfahan, Iran

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Soheil Mohtaram, Mohammad Amin Nikbakht. (2013). Detect Tool Breakage By Using Combination Neural Decision System & Anfis Tool Wear Predictor. International Journal of Mechanical Engineering and Applications, 1(2), 59-63. https://doi.org/10.11648/j.ijmea.20130102.15

Copy | Download

ACS Style

Soheil Mohtaram; Mohammad Amin Nikbakht. Detect Tool Breakage By Using Combination Neural Decision System & Anfis Tool Wear Predictor. Int. J. Mech. Eng. Appl. 2013, 1(2), 59-63. doi: 10.11648/j.ijmea.20130102.15

Copy | Download

AMA Style

Soheil Mohtaram, Mohammad Amin Nikbakht. Detect Tool Breakage By Using Combination Neural Decision System & Anfis Tool Wear Predictor. Int J Mech Eng Appl. 2013;1(2):59-63. doi: 10.11648/j.ijmea.20130102.15

Copy | Download

@article{10.11648/j.ijmea.20130102.15,
  author = {Soheil Mohtaram and Mohammad Amin Nikbakht},
  title = {Detect Tool Breakage By Using Combination Neural Decision System & Anfis Tool Wear Predictor},
  journal = {International Journal of Mechanical Engineering and Applications},
  volume = {1},
  number = {2},
  pages = {59-63},
  doi = {10.11648/j.ijmea.20130102.15},
  url = {https://doi.org/10.11648/j.ijmea.20130102.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmea.20130102.15},
  abstract = {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 predictor. The ANFIS method uses the relationship between flank wear and the resultant cutting force to estimate tool wear. Therefore, the ANFIS method is used to extract the features of tool states from cutting force signals. A neural network is used in tool condition monitoring system (TCM) as a decision making system to discriminate different malfunction states from measured signal. A series of experiments were conducted to determine the relationship between flank wear and cutting force as well as cutting 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 modeling. By developed tool condition monitoring system (TCM) the machining process can be on-line monitored and stopped for tool change based on a pre-set tool-wear limit.},
 year = {2013}
}

Copy | Download

TY  - JOUR
T1  - Detect Tool Breakage By Using Combination Neural Decision System & Anfis Tool Wear Predictor
AU  - Soheil Mohtaram
AU  - Mohammad Amin Nikbakht
Y1  - 2013/06/30
PY  - 2013
N1  - https://doi.org/10.11648/j.ijmea.20130102.15
DO  - 10.11648/j.ijmea.20130102.15
T2  - International Journal of Mechanical Engineering and Applications
JF  - International Journal of Mechanical Engineering and Applications
JO  - International Journal of Mechanical Engineering and Applications
SP  - 59
EP  - 63
PB  - Science Publishing Group
SN  - 2330-0248
UR  - https://doi.org/10.11648/j.ijmea.20130102.15
AB  - 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 predictor. The ANFIS method uses the relationship between flank wear and the resultant cutting force to estimate tool wear. Therefore, the ANFIS method is used to extract the features of tool states from cutting force signals. A neural network is used in tool condition monitoring system (TCM) as a decision making system to discriminate different malfunction states from measured signal. A series of experiments were conducted to determine the relationship between flank wear and cutting force as well as cutting 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 modeling. By developed tool condition monitoring system (TCM) the machining process can be on-line monitored and stopped for tool change based on a pre-set tool-wear limit.
VL  - 1
IS  - 2
ER  -

Copy | Download