Surface Topography Assessment Techniques based on an In-process Monitoring Approach of Tool Wear and Cutting Force Signature
The quality of a machined surface is becoming more and more important to justify the increasing demands of sophisticated component performance, longevity, and reliability. Usually, any machining operation leaves its own characteristic evidence on the machined surface in the form of finely spaced micro irregularities (surface roughness) left by the associated indeterministic characteristics of the different elements of the system: tool-machineworkpart- cutting parameters. However, one of the most influential sources in machining affecting surface roughness is the instantaneous state of tool edge. The main objective of the current work is to relate the in-process immeasurable cutting edge deformation and surface roughness to a more reliable easy-to-measure force signals using a robust non-linear time-dependent modeling regression techniques. Time-dependent modeling is beneficial when modern machining systems, such as adaptive control techniques are considered, where the state of the machined surface and the health of the cutting edge are monitored, assessed and controlled online using realtime information provided by the variability encountered in the measured force signals. Correlation between wear propagation and roughness variation is developed throughout the different edge lifetimes. The surface roughness is further evaluated in the light of the variation in both the static and the dynamic force signals. Consistent correlation is found between surface roughness variation and tool wear progress within its initial and constant regions. At the first few seconds of cutting, expected and well known trend of the effect of the cutting parameters is observed. Surface roughness is positively influenced by the level of the feed rate and negatively by the cutting speed. As cutting continues, roughness is affected, to different extents, by the rather localized wear modes either on the tool nose or on its flank areas. Moreover, it seems that roughness varies as wear attitude transfers from one mode to another and, in general, it is shown that it is improved as wear increases but with possible corresponding workpart dimensional inaccuracy. The dynamic force signals are found reasonably sensitive to simulate either the progressive or the random modes of tool edge deformation. While the frictional force components, feeding and radial, are found informative regarding progressive wear modes, the vertical (power) components is found more representative carrier to system instability resulting from the edge-s random deformation.
[1] Machinability Data Handbook Machinability Data, Machinability Data
Center, 2nd Edition 1972.
[2] C. Y. H. Lim, S. C. Lim and K. S. Lee , "Machining conditions and the
wear of TiC-coated tools", Wear Progress in Manufacturing, ASTM.
STP 1362, pp. 57-69, 1999.
[3] S. E. Oraby and D. R. Hayhurst, "Development of models for tool wear
force relationships in metal cutting," International Journal of
Mechanical Science, vol. 33, no. 2, pp. 125-138, 1991.
[4] S. E. Oraby, "Monitoring of machining processes via force signals - Part
I: Recognition of different tool failure forms by spectral analysis," Wear,
vol. 33, pp. 133-143, 1995.
[5] J. W. Youn and M. Y. Yang, "A study on the relationships between
static/dynamic cutting force components and tool wear," Trans ASME,
Journal of Manufacturing Science and Engineering, vol. 123, no. 2, pp.
196-205, 2001.
[6] S. E. Oraby and D. R. Hayhurst, "Tool life determination based on the
measurement of wear and tool force ratio variation," International
Journal of Machine Tools & Manufacture, vol. 44, pp. 1261-1269, 2004.
[7] S. E. Oraby, A. F. Al-Mudhaf and D. R. Hayhurst, "A diagnostic
approach for turning tool based on the dynamic force signals," Trans.
ASME, Journal of Manufacturing Science and Engineering, vol. 127, pp.
463-475, 2005.
[8] A. Zehnder, Y. Potdor and X. Deng, "Measurement and simulation of
temperature and strain fields in orthogonal metal cutting," International
Congress of Theoretical and Applied Mechanics (ICTAM 2000).
[9] G. Dawson, "Machining hardened steel with polycrystalline cubic boron
nitride cutting tools," Ph.D. thesis, Georgia Institute of Technology,
2002.
[10] H. Shao, H. L. Wang and X. M. Zhao, "A cutting power model for tool
wear monitoring in milling," International Journal of Machine Tools &
Manufacture, vol. 44, pp. 1503-1509, 2004.
[11] S. C. Lin and M. F. Chang, "A study on the effects of vibrations on the
surface finish using a surface topography simulation model for turning,"
International Journal of Machine Tools and Manufacture, vol. 38, no. 7,
pp. 763-782, 1998.
