Search for optimal grain size of nitride-boron wheels during flat grinding of parts made of 06Х14Н6Д2МВТ-Ш steel on surface microrelief under conditions of fuzzy logic simulation

Selection of grinding wheels is the most effective means of enhancing both the efficiency of grinding and the quality of the manufactured parts. The process stochastic nature determines the use of the statistical methods for interpreting the output parameters while considering them as random values. Both violation of dispersions and deviation from the normal distributions during grinding make it appropriate to use the non-parametrical statistical method, which implies that the medians are considered the position measurer and quartile latitudes - the dispersion measurer. Unfortunately, the statistical methods cannot provide a complex evaluation of the wheels cutting capacity using the both measurers simultaneously. An innovative approach to this analysis determines the usage of the fuzzy logic methods during the simulation process in the MATLAB environment with the help of the FuzzyLogicToolbox specialized additional package. The simulation showed that CBN30 B151 100 OVK27-KF40 wheels provide the best quality in terms of the surface roughness during grinding ofthe parts made of hardened corrosion-resistant 06X14H6Д2MBT-Ш steel.

References

[1] Suslov A.G., Bezyazichny V.F., Panfilov Y.V. Inzheneriya poverkhnosti detaley [Surface Engineering Details]. Mocow, Mashinostroyeniye Publ., 2008. 320 p.

[2] Tonshoff H.K. Modelling and simulation of grinding processes. Annals of the CIRP, 1992, vol. 41 (2), pp. 677-688.

[3] Badger J.A., Torrance A.A. A Computer Program to Predict Grinding Forces from Wheel Surface Profiles Using Slip-Line Fields. Proc. of the Conf. in Adv. Man. Tech., San Sebastian, 1998, pp. 6-8.

[4] Badger J.A., Torrance A.A. The Relation between the Traverse Dressing of Vitrified Grinding Wheels and Their Performance. Int. J. Mach. Tools & Manufacture, 2000, vol. 40, pp. 1787-1811.

[5] Hou Z.B., Komanduri R. On the Mechanics of the Grinding Process. Part I. Stochastic Nature of the Grinding Process. Int. J. Mach. Tools & Manufacture, 2003, vol. 43, pp. 1579-1593.

[6] Ali Y.M., Zhang L.C. Surface Roughness Prediction of Ground Components Using a Fuzzy Logic Approach. J. of Materials Processing Technology, 1999, pp. 561-568.

[7] Ali Y.M., Zhang L.C. A Fuzzy Model for Predicting Burns in Surface Grinding of Steel. Int. J. Mach. Tools & Manufacture, 2004, vol. 44. 563 p.

[8] Hollander M., Wolfe D.A. Nonparametric statistical methods. 2nd Edition. Wiley-Interscience, 1999. 787 p.

[9] Standard RF GOST 5725-2-2012. Tochnost’ (pravil’nost’ I pretsizionnost’) metodov i rezul’tatov izmereniy. Chast’ 2. Osnovnoy metod opredeleniya povtoryayemosti i vosproizvodimosti standartnogo metoda izmereniy [State Standard 5725-2-2012 Accuracy (trueness and precision) of measurement methods and results. Part 2. Basic method for the determination of repeatability and reproducibility of a standard measurement metho]. Moscow, Izd. Standartov Publ., 2002. 58 p.

[10] Zaks L. Russ. ed.: Statisticheskoye otsenivaniye [Statistical estimation]. Trans. from German. Moscow, Statistica Publ., 1976. 598 p.

[11] Wheeler Donald J., Chambers David S. Understanding Statistical Process Control. SPC Press, Knoxville, Tennessee, 1986.

[12] Soler Ya.I., Prokop’yeva A.V. Research into sparking-out influence on the Р9М4К8 plate microrelief while applying CBN (cubic boron nitride) grinding technology. Obrabotka metallov [Metal Working and Material Science], 2009, no. 1 (42), pp. 2427 (in Russ.).

[13] Soler Ya.I., Kazimirov D.Yu. Selecting Abrasive Wheels for the Plane Grinding of Airplane Parts of the Basis Surface Roughness. Russian engineering research, 2010, vol. 30, no. 3, pp. 251-261.

[14] Standard RF GOST R 53922-2010. Poroshki almaznyye I iz kubicheskogo nitrida bora (el’bora). Zernistost’ I zernovoy sostav shlifporoshkov. Kontrol’ zernovogo sostava [State Standard R 53922-2010. Powders of diamond and cubic boron nitride (CBN)]. Grain size and grain structure of grinding powders. Control of grain composition]. Moscow, Standartinform Publ., 2009. 15 p.

[15] Standard RF GOST R 52587-2006. Instrument abrazivnyy. Oboznacheniya i metody izmereniya tverdosti [State Standard R 52587-2006. Abrasive tool. Notation and methods of measurement of hardness]. Moscow, Standartinform Publ., