Development of Fuzzy Logic and Neuro-Fuzzy Surface Roughness Prediction Systems Coupled with Cutting Current in Milling Operation

Development of two real-time surface roughness (Ra) prediction systems for milling operations was attempted. The systems used not only cutting parameters, such as feed rate and spindle speed, but also the cutting current generated and corrected by a clamp type energy sensor. Two different approaches were developed. First, a fuzzy inference system (FIS), in which the fuzzy logic rules are generated by experts in the milling processes, was used to conduct prediction modeling using current cutting data. Second, a neuro-fuzzy system (ANFIS) was explored. Neuro-fuzzy systems are adaptive techniques in which data are collected on the network, processed, and rules are generated by the system. The inference system then uses these rules to predict Ra as the output. Experimental results showed that the parameters of spindle speed, feed rate, depth of cut, and input current variation could predict Ra. These two systems enable the prediction of Ra during the milling operation with an average of 91.83% and 94.48% accuracy by FIS and ANFIS systems, respectively. Statistically, the ANFIS system provided better prediction accuracy than that of the FIS system.

Authors:

• Joseph C. Chen
• Venkata Mohan Kudapa

Keywords:

• Surface roughness
• input current
• fuzzy logic
• neuro-fuzzy
• milling operations.

References:

[1] Honess, C., “Importance of Surface Finish in the Design of Stainless Steel,” British Stainless Steel Association. (Online). Available: https://www.bssa.org.uk/publications.php?id=97&featured=1. (Accessed: 06-Mar-2019)
[2] Lou, M.S., Chen, J.C., and C. M. Li, “Surface Roughness Prediction Technique for CNC End-Milling,” J. Ind. Technol., vol. 15, no. 1, pp. 1–6, 1998.
[3] Bhushan, B., Ed., “Surface Roughness Analysis and Measurement Techniques,” in Modern tribology handbook, vol. 1, 2 vols., Boca Raton, FL: CRC Press, 2001, pp. 49–120.
[4] Lou, S.-J., “Development of four in-process surface recognition systems to predict surface roughness in end milling,” Iowa State University, Ames, Iowa, USA, 1997.
[5] Huang, H., “The development of in-process surface roughness prediction systems in turning operation using accelerometer,” Iowa State University, Ames, Iowa, USA, 2001.
[6] Suhail, A. H., Ismail, N., and N. A. A. Jalil, “In-process Surface Roughness Prediction Using Heat Generation Rate of Workpiece Surface in Turning Operation,” IOP Conf. Ser. Mater. Sci. Eng., vol. 17, p. 012044, Feb. 2011.
[7] Benardos, P. and G.Vosniakos, “Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments,” Robot. Comput.-Integr. Manuf., vol. 18, no. 5–6, pp. 343–354, Oct. 2002.
[8] Patel, R. D., Oza, N. V., and S. N. Bhavsar, “Prediction of Surface Roughness in CNC Milling Machine by Controlling Machining Parameters Using ANN,” Int. J. Mech. Eng. Robot. Res., vol. 3, no. 4, pp. 353–359, Oct. 2014.
[9] Tseng, T.-L. (Bill), Konada, U., and Y. (James) Kwon, “A novel approach to predict surface roughness in machining operations using fuzzy set theory,” J. Comput. Des. Eng., vol. 3, no. 1, pp. 1–13, Jan. 2016.
[10] Kromanis, A. and J. Krizbergs, “Prediction of Surface Roughness in End-Milling using Fuzzy Logic and its Comparison to Regression Analysis,” presented at the Annals of DAAAM for 2009 & proceedings of the 20th International DAAAM Symposium, Vienna, Austria, 2009, vol. 1, pp. 803–804.
[11] Abraham, A., “Neuro Fuzzy Systems: State-of-the-Art Modeling Techniques,” in Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence, vol. 2084, J. Mira and A. Prieto, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001, pp. 269–276.
[12] Chen, J. C., and M. S. Lou, “Fuzzy-nets based approach to using an accelerometer for an in-process surface roughness prediction system in milling operations,” Int. J. Comput. Integr. Manuf., vol. 13, no. 4, pp. 358–368, Jan. 2000.
[13] Chen, J. C., and J. C. Chen, “A Statistics-Assisted Fuzzy-Nets-Based In-Process Tool Wear Prediction System in Milling Operations,” Int. J. Manuf. Sci. Technol., vol. 4, no. 2, pp. 84–101, 2003.
[14] Lo, S.-P., “An adaptive-network based fuzzy inference system for prediction of workpiece surface roughness in end milling,” J. Mater. Process. Technol., vol. 142, no. 3, pp. 665–675, Dec. 2003.
[15] Fuller, R., Introduction to Neuro-Fuzzy Systems. 2000.