[12] S. V. Kamarthi and S. R. T. Kumara, "Flank wear estimation in turning
through wavelet representation of acoustic emission signals," Trans
ASME, Journal of Manufacturing Science and Engineering, vol. 122, no.
1, pp. 12-19, 2000.
[13] C. XiaoQi., Z. Hao and D. Wildermuth, "In-process tool wear
monitoring through acoustic emission sensing," SIMTECH Technical
report (AT/01/014/AMP), Automated Material Processing Group.
Automation Technology Division, Singapore Institute of Manufacturing
Technology, 2001.
[14] S. Y. Liang, R. L. Hecker and Landers, "Machining processes
monitoring and control: the state-of-the-art," in Proc IMECE-2 ASME
International Mechanical Engineering Congress & Exposition, New
Orleans, Louisiana, USA, 2002.
[15] S. K. Choudhury, V. K. Jain and S. R. Krishna, "On-line monitoring of
tool wear and control of dimensional inaccuracy in turning," Trans.
ASME, Journal of Manufacturing Science and Engineering, vol. 123,
no. 1, pp. 10-12, 2001.
[16] X. D. Fang and H. S. J. Shahi, "A new algorithm for developing a
reference-based model for predicting surface roughness in finish
machining of steels," International Journal of Production Research, vol.
35, no. 1, pp. 179-199, 1997.
[17] R. Azouzi and M. Guillot, "Online prediction of surface finish and
dimensional deviation in turning using neural network based sensor
fusion," International Journal of Machine Tools and Manufacture, vol.
37, no. 9, pp. 1201-1217, 1997.
[18] P. V. S. Suresh, P. V. Rao and S. G. Deshmukh, "A genetic algorithm
approach for optimization of surface roughness prediction model,"
International journal of Machine Tools & Manufacture, vol. 42, no. 6,
pp. 675-680, 2002.
[19] C. S. Rao and R. R. Srikant, "Tool wear monitoring - An intelligent
approach," in Proc. Institution of Mechanical Engineering
(IMECH2004), Part B: Journal of Engineering Manufacture, vol. 218,
pp. 905-912, 2004.
[20] E. O. Ezugwu, D. A. Fadare, J. Bonney, R. D. Das Silvaand W. F.
Sales, "Modelling the correlation between cutting and process
parameters in high-speed machining of Inconel 718 alloy using an
artificial neural network," International Journal of Machine Tools &
Manufacture, vol. 45, pp. 1375-1385, 2005.
[21] P. Balakrishanan and M. F. DeVries, "Analysis of mathematical model
building techniques adaptable data base systems," in Proc. NAMRC-XI,
pp. 466-475, 1983.
[22] N. R. Draper and H. Smith, Applied regression analysis, Wiley, New
York, NY, USA, 1967.
[23] W. W. Hines and D. C. Montgomery, Probability and statistics in
engineering and management Science, John Wily&Sons, 2nd edition,
1982.
[24] S. E. Oraby and D. R. Hayhurst, "High-capacity compact threecomponent
cutting force dynamometer," International Journal of
Machine Tools & Manufacturing, vol. 30, no. 4, pp. 549-559, 1990.
[25] M. Zecchino, "Why average roughness is not enough: advances in three
dimensional measurement techniques such as optical profiling have
given engineers, process designers, and quality control professionals a
significantly improved toolkit for describing surfaces," ASM, Advances
Materials & Processes, 2003.
[26] G. Schuetz, "Rx for Ra measurements - average roughness," Modern
Machine Shop (www.mmsonline.com), 1994.
[27] J. Chen and S. Lee, "On-line surface roughness system using an
accelerometers in turning operations," Journal of Engineering
Technolog, 2003.
[28] W. Grzesk and T. Wanat, "Comparative assessment of surface roughness
produced by hard machining with mixed ceramic tools including 2D and
3D analysis," Journal of Materials processing Technology, vol. 169, no.
3, pp. 364-371, 2005.
[1] Machinability Data Handbook Machinability Data, Machinability Data
Center, 2nd Edition 1972.
[2] C. Y. H. Lim, S. C. Lim and K. S. Lee , "Machining conditions and the
wear of TiC-coated tools", Wear Progress in Manufacturing, ASTM.
STP 1362, pp. 57-69, 1999.
[3] S. E. Oraby and D. R. Hayhurst, "Development of models for tool wear
force relationships in metal cutting," International Journal of
Mechanical Science, vol. 33, no. 2, pp. 125-138, 1991.
[4] S. E. Oraby, "Monitoring of machining processes via force signals - Part
I: Recognition of different tool failure forms by spectral analysis," Wear,
vol. 33, pp. 133-143, 1995.
[5] J. W. Youn and M. Y. Yang, "A study on the relationships between
static/dynamic cutting force components and tool wear," Trans ASME,
Journal of Manufacturing Science and Engineering, vol. 123, no. 2, pp.
196-205, 2001.
[6] S. E. Oraby and D. R. Hayhurst, "Tool life determination based on the
measurement of wear and tool force ratio variation," International
Journal of Machine Tools & Manufacture, vol. 44, pp. 1261-1269, 2004.
[7] S. E. Oraby, A. F. Al-Mudhaf and D. R. Hayhurst, "A diagnostic
approach for turning tool based on the dynamic force signals," Trans.
ASME, Journal of Manufacturing Science and Engineering, vol. 127, pp.
463-475, 2005.
[8] A. Zehnder, Y. Potdor and X. Deng, "Measurement and simulation of
temperature and strain fields in orthogonal metal cutting," International
Congress of Theoretical and Applied Mechanics (ICTAM 2000).
[9] G. Dawson, "Machining hardened steel with polycrystalline cubic boron
nitride cutting tools," Ph.D. thesis, Georgia Institute of Technology,
2002.
[10] H. Shao, H. L. Wang and X. M. Zhao, "A cutting power model for tool
wear monitoring in milling," International Journal of Machine Tools &
Manufacture, vol. 44, pp. 1503-1509, 2004.
[11] S. C. Lin and M. F. Chang, "A study on the effects of vibrations on the
surface finish using a surface topography simulation model for turning,"
International Journal of Machine Tools and Manufacture, vol. 38, no. 7,
pp. 763-782, 1998.
[12] S. V. Kamarthi and S. R. T. Kumara, "Flank wear estimation in turning
through wavelet representation of acoustic emission signals," Trans
ASME, Journal of Manufacturing Science and Engineering, vol. 122, no.
1, pp. 12-19, 2000.
[13] C. XiaoQi., Z. Hao and D. Wildermuth, "In-process tool wear
monitoring through acoustic emission sensing," SIMTECH Technical
report (AT/01/014/AMP), Automated Material Processing Group.
Automation Technology Division, Singapore Institute of Manufacturing
Technology, 2001.
[14] S. Y. Liang, R. L. Hecker and Landers, "Machining processes
monitoring and control: the state-of-the-art," in Proc IMECE-2 ASME
International Mechanical Engineering Congress & Exposition, New
Orleans, Louisiana, USA, 2002.
[15] S. K. Choudhury, V. K. Jain and S. R. Krishna, "On-line monitoring of
tool wear and control of dimensional inaccuracy in turning," Trans.
ASME, Journal of Manufacturing Science and Engineering, vol. 123,
no. 1, pp. 10-12, 2001.
[16] X. D. Fang and H. S. J. Shahi, "A new algorithm for developing a
reference-based model for predicting surface roughness in finish
machining of steels," International Journal of Production Research, vol.
35, no. 1, pp. 179-199, 1997.
[17] R. Azouzi and M. Guillot, "Online prediction of surface finish and
dimensional deviation in turning using neural network based sensor
fusion," International Journal of Machine Tools and Manufacture, vol.
37, no. 9, pp. 1201-1217, 1997.
[18] P. V. S. Suresh, P. V. Rao and S. G. Deshmukh, "A genetic algorithm
approach for optimization of surface roughness prediction model,"
International journal of Machine Tools & Manufacture, vol. 42, no. 6,
pp. 675-680, 2002.
[19] C. S. Rao and R. R. Srikant, "Tool wear monitoring - An intelligent
approach," in Proc. Institution of Mechanical Engineering
(IMECH2004), Part B: Journal of Engineering Manufacture, vol. 218,
pp. 905-912, 2004.
