Analysis of machining parameters effects on surface roughness: A review

International Journal of Computational Materials Science and Surface Engineering

Volumn 5, Issue 1, 2012, Pages 68-84

  Mahyar Khorasani, A ^a   Reza Soleymani Yazdi, M ^b   Safizadeh, Mir Saeed ^c  

^a Iran University of Industries and Mines IUIM   (Iran)

^b IMAM HOSSEIN UNIVERSITY   (Iran)

^c IRAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Iran)

Author keywords

Ai; Artificial intelligence; Machining parameter; Milling; Surface roughness; Turning process

Indexed keywords

ARTIFICIAL INTELLIGENCE; EXPERT SYSTEMS; GENETIC ALGORITHMS; MACHINING CENTERS; MILLING (MACHINING); NEURAL NETWORKS; ONLINE SYSTEMS; REAL TIME SYSTEMS; TURNING;

DYNAMIC PARAMETERS; EFFECTIVE PARAMETERS; KNOWLEDGE-BASED EXPERT SYSTEMS; MACHINING PARAMETERS; MACHINING PROCESS; REAL TIME; THREE-BRANCH; TURNING PROCESS;

SURFACE ROUGHNESS;

EID: 84866485163     PISSN: 17533465     EISSN: 17533473     Source Type: Journal    
DOI: 10.1504/ijcmsse.2012.049058     Document Type: Article

References (52)

1. Abburi, N. R. and Dixit, U. S. (2006) 'A knowledge-based system for the prediction of surface roughness in turning process', Robot. Comput. Integr. Manuf., Vol. 22, No. 3, pp. 363-372.
2. Abouelatta, O. B. and Madl, J. (2001) 'Surface roughness prediction based on cutting parameters and tool vibrations in turning operations', J. Mater. Process. Technol., Vol. 118, Nos. 1-3, pp. 269-277.
3. Alauddin, M., El Baradie, M. A. and Hashmi, M. S. J. (1995) 'Computer aided analysis of a surface roughness model for end milling', J. Mater. Process Techn., Vol. 55, pp. 123-127.
4. Arbizu, I. P. and Pérez, C. J. L. (2003) 'Surface roughness prediction by factorial design of experiments in turning processes', J. Mater. Process Tech., pp. 143-144, 390-396.
5. Azouzi, R. and Guillot, M. (1997) 'On-line prediction of surface finish and dimensional deviation in turning using neural network based sensor fusion', Int. J. Mach. Tools Manuf., Vol. 37, No. 9, pp. 1201-1217.
6. Baek, D. K., Ko, T. J. and Kim, H. S. (1997) 'A dynamic surface roughness model for face milling', Precis Eng., Vol. 20, No. 3, pp. 71-178.
7. Baek, D. K., Ko, T. J. and Kim, H. S. (2001) 'Optimization of feedrate in a face milling operation using a surface roughness model', Int. J. Mach. Tools Manuf., Vol. 41, No. 3, pp. 451-462.
8. Barry, J. and Byrne, G. (2001) 'Cutting tool wear in the machining of hardened steels, part II: CBN cutting tool wear', Wear, Vol. 247, No. 2, pp. 152-160.
9. Benardos, P. G. and Vosniakos, G. C. (2002) 'Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments', Robotics and Computer Integrated Manufacturing, Vol. 18, Nos. 5-6, pp. 343-354.
10. Brezocnik, M., Kovacic, M. and Ficko, M. (2004) 'Prediction of surface roughness with genetic programming', Journal of Materials Processing Technology, pp. 28-36, 157-158.
11. Changa, H., Kima, J., Kimb, H., Jangc, D. and Han, D. (2007) 'In-process surface roughness prediction using displacement signals from spindle motion', International Journal of Machine Tools & Manufacture, Vol. 47, pp. 1021-1026.
12. Choudhury, I. A. and El-Baradie, M. A. (1997) 'Surface roughness in the turning of high-strength steel by factorial design of experiments', J. Mater. Process Tech., Vol. 67, pp. 55-61.
13. Davim, J. P. (2001) 'A note on the determination of optimal cutting conditions for surface finish obtained in turning using design of experiments', J. Mater. Process Tech., Vol. 116, Nos.2/3, pp. 305-308.
14. Davim, P., Gaitondeb, V. N. and Karnikc, S. R. (2008) 'Investigations into the effect of cutting conditions on surface roughness in turning of free machining steel by ANN models', Journal of Materials Processing Technology, Vol. 205, pp. 16-23.
15. Dimla, E. and Dimla, S. (1999) 'Application of perceptron neural network to tool-state classification in a metal-turning operation', Engineering Application of Artificial Intelligence, Vol. 12, No. 4, pp. 471-477.
16. Diniz, A. E., Liu, J. J. and Dornfeld, D. A. (1992) 'Correlating tool life, tool wear and surface roughness by monitoring acoustic emission in finish turning', Wear, Vol. 152, pp. 395-407.
17. Durmus, H. K., Ozkaya, E. and Meric, C. (2006) 'The use of neural networks for the prediction of wear loss and surface roughness of AA 6351 aluminum alloy', Mater. Des., Vol. 27, pp. 156-159.
18. Elanayar, S. and Shin, Y. C. (1995) 'Robust tool wear estimation with radial basis function neural networks', ASME J of Dynamic Sys, Measurement and Control, Vol. 117, pp. 459-467.
19. Ezugwu, E. O., Fadare, D. A., Bonney, J., Da Silva, R. B. and Sales, W. F. (2005) 'Modelling the correlation between cutting and process parameters in high-speed machining of Inconel 718 alloy using an artificial neural network', Int. J. Mach. Tools Manuf., Vol. 45, pp. 1375-1385.
20. Feng, C. X. J. and Wang, X. (2002) 'Development of empirical models for surface roughness prediction in finish turning', Int. J. Adv. Manuf. Technol., Vol. 20, Nos. 2-3, pp. 348-356.
21. Fisher, H. L. and Elrod, J. T. (1971) 'Surface finish as a function of tool geometry and feed - a theoretical approach', Microtechnic, Vol. 25, No. 8, pp. 175-178.
22. Fuh, K-H. and Wu, C-F. (1995) 'A proposed statistical model for surface quality prediction in end milling of Al alloy', Int. J. Mach Tools Manuf, Vol. 35, No. 8, pp. 1187-1200.
23. Gong, W., Obikawa, T. and Shirakashi, T. (1997) 'Monitoring of tool wear states in turning based on wavelet analysis', JSME Int. J., Vol. 40, No. 3, pp. 447-453.
24. Ho, S., Lee, K., Chen, S. and Ho, S. (2002) 'Accurate modeling and prediction of surface roughness by computer vision in turning operations using an adaptive neurofuzzy inference system', International Journal of Machine Tools & Manufacture, Vol. 42, No. 13, pp. 1441-1446.
25. Hoa, W., Tsai, J., Lin, B. and Chou, J. (2009) 'Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm', Expert Systems with Applications, Vol. 36, pp. 3216-3222.
26. Huanga, B. P., Chen, J. C. and Li, Y. (2008) 'Artificial-neural-networks- based surface roughness Pokayoke system for end-milling operations', Neurocomputing, Vol. 71, Nos. 4-6, pp. 544-549.
27. Jiao, Y., Lei, S., Pei, Z. J. and Lee, E. S. (2004) 'Fuzzy adaptive networks in machining process modeling: surface roughness prediction for turning operations', International Journal of Machine Tools & Manufacture, Vol. 44, pp. 1643-1651.
28. Karayel, D. (2009) 'Prediction and control of surface roughness in CNC lathe using artificial neural network', J Mater Process Tech, Vol. 209, pp. 3125-3137.
29. Kim, B. H. and Chu, C. N. (1999) 'Texture prediction of milled surfaces using texture superposition method', Comput Aided Des, Vol. 31, No. 8, pp. 485-494.
30. Kirby, E., Chen, J. and Zhang, J. (2006) 'Development of a fuzzy-nets-based in-process surface roughness adaptive control system in turning operations', Expert Systems with Applications, Vol. 30, No. 4, pp. 592-604.
31. Ktem, O., Erzurumlu, T. and Kurtaran, H. (2005) 'Application of response surface methodology in the optimization of cutting conditions for surface roughness', J. Mater. Process Tech., Vol. 170, Nos. 1-2, pp. 11-16.
32. Lalwani, D. I., Mehta, N. K. and Jain, P. K. (2008) 'Experimental investigations of cutting parameters influence on cutting forces and surface roughness in finish hard turning of MDN250 steel', Journal of Materials Processing Technology, Vol. 206, pp. 167-179.
33. Lee, B. Y., Yu, S. F. and Juan, H. (2004) 'The model of surface roughness inspection by vision system in turning', Mechatronics, Vol. 14, No. 1, pp. 129-141.
34. Lee, K. Y., Kang, M. C., Jeong, Y. H., Lee, D. W. and Jeong, S. K. (2001) 'Simulation of surface roughness and profile in high-speed end milling', J. Mater. Process Tech., Vol. 113, Nos. 1-3, pp. 410-415.
35. Lin, W. S., Lee, B. Y. and Wu, C. L. (2001) 'Modeling the surface roughness and cutting force for turning', J Mater Process Tech, Vol. 108, No. 3, pp. 286-293.
36. Liu, Q. and Altintas, Y. (1999) 'On-line monitoring of flank wear in turning with multilayered feed-forward neural network', International Journal of Machine Tools and Manufacture, Vol. 39, No. 12, pp. 1945-1959.
37. Lo, S. (2003) 'An adaptive-network based fuzzy inference system for prediction of workpiece surface roughness in end milling', Journal of Materials Processing Technology, Vol. 142, No. 3, pp. 665-675.
38. Lu, C. (2008) 'Study on prediction of surface quality in machining process', Journal of Materials Processing Technology, Vol. 205, Nos. 1-3, pp. 439-450.
39. Mohd Zain, A., Haron, H. and Sharif, S. (2010) 'Prediction of surface roughness in the end milling machining using artificial neural network', Expert Systems with Applications, Vol. 37, No. 2, pp. 1755-1768.
40. Nalbanta, M., Gokkayab, H., Toktas, I. and Sur, G. (2009) 'The experimental investigation of the effects of uncoated, PVD and CVD-coated cemented carbide inserts and cutting parameters on surface roughness in CNC turning and its prediction using artificial neural networks', Robotics and Computer Integrated Manufacturing, Vol. 25, No. 1, pp. 211-223.
41. Nandi, A. K. and Pratihar, D. K. (2004) 'An expert system based on FBFN using a GA to predict surface finish in ultra-precision turning', Journal of Materials Processing Technology, Vol. 155, 1150-1156.
42. Narutaki, N. and Yamane, Y. (1979) 'Tool wear and cutting temperature of CBN tools in machining of hardened steels', Annals of the CIRP, Vol. 28, No. 1, pp. 23-28.
43. Nassirpour, F. and Wu, S. M. (1977) 'Statistical evaluation of surface finish and its relationship to cutting parameters in turning', Int. J. Mach. Tool. Des. Res., Vol. 17, No. 4, pp. 197-208.
44. Oktem, H., Erzurumlu, T. and Erzincanli, F. (2006) 'Prediction of minimum surface roughness in end milling mold parts using neural network and genetic algorithm', Materials and Design, Vol. 27, No. 9, pp. 735-744.
45. Ozel, T. and Karpat, Y. (2005) 'Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks', International Journal of Machine Tools & Manufacture, Vol. 45, Nos. 4-5, pp. 467-479.
46. Ozel, T., Hsu, T-K. and Zeren, E. (2005) 'Effects of cutting edge geometry, workpiece hardness, feed rate and cutting speed on surface roughness and forces in finish turning of hardened AISI H13 steel', International J Adv Manuf. Tech., Vol. 25, pp. 262-269.
47. Risbood, K. A., Dixit, U. S. and Sahasrabudhe, A. D. (2003) 'Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process', Journal of Materials Processing Technology, Vol. 132, Nos. 1-3, pp. 203-214.
48. Scheffer, C., Kratz, H., Heyns, P. and Klocke, F. (2003) 'Development of a tool wear-monitoring system for hard turning', International Journal of Machine Tools and Manufacture, Vol. 43, pp. 973-985.
49. Sick, B. (2002) 'On-line and indirect tool wear monitoring in turning with artificial neural networks: a review of more than a decade of research', Mechanical Systems and Signal Processing, Vol. 16, No. 4, pp. 487-546.
50. Suresh, P. V. S., Rao, P. and Deshmukh, S. G. (2002) 'A genetic algorithmic approach for optimization of surface roughness prediction model', International Journal of Machine Tools & Manufacture, Vol. 42, No. 6, pp. 675-680.
51. Taraman, K. (1974) 'Multi-machining output multi-independent variable turning research by response surface methodology', Int. J. Product. Res., Vol. 12, No. 2, pp. 233-245.
52. Topal, E. (2009) 'The role of step over ratio in prediction of surface roughness in flat end milling', International Journal of Mechanical Sciences, Vol. 51, Nos. 11-12, pp. 782-789.