Wear Measuring and Wear Modelling Based On Archard, ASTM, and Neural Network Models
The wear measuring and wear modelling are
fundamental issues in the industrial field, mainly correlated to the
economy and safety. Therefore, there is a need to study the wear
measurements and wear estimation. Pin-on-disc test is the most
common test which is used to study the wear behaviour. In this paper,
the pin-on-disc (AEROTECH UNIDEX 11) is used for the
investigation of the effects of normal load and hardness of material on
the wear under dry and sliding conditions. In the pin-on-disc rig, two
specimens were used; one, a pin is made of steel with a tip, positioned
perpendicular to the disc, where the disc is made of aluminium. The
pin wear and disc wear were measured by using the following
instruments: The Talysurf instrument, a digital microscope, and the
alicona instrument. The Talysurf profilometer was used to measure
the pin/disc wear scar depth, digital microscope was used to measure
the diameter and width of wear scar, and the alicona was used to
measure the pin wear and disc wear. After that, the Archard model,
American Society for Testing and Materials model (ASTM), and
neural network model were used for pin/disc wear modelling.
Simulation results were implemented by using the Matlab program.
This paper focuses on how the alicona can be used for wear
measurements and how the neural network can be used for wear
estimation.
[1] J. R. Davis, “Surface Engineering for Corrosion and Wear Resistance”,
ASM international Publishing, USA, 2001.
[2] J. Stokes, “Theory and Application of the High Velocity Oxy-Fuel
(HVOF) Thermal Spray Process”, PhD thesis, School of Mechanical and
Manufacturing Engineering, Dublin City University, Ireland, 2008.
[3] J. A. Collins, “Mechanical Design of Machine Elements and Machines”,
John Wiley & Sons, USA, 2002.
[4] B. Bhushan, “Tribology: Friction, Wear, and Lubrication”, CRC Press
LCC, USA, 2000.
[5] A. Sethuramiah, “Lubricated Wear Science and Technology”, Elsevier
Science Publishing, USA, 2003.
[6] M. Braunovic, N. Myshkin, and V. Konchits, “Fundamentals,
Applications and Technology”, CRC press LCC, USA, 2007.
[7] O. Mokhlessi, H. M. Rad, “Utilization of 4 types of Artificial Neural
Network on the diagnosis of valve-physiological heart disease from heart
sounds”, 17th Iranian Conference of Biomedical Engineering, Vol. 978,
pp. 4244-7484, Iran, 2010.
[8] L. Jinkun, “Radial Basis Function (RBF) Neural Network control for
Mechanical Systems”, Springer, USA, 2013.
[9] C. Otto, “Modeling a hydraulic drive using neural networks”,
Department of Measurement and Control, University of Duisburg,
Germany, 2000.
[10] Ruano, “Applications of neural networks to control systems”, University
of Wales, School of Electronic Engineering Science University of Wales,
Bangor, 1992.
[11] P. J. Blau, R. L. Martin, and L. Riester, “A comparison of several surface
finish measurement methods as applied to ground ceramic and metal
surfaces”, Oak Ridge National Laboratory (Ornl), USA, 1996.
[12] L. Mummery, “Surface Texture Analysis the Handbook”, Hommlwerke
GmbH, Germany, 1990.
[13] W. Yan, N. P. Dowd, E. Busso, “Numerical study of sliding wear caused
by a loaded pin on a rotating disc”, Journal of the Mechanics and Physics
of Solids, vol. 50, pp. 449 – 470, 2002. Elsevier Science Publishing,
USA, 2002.
[14] A. Sanchez, “Tribological characterization of carbon based solid
lubricants”, Master thesis, Texas A&M University, USA, 2011.
[15] Orvnas, “Simulation of rail wear on the Swedish light rail line
Tvarbanan”, Master thesis, Royal Institute of Technology (KTH),
Department of Engineering, Stockholm, 2005.
[16] ASTM, “Standard Test Method for Wear Testing with a Pin-on-Disk
Apparatus”, ASTM Publishing, Vol. 14.02, pp. 95 – 99, USA, 2000.
[17] N. X. Randall, “Tribological Characterization of Biomaterials”,
Materials for Medical Devices, ASM, ASM Handbook, ASM
International Press, Vol. 23, p 148–156, Switzerland, 2007.
[18] Steel stockholders, “EN8 Steel”, KV steel Ltd, UK, 2014.
http://kvsteel.co.uk/steel/EN8.html (online).
[19] Aalco, “Aluminum, Aalco metal Ltd, UK, 2014.
https://www.google.co.uk/webhp?sourceid=chrome-instant&ion=1&
espv=2&ie=UTF-8#q=Aluminium%20Alloy%206082 (online).
[20] N. B. Karayiannis, “Reformulated Radial Basis Neural Networks Trained
by Gradient Descent”, IEEE Trans. Neural Networks, vol. 10, pp. 657-
671, Aug. 2002.
[21] J. Moody, and C. Darken, “Fast learning in Networks of Locally-Tuned
Processing Units”, neural computation, Vol. 1, 1989.
[22] R. Mammone, “Artificial Neural Networks for Speech and Vision”, New
Jersey, USA, 1994.
[23] L. Fu, “Neural Networks in Computer Intelligence”, university of
Florida, 1994.
[1] J. R. Davis, “Surface Engineering for Corrosion and Wear Resistance”,
ASM international Publishing, USA, 2001.
