Uncertainty analysis of cutting parameters during grinding based on RSM optimization and Monte Carlo simulation

Mehmet Fatih Kahraman; Sabri Öztürk

doi:10.3139/120.111443

Article

Uncertainty analysis of cutting parameters during grinding based on RSM optimization and Monte Carlo simulation

Mehmet Fatih Kahraman and Sabri Öztürk

Published/Copyright: November 18, 2019

Published by

Become an author with De Gruyter Brill

Submit Manuscript Author Information

From the journal Materials Testing Volume 61 Issue 12

Abstract

Due to the importance of high surface quality of machined parts, regarding its functional requirements, it is necessary to determine an appropriate set of grinding parameters. According to the uncertainty of the machining process, the statistical techniques have recently been used to set up an experimental-based model for estimating the performance of machining parameters and optimizing them. The purpose of this study is to demonstrate the modeling and optimization of the grinding process using three approaches. First, multi non-linear regression (MNLR) based on central composite design (CCD) was used to determine the process model. Then the grinding parameters were optimized considering response surface methodology (RSM). Finally, the probabilistic uncertainty analysis was applied by using Monte Carlo simulation as a function of wheel speed and feed rate. The surface roughness value, which was named the response variable, was estimated by fitting the MNLR model with a predicted regression coefficient (R²_pred) of 84.69 %. Wheel speed of 4205.6 rpm and feed rate of 2.969 mm × min⁻¹ were calculated as RSM-optimized conditions with a surface roughness of 2.26326 μm. The verification experiments were performed with three replications to verify the predicted surface roughness value obtained with the derived model, and 2.263 ± 2 % μm of surface roughness was calculated using RSM optimized conditions. Monte Carlo simulations were found to be quite effective for identification of the uncertainties in surface roughness that could not be identified by deterministic ways.

*Correspondence Address, Mehmet Fatih Kahraman, Mechanical Engineering Department, Gölköy Campus, Bolu Abant Izzet Baysal University, Bolu 14280, Turkey, E-mail: mfkahraman@gmail.com

Mehmet Fatih Kahraman completed his BSc degree at Kocaeli University in Kocaeli, Turkey. He achieved his MSc degree at Bolu Abant Izzet Baysal University in Bolu, Turkey. He also achieved an MBA degree in 2014 at TOBB University in Ankara, Turkey. He has been studying to receive a PhD degree in Mechanical Engineering at Sakarya University in Sakarya, Turkey, since 2015, and has worked at Bolu Abant Izzet Baysal University as a research and teaching assistant since 2013.

Dr. Sabri Ozturk completed his BSc and MSc degrees at Yıldız Technical University in Istanbul, Turkey. He also received his PhD in Mechanical Engineering from that University in 2009. Throughout his studies from September 2003 to May 2012, he worked as an engineer with the Erdemir Iron and Steel Company (Zonguldak), Turkey. Currently, he is pursuing further studies at the Department of Mechanical Engineering at Abant Izzet Baysal University in Bolu, Turkey.

References

1 S.Ozturk: Grinding of flat glass with Fe-and Cu-based diamond tools, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture232 (2018), No. 9, pp. 1561–156810.1177/0954405416673113Search in Google Scholar

2 S.Öztürk: Microstructural analysis of metal-bond diamond tools in grinding of flat glass: Gefügeanalyse von metallisch gebundenen Diamantwerkzeugen beim Schleifen von Flachglas, Materialwissenschaft und Werkstofftechnik45 (2014), No. 3, pp. 187–19110.1002/mawe.201400199Search in Google Scholar

3 P.Selvaraj, P.Radhakrishnan, M.Adithan: An integrated approach to design for manufacturing and assembly based on reduction of product development time and cost, The International Journal of Advanced Manufacturing Technology42 (2009), No. 1-2, pp. 13–2910.1007/s00170-008-1580-8Search in Google Scholar

4 T.Matsumura, P.Aristimuno, E.Gandarias, P. J.Arrazola: Cutting process in glass peripheral milling, Journal of Materials Processing Technology213 (2013), No. 9, pp. 1523–153110.1016/j.jmatprotec.2013.02.002Search in Google Scholar

5 T. C.Science, Industry and Technology Ministry of Industry Directorate-General, Sectorial Overview report, Turkey (2016)Search in Google Scholar

6 A. V.Popov: Increasing the efficiency of diamond edging of flat glass, glass and ceramics66 (2009), No. 5, pp. 210–21110.1007/s10717-009-9176-xSearch in Google Scholar

7 M. F.Kahraman, H.Bilge, M.Karağaç, S.Öztürk: Performance of the copper based grinding wheels, Bilge International Journal of Science and Technology Research1 (2017), Special Issue, pp. 26–31Search in Google Scholar

8 B.Denkena, T.Grove, I.Bremer, L.Behrens: Design of bronze-bonded grinding wheel properties, CIRP Annals65 (2016), No. 1, pp. 333–33610.1016/j.cirp.2016.04.096Search in Google Scholar

9 K.Wegener, H. W.Hoffmeister, B.Karpuschewski, F.Kuster, W. C.Hahmann, M.Rabiey: Conditioning and monitoring of grinding wheels, CIRP Annals-Manufacturing Technology60 (2011), No. 2, pp. 757–77710.1016/j.cirp.2011.05.003Search in Google Scholar

10 M. F.Kahraman, H.Bilge, S.Öztürk: Uncertainty analysis of milling parameters using Monte Carlo simulation, the Taguchi optimization method and data-driven modeling, Materials Testing61 (2019) No. 5, pp. 477–48310.3139/120.111344Search in Google Scholar

