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2020 | OriginalPaper | Chapter

Modelling and Optimization of Surface Roughness Parameters of Stainless Steel by Artificial Intelligence Methods

Authors : Pavel Kovač, Borislav Savković, Dragan Rodić, Andjelko Aleksić, Marin Gostimirović, Milenko Sekulić, Nenad Kulundžić

Published in: Proceedings of the International Symposium for Production Research 2019

Publisher: Springer International Publishing

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Abstract

The objective of this study is to examine the influence of machining parameters on surface finish in turning of medical steel. A new approach in modeling surface roughness which uses design of experiments is described in this paper. The values of surface roughness predicted by different models are then compared. Used were adaptive-neuro-fuzzy-inference system (ANFIS). The results showed that the proposed system can significantly increase the accuracy of the product profile when compared to the conventional approaches. The results indicate that the design of experiments with central composition plan modeling technique can be effectively used for the prediction of the surface roughness for medical steel difficult to machining. Optimizations of surface roughness parameters was done by use of ant colony method.

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Literature
1.
Chen, J.C., Savage, M.: A fuzzy-net-based multilevel in-process surface roughness recognition system in milling operations. Int. J. Adv. Manuf. Technol. 17, 670–676 (2001)CrossRef
2.
Quintana, G., Garcia-Romeu, M.L., Ciurana, J.: Surface roughness monitoring application based on artificial neural networks for ball-end milling operations. J. Intell. Manuf. 22, 607–617 (2009)CrossRef
3.
Sivarao, Castillo, Taufik: Machining quality predictions: comparative analysis of neural network and fuzzy logic. Int. J. Electr. Comput. Sci. IJECS 9, 451–456 (2000)
4.
Drégelyi-Kiss, Á., Horváth, R., Mikó, B.: Design of experiments (DOE) in investigation of cutting technologies, Development in Machining Technology DIM, Cracow, pp. 20–34 (2013)
5.
Maňková, I., Vrabeľ, M., Beňo, J., Kovač, P., Gostimirovic, M.: Application of Taguchi method and surface response methodology to evaluate of mathematical models for chip deformation when drilling with coated and uncoated twist drills. Manuf. Technol. 13(4), 492–499 (2013)
6.
Hadi, Y., Ahmed, S.G.: Assessment of surface roughness model for turning process. In: Knowledge Enterprise: Intelligent Strategies in Product Design, Manufacturing, and Management. International Federation for Information Processing (IFIP), vol. 207, pp. 152–158 (2006)
7.
Chen, C.C., Chiang, K.T., Chou, C.C., Liao, Y.C.: The use of D-optimal design for modeling and analyzing the vibration and surface roughness in the precision turning with a diamond cutting tool. Int. J. Adv. Manuf. Technol. 54, 465–478 (2011)CrossRef
8.
Choudhary, A., Harding, J., Tiwari, M.: Data mining in manufacturing: A review based on the kind of knowledge. J. Intell. Manuf. 20(5), 501–521 (2009)CrossRef
9.
Grzenda, M., Bustillo, A., Zawistowski, P.: A soft computing system using intelligent imputation strategies for roughness prediction in deep drilling. J. Intell. Manuf. 23, 1733–1743 (2012)CrossRef
10.
Balic, J., Korosec, M.: Intelligent tool path generation for milling of free surfaces using neural networks. Int. J. Mach. Tools Manuf. 42, 1171–1179 (2002)CrossRef
11.
Pérez, C.J.L.: Surface roughness modeling considering uncertainty in measurements. Int. J. Prod. Res. 40(10), 2245–2268 (2002)
12.
Azouzi, R., Gullot, M.: On-line prediction of surface finish and dimensional deviation in turning using neural network based sensor fusion. Int. J. Mach. Tools Manuf. 37(9), 1201–1217 (1997)CrossRef
13.
Ho, S.Y., Lee, K.C., Chen, S.S., Ho, S.J.: Accurate modeling and prediction of surface roughness by computer vision in turning operations using an adaptive neuro-fuzzy inference system. Int. J. Mach. Tools Manuf. 42(13), 1441–1446 (2002)CrossRef
14.
Zębala, W., Gawlik, J., Matras, A., Struzikiewicz, G., Ślusarczyk, Ł.: Research of surface finish during titanium alloy turning. Key Eng. Mater. 581, 409–414 (2014)CrossRef
15.
Rajasekaran, T., Palanikumar, K., Vinayagam, B.K.: Application of fuzzy logic for modeling surface roughness in turning CFRP composites using CBN tool. Prod. Process. 5(2), 191–199 (2011)
16.
Savković, B., Kovač, P., Gerić, K., Sekulić, M., Rokosz, K.: Application of neural network for determination of cutting force changes versus instantaneous angle in face milling. J. Prod. Eng. 16(2), 25–28 (2013)
17.
Kundrák, J., Varga, G.: Use of coolants and lubricants in hard machining. Tech. Gaz. 20(6), 1081–1086 (2013)
18.
Kovac, P., Rodic, D., Pucovsky, V., Savkovic, B., Gostimirovic, M.: Application of fuzzy logic and regression analysis for modeling surface roughness in face milling. J. Intell. Manuf. 24(4), 755–762 (2013)CrossRef
19.
Jang, J.S.R., Sun, C.T.: Neuro-fuzzy modeling and control. Proc. IEEE 83, 378–406 (1995)CrossRef
20.
Ying, L.C., Pan, M.C.: Using adaptive network based fuzzy inference system to forecast regional electricity loads. Energy Conversat. Manag. 49, 205–211 (2008)CrossRef
21.
Avci, E.: Comparison of wavelet families for texture classification by using wavelet packet entropy adaptive network based fuzzy inference system. Appl. Soft Comput. 8, 225–231 (2008)CrossRef
22.
Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B Cybern. 26(1), 29–41 (1996)CrossRef
Metadata
Title
Modelling and Optimization of Surface Roughness Parameters of Stainless Steel by Artificial Intelligence Methods
Authors
Pavel Kovač
Borislav Savković
Dragan Rodić
Andjelko Aleksić
Marin Gostimirović
Milenko Sekulić
Nenad Kulundžić
Copyright Year
2020
Book
Proceedings of the International Symposium for Production Research 2019

Print ISBN: 978-3-030-31342-5

Electronic ISBN: 978-3-030-31343-2

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-31343-2_1

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