[20] E. O. Ezugwu, D. A. Fadare, J. Bonney, R. D. Das Silvaand W. F.
Sales, "Modelling the correlation between cutting and process
parameters in high-speed machining of Inconel 718 alloy using an
artificial neural network," International Journal of Machine Tools &
Manufacture, vol. 45, pp. 1375-1385, 2005.
[21] P. Balakrishanan and M. F. DeVries, "Analysis of mathematical model
building techniques adaptable data base systems," in Proc. NAMRC-XI,
pp. 466-475, 1983.
[22] N. R. Draper and H. Smith, Applied regression analysis, Wiley, New
York, NY, USA, 1967.
[23] W. W. Hines and D. C. Montgomery, Probability and statistics in
engineering and management Science, John Wily&Sons, 2nd edition,
1982.
[24] S. E. Oraby and D. R. Hayhurst, "High-capacity compact threecomponent
cutting force dynamometer," International Journal of
Machine Tools & Manufacturing, vol. 30, no. 4, pp. 549-559, 1990.
[25] M. Zecchino, "Why average roughness is not enough: advances in three
dimensional measurement techniques such as optical profiling have
given engineers, process designers, and quality control professionals a
significantly improved toolkit for describing surfaces," ASM, Advances
Materials & Processes, 2003.
[26] G. Schuetz, "Rx for Ra measurements - average roughness," Modern
Machine Shop (www.mmsonline.com), 1994.
[27] J. Chen and S. Lee, "On-line surface roughness system using an
accelerometers in turning operations," Journal of Engineering
Technolog, 2003.
[28] W. Grzesk and T. Wanat, "Comparative assessment of surface roughness
produced by hard machining with mixed ceramic tools including 2D and
3D analysis," Journal of Materials processing Technology, vol. 169, no.
3, pp. 364-371, 2005.
@article{"International Journal of Engineering, Mathematical and Physical Sciences:55236", author = "A. M. Alaskari and S. E. Oraby", title = "Surface Topography Assessment Techniques based on an In-process Monitoring Approach of Tool Wear and Cutting Force Signature", abstract = "The quality of a machined surface is becoming more and more important to justify the increasing demands of sophisticated component performance, longevity, and reliability. Usually, any machining operation leaves its own characteristic evidence on the machined surface in the form of finely spaced micro irregularities (surface roughness) left by the associated indeterministic characteristics of the different elements of the system: tool-machineworkpart- cutting parameters. However, one of the most influential sources in machining affecting surface roughness is the instantaneous state of tool edge. The main objective of the current work is to relate the in-process immeasurable cutting edge deformation and surface roughness to a more reliable easy-to-measure force signals using a robust non-linear time-dependent modeling regression techniques. Time-dependent modeling is beneficial when modern machining systems, such as adaptive control techniques are considered, where the state of the machined surface and the health of the cutting edge are monitored, assessed and controlled online using realtime information provided by the variability encountered in the measured force signals. Correlation between wear propagation and roughness variation is developed throughout the different edge lifetimes. The surface roughness is further evaluated in the light of the variation in both the static and the dynamic force signals. Consistent correlation is found between surface roughness variation and tool wear progress within its initial and constant regions. At the first few seconds of cutting, expected and well known trend of the effect of the cutting parameters is observed. Surface roughness is positively influenced by the level of the feed rate and negatively by the cutting speed. As cutting continues, roughness is affected, to different extents, by the rather localized wear modes either on the tool nose or on its flank areas. Moreover, it seems that roughness varies as wear attitude transfers from one mode to another and, in general, it is shown that it is improved as wear increases but with possible corresponding workpart dimensional inaccuracy. The dynamic force signals are found reasonably sensitive to simulate either the progressive or the random modes of tool edge deformation. While the frictional force components, feeding and radial, are found informative regarding progressive wear modes, the vertical (power) components is found more representative carrier to system instability resulting from the edge-s random deformation.
", keywords = "Dynamic force signals, surface roughness (finish),
tool wear and deformation, tool wear modes (nose, flank)", volume = "3", number = "1", pages = "6-11", }
