Surface roughness prediction model for CNC machining of polypropylene

Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture

Volumn 222, Issue 2, 2008, Pages 137-153

  Dhokia, V G ^a   Kumar, S ^a   Vichare, P ^a   Newman, S T ^a   Allen, R D ^b  

^a University of Bath   (United Kingdom)

^b Soletec Systems Ltd   (United Kingdom)

Author keywords

Neural networks; Polypropylene; Slotmilling

Indexed keywords

ARCHITECTURAL DESIGN; ARTIFICIAL INTELLIGENCE; CHEMICALS; COMPUTER NETWORKS; ELECTRIC NETWORK TOPOLOGY; FRICTION; MACHINING; MATHEMATICAL MODELS; METAL ANALYSIS; METROPOLITAN AREA NETWORKS; NETWORK PROTOCOLS; NEURAL NETWORKS; PHOTORESISTS; PIGMENTS; PLASTIC PRODUCTS; PREDICTIVE CONTROL SYSTEMS; PROCESS DESIGN; PROCESS ENGINEERING; PROCESS PLANNING; STEEL ANALYSIS; SURFACE PROPERTIES; SURFACE ROUGHNESS; SURFACES; THERMOPLASTICS;

(1 1 0) SURFACE; (E ,3E) PROCESS; CNC MACHINING; DATA SETS; DEPTH OF CUT (DOC); EXTRACTING INFORMATION; FEED RATES; HARD MATERIALS; HYPOTHESIS TESTING; INDEPENDENT VARIABLES; LITTLE RESEARCH; MACHINING STRATEGIES; NETWORK TOPOLOGIES; OPTIMAL CUTTING; OPTIMAL MACHINING; PERSONALIZED PRODUCTS; PREDICTION MODELING; PREDICTIVE MODELING; SPINDLE SPEED (RPM); STRATEGY RESEARCH; TRAINING ALGORITHMS;

DESIGN OF EXPERIMENTS;

EID: 45749105080     PISSN: 09544054     EISSN: None     Source Type: Journal    
DOI: 10.1243/09544054JEM884     Document Type: Article

References (47)

1. Rohlfs, T. Plastic machining: understanding the basics, Medical Device and Diagnostic Industry, 2002, www.devicelink.com/mddi/archive/ 02/04/000/htm.
2. Surface roughness measurement instrumentation, www.taylor-hobson.com (accessed 13/09/06).
3. http://composite.about.com/library/glossary/s/bldef-s5326.htm (accessed 09/08/06).
4. Dickinson, G. R. A survey of factors affecting surface finish. In Proceedings of the Conference on Properties and metrology of surfaces, 1968, pp. 135-147 (Institute of Mechanical Engineering).
5. Sata, T. Surface finish in metal cutting. Ann. CIRP, 1963, 12(4), 190-197.
6. Feng, C.-X. and Wang, X.-F. Surface roughness predictive modelling: neural networks versus regression. IIE Trans., 2003, 35, 11-27.
7. Oktem, H., Erzurumlu, T., and Col, M. A study of the Taguchi optimisation method for surface roughness in finish milling of mold surfaces. Int. J. Adv. Manuf. Technol., 2006, 28, 694-700.
8. Zhong, Z. W., Khoo, L. P., and Han, S. T. Prediction of surface roughness of turned surfaces using neural networks. Int. J. Adv. Manuf. Technol., 2006, 27, 688-693.
9. Sonar, D. K., Dixit, U. S., and Ojha, D. K. The application of a radial basis function neural network for predicting the surface roughness in a turning process. Int. J. Adv. Manuf. Technol., 2006, 27, 661-666.
10. Ozcelik, B. and Bayramoglu, M. The statistical modelling of surface roughness in high speed flat end milling. Int. J. Mach. Tools and Manuf., 2006, 46, 1395-1402.
11. Feng, X.-C., Yu, Z.-G., and Kusiak, A. Selection and validation of predictive regression and neural network models based on designed experiments. HE Trans., 2006, 38, 13-23.
12. Abburi, N. R. and Dixit, U. S. A Knowledge-based system for the prediction of surface roughness in turning process. Robot. Comput. Integr. Manuf., 2006, 22, 363-372.
13. Ozcelik, B., Oktem, H., and Kurtaran, H. Optimum surface roughness in end milling Inconel 718 by coupling neural network model and genetic algorithm. Int. J. Adv. Manuf. Technol., 2005, 27, 234-241.
14. Ozel, T. and Karpat, Y. Predictive modelling of surface roughness and tool wear in hard turning using regression and neural networks. Int. J. Mach. Tools Manuf., 2005, 45, 467-479.
15. Reddy, N. S. K. and Rao, P. V. Selection of optimum tool geometry and cutting conditions using a surface roughness prediction model for end milling. Int. J. Adv. Manuf Technol., 2005, 26, 1202-1210.
16. Kohli, A. and Dixit, U. S. A neural network based methodology for the prediction of surface roughness in turning process. Int. J. Adv. Manuf Technol., 2005, 25, 118-129.
17. Pal, S. K. and Chakraborty, D. Surface roughness prediction in turning using artificial neural network. Neural Comput. Appl., 2005, 14, 319-324.
18. Ali, Y. M. and Zhang, L. C. A fuzzy model for predicting burns in surface grinding of steel. Int. J. Mach. Tools Manuf, 2004, 44, 563-571.
19. Lee, K. and Dornfeld, D. A. A study of surface rougness in the micro - end - muling process, eScholarship Repository, University of California, 2004.
20. Jiao, Y., Lei, S., Pei, Z. J., and Lee, E. S. Fuzzy adaptive networks in machining process modelling: surface roughness prediction for turning operations. Int. J. Mach. Took Manuf, 2004, 44, 1643-1651.
21. Hecker, R. L. and Liang, S. Y. Predictive modelling of surface roughness in grinding. Int. J. Mach. Tools Manuf, 2003, 43, 755-761.
22. Lee, S. S. and Chen, J. C. On-line surface roughness recognition system using artificial neural networks system in turning operations. Int. J. Adv. Manuf. Technol., 2003, 22, 498-509.
23. Choudhury, S. K. and and Baratarya, G. Role of temperature and surface finish in predicting tool wear using neural network and design of experiments. Int. J. Mach. Tools Manuf, 2003, 43, 747-753.
24. Brezocnik, M. and Kavacic, M. Integrated genetic programming and genetic algorithm approach to predict surface roughness. Materials and Manufacturing Processes, 2003, 18(3), 475-491.
25. El-Maunayri, H., Dugla, Z., and Deng, H. Prediction of surface roughness. In End milling using swarm intelligence, 2003, pp. 220-227(IEEE, Piscataway, New Jersey).
26. Mansour, A. and Abdalla, H. Surface roughness model for end milling: a semi-free cutting carbon case hardening steel (EN32) in dry condition. J. Mater. Process. Technol., 2002, 124, 183-191.
27. Zhou, X. and Xi, F. Modelling and predicting surface roughness of the grinding process. Int. J. Mach. Tools Manuf, 2002, 42, 969-977.
28. Balk, J. and Kovacic, M. Intelligent tool path generations for milling of free surfaces using neural networks. Int. J. Mach. Tools Manuf., 2002, 42, 1172-1179.
29. Kopac, J., Bahor, M., and Sokovic, M. Optimal machining parameters for achieving the desired surface roughness in fine turning of cold pre-formed steel workpieces. Int. J. Mach. Tools Manuf, 2002, 42, 707-716.
30. Ho, S.-Y., Lee, K.-C., Chen, S.-S., and Ho, S.-J. Accurate modelling and prediction of surface roughness by computer vision in turning operations using an adaptive neuro-fuzzy inference system. Int. J. Mach. Tools Manuf, 2002, 42, 1441-1446.
31. Benardos, P. G. and Vosinakos, G. C. Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments. Robot. Comput. Integr. Manuf, 2002, 18, 343-354.
32. Baek, D. K., Ko, T. J., and Kim, H. S. Optimization of feedrate in a face milling operation using a surface roughness model. Int. J. Mach. Tools Manuf, 2001, 41, 451-462.
33. Chen, J. C. and Savage, M. A fuzzy-net-based multilevel in-process surface roughness recognition system in milling operations. Int. J. Adv. Manuf. Technol., 2001, 17, 670-676.
34. Tsai, Y.-H., Chen, J. C., and Lou, S.-J. An in-process surface recognition system based on neural networks in end milling cutting operations. Int. J. Mach. Tools Manuf, 1999, 39, 583-605.
35. Ali, Y. M. and Zhang, L. C. Surface roughness prediction of ground components using fuzzy logic approach. J. Mater. Process. Technol., 1999, 89-90, 561-568.
36. Lou, S. J. and Chen, J. C. In-process surface roughness recognition (ISRR) system in end-milling operations. Int. J. Adv. Manuf. Technol., 1999, 15, 200-209.
37. Lou, M. S., Chen, J. C., and Li, C. M. Surface roughness prediction technique for CNC end-milling. J. Ind. Technol., 1999, 15(1), 2-6.
38. Alauddin, M., El Baradie, M. A., and Hashmi, M. S. J. Computer - aided analysis of a surface - roughness model for end milling. J. Mater. Process. Technol., 1995, 55, 123-127.
39. Brezocnik, M., Kovacic, M., and Ficko, M. Prediction of surface roughness with genetic programming. J. Mater. Process. Technol., 2004, 157-158, 28-36.
40. Deutsches institut fuer normung. Din 4760: Form deviations, concepts, classification system, 1982.
41. Coit, D. W., Jackson, B. T., and Smith, A. E. Static neural network process models: considerations and case studies. Int. J. Prod. Res., 1998, 36(11), 2953-2967.
42. Lawrence, J. Introduction to neural network: design, theory and applications, sixth edition, 1993 (California Scientific Software, Nevada City, California).
43. Hastie, T., Tibshirani, R., and Friedman, J. (eds). The elements of statistical learning: data mining, inference and prediction, 2001 (Springer, New York).
44. Ngia, H. S. L. and Sjoberg, J. Efficient training of neural nets for nonlinear adaptive filtering using a recursive Levenberg-Marquardt algorithm. IEEE Trans. Signal Process., 2000, 48(7), 1915-1927.
45. Ross, P. Taguchi techniques for quality engineering, 1996 (McGraw-Hill, New York).
46. Montgomery, D. C. Design, analyses of experiments, third edition, 1997 (John Wiley, New Jersey).
47. Dhokia, V. G., Kumar, S., Vicahre, P., and Newman, S. T. A surface roughness prediction model for the ballnose machining of polypropylene. In Proceedings of the 17th International Conference on Flexible automation and intelligent manufacturing, Philadelphia, Pennsylvania, 2007, pp. 211-218.