[2] J. Stokes, “Theory and Application of the High Velocity Oxy-Fuel
(HVOF) Thermal Spray Process”, PhD thesis, School of Mechanical and
Manufacturing Engineering, Dublin City University, Ireland, 2008.
[3] J. A. Collins, “Mechanical Design of Machine Elements and Machines”,
John Wiley & Sons, USA, 2002.
[4] B. Bhushan, “Tribology: Friction, Wear, and Lubrication”, CRC Press
LCC, USA, 2000.
[5] A. Sethuramiah, “Lubricated Wear Science and Technology”, Elsevier
Science Publishing, USA, 2003.
[6] M. Braunovic, N. Myshkin, and V. Konchits, “Fundamentals,
Applications and Technology”, CRC press LCC, USA, 2007.
[7] O. Mokhlessi, H. M. Rad, “Utilization of 4 types of Artificial Neural
Network on the diagnosis of valve-physiological heart disease from heart
sounds”, 17th Iranian Conference of Biomedical Engineering, Vol. 978,
pp. 4244-7484, Iran, 2010.
[8] L. Jinkun, “Radial Basis Function (RBF) Neural Network control for
Mechanical Systems”, Springer, USA, 2013.
[9] C. Otto, “Modeling a hydraulic drive using neural networks”,
Department of Measurement and Control, University of Duisburg,
Germany, 2000.
[10] Ruano, “Applications of neural networks to control systems”, University
of Wales, School of Electronic Engineering Science University of Wales,
Bangor, 1992.
[11] P. J. Blau, R. L. Martin, and L. Riester, “A comparison of several surface
finish measurement methods as applied to ground ceramic and metal
surfaces”, Oak Ridge National Laboratory (Ornl), USA, 1996.
[12] L. Mummery, “Surface Texture Analysis the Handbook”, Hommlwerke
GmbH, Germany, 1990.
[13] W. Yan, N. P. Dowd, E. Busso, “Numerical study of sliding wear caused
by a loaded pin on a rotating disc”, Journal of the Mechanics and Physics
of Solids, vol. 50, pp. 449 – 470, 2002. Elsevier Science Publishing,
USA, 2002.
[14] A. Sanchez, “Tribological characterization of carbon based solid
lubricants”, Master thesis, Texas A&M University, USA, 2011.
[15] Orvnas, “Simulation of rail wear on the Swedish light rail line
Tvarbanan”, Master thesis, Royal Institute of Technology (KTH),
Department of Engineering, Stockholm, 2005.
[16] ASTM, “Standard Test Method for Wear Testing with a Pin-on-Disk
Apparatus”, ASTM Publishing, Vol. 14.02, pp. 95 – 99, USA, 2000.
[17] N. X. Randall, “Tribological Characterization of Biomaterials”,
Materials for Medical Devices, ASM, ASM Handbook, ASM
International Press, Vol. 23, p 148–156, Switzerland, 2007.
[18] Steel stockholders, “EN8 Steel”, KV steel Ltd, UK, 2014.
http://kvsteel.co.uk/steel/EN8.html (online).
[19] Aalco, “Aluminum, Aalco metal Ltd, UK, 2014.
https://www.google.co.uk/webhp?sourceid=chrome-instant&ion=1&
espv=2&ie=UTF-8#q=Aluminium%20Alloy%206082 (online).
[20] N. B. Karayiannis, “Reformulated Radial Basis Neural Networks Trained
by Gradient Descent”, IEEE Trans. Neural Networks, vol. 10, pp. 657-
671, Aug. 2002.
[21] J. Moody, and C. Darken, “Fast learning in Networks of Locally-Tuned
Processing Units”, neural computation, Vol. 1, 1989.
[22] R. Mammone, “Artificial Neural Networks for Speech and Vision”, New
Jersey, USA, 1994.
[23] L. Fu, “Neural Networks in Computer Intelligence”, university of
Florida, 1994.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:69216", author = "A. Shebani and C. Pislaru", title = "Wear Measuring and Wear Modelling Based On Archard, ASTM, and Neural Network Models", abstract = "The wear measuring and wear modelling are
fundamental issues in the industrial field, mainly correlated to the
economy and safety. Therefore, there is a need to study the wear
measurements and wear estimation. Pin-on-disc test is the most
common test which is used to study the wear behaviour. In this paper,
the pin-on-disc (AEROTECH UNIDEX 11) is used for the
investigation of the effects of normal load and hardness of material on
the wear under dry and sliding conditions. In the pin-on-disc rig, two
specimens were used; one, a pin is made of steel with a tip, positioned
perpendicular to the disc, where the disc is made of aluminium. The
pin wear and disc wear were measured by using the following
instruments: The Talysurf instrument, a digital microscope, and the
alicona instrument. The Talysurf profilometer was used to measure
the pin/disc wear scar depth, digital microscope was used to measure
the diameter and width of wear scar, and the alicona was used to
measure the pin wear and disc wear. After that, the Archard model,
American Society for Testing and Materials model (ASTM), and
neural network model were used for pin/disc wear modelling.
Simulation results were implemented by using the Matlab program.
This paper focuses on how the alicona can be used for wear
measurements and how the neural network can be used for wear
estimation.
", keywords = "Wear measuring, Wear modelling, Neural Network,
Alicona.", volume = "9", number = "1", pages = "177-6", }