11 D. Z.Duan, B.Xiao, W.Wang, Z. Y.Zhang, B.Wang, P.Han, X. Y.Ding: Interface characteristics and performance of pre-brazed diamond grains with Ni–Cr composite alloy, Journal of Alloys and Compounds644 (2015), pp. 626–63110.0.3.248/j.jallcom.2015.03.269Search in Google Scholar

12 X.Chen, W. B.Rowe: Analysis and simulation of the grinding process. Part I: Generation of the grinding wheel surface, International Journal of Machine Tools and Manufacture36 (1996), No. 8, pp. 871–88210.1016/0890-6955(96)00116-2Search in Google Scholar

13 M. F.Kahraman, S.Öztürk: Experimental study of newly structural design grinding wheel considering response surface optimization and Monte Carlo simulation, Journal of the International Measurement Confederation147 (2019), pp. 1–1210.1016/j.measurement.2019.07.053Search in Google Scholar

14 M.Zeren, Ş.Karagöz: Sintering of polycrystalline diamond cutting tools, Materials & Design28 (2007), No. 3, pp. 1055–105810.1016/j.matdes.2005.09.018Search in Google Scholar

15 Y.Wu, P. D.Funkenbusch: Microstructure and mechanical properties of commercial, bronze-bond, diamond-abrasive tool materials, Journal of Materials Science45 (2010), No. 1, pp. 251–25810.1007/s10853-009-3927-4Search in Google Scholar

16 F.Kahraman, S.Karadeniz, H.Durmuş: ANN-based wear performance prediction for plasma nitrided Ti6Al4 V alloy, Materials Testing54 (2012), No. 1, pp. 30–3510.3139/120.110289Search in Google Scholar

17 S.Bora, H.Kurtaran: Optimization of process parameters for rectangular cup deep drawing by the Taguchi method and genetic algorithm, Materials Testing58 (2016), No. 3, pp. 238–24510.3139/120.110840Search in Google Scholar

18 P.Lezanski, M.Pilacinska: The dominance-based rough set approach to cylindrical plunge grinding process diagnosis, Journal of Intelligent Manufacturing29 (2018), No. 5, pp. 989–100410.1007/s10845-016-1230-1Search in Google Scholar

19 I.Maher, M. E. H.Eltaib, A. A.Sarhan, R. M.El-Zahry: Cutting force-based adaptive neuro-fuzzy approach for accurate surface roughness prediction in end milling operation for intelligent machining, The International Journal of Advanced Manufacturing Technology76 (2015), No. 5-8, pp. 1459–146710.1007/s00170-014-6379-1Search in Google Scholar

20 S.Ozturk: Application of ANOVA and Taguchi methods for evaluation of the surface roughness of stellite-6 coating material, Materials Testing56 (2014), No. 11-12, pp. 1015–102010.3139/120.110665Search in Google Scholar

21 A.Uysal, M.Altan, E.Altan: Effects of cutting parameters on tool wear in drilling of polymer composite by Taguchi method, The International Journal of Advanced Manufacturing Technology58 (2012), No. 9-12, pp. 915–92110.1007/s00170-011-3464-6Search in Google Scholar

22 P. G.Benardos, G-C.Vosniakos: Predicting surface roughness in machining: a review, International Journal of Machine Tools and Manufacture43 (2003), No. 8, pp. 833–84410.1016/S0890-6955(03)00059-2Search in Google Scholar

23 S.Ozturk: Application of the Taguchi method for surface roughness predictions in the turning process, Materials Testing58 (2016), No. 9, pp. 782–78710.3139/120.110917Search in Google Scholar

24 E.Kuram, B.Ozcelik: Multi-objective optimization using Taguchi based grey relational analysis for micro-milling of Al 7075 material with ball nose end mill, Journal of the International Measurement Confederation46 (2013), No. 6, pp. 1849–186410.1016/j.measurement.2013.02.002Search in Google Scholar

25 S.Öztürk, M. F.Kahraman: Modeling and optimization of machining parameters during grinding of flat glass using response surface methodology and probabilistic uncertainty analysis based on Monte Carlo simulation, Journal of the International Measurement Confederation145 (2019), pp. 274–29110.1016/j.measurement.2019.05.098Search in Google Scholar

26 D.Murat, C.Ensarioglu, N.Gursakal, A.Oral, M. C.Cakir: Surface roughness analysis of greater cutting depths during hard turning. Materials Testing59 (2017), No. 9, pp. 795–80210.3139/120.111074Search in Google Scholar

27 S.Ozturk, E.Altan: Position of the separation point in machining with a rounded-edge tool, Proceedings of the Institution of Mechanical Engineers, Part B-Journal of Engineering Manufacture227 (2013), No. 7, pp. 965–97110.1177/0954405413484137Search in Google Scholar

28 A.Uysal: Cutting parameters for optimum surface roughness during end milling of carbon fiber reinforced composites. Materials Testing58 (2016), No. 6, pp. 588–59310.3139/120.110881Search in Google Scholar

29 T.Cobanoglu, S.Ozturk: Effect of burnishing parameters on the surface quality and hardness, Proceedings of the Institution of Mechanical Engineers, Part B-Journal of Engineering Manufacture229 (2015), No. 2, pp. 286–29410.1177/0954405414527962.Search in Google Scholar

30 ISO-4287: Geometrical product specifications (GPS), Surface texture: Profile method, terms, definitions and surface texture parameters, International Organization for Standardization, Geneva, Switzerland (1997)Search in Google Scholar

Published Online: 2019-11-18

Published in Print: 2019-12-02

You are currently not able to access this content.

Articles in the same Issue

https://doi.org/10.3139/120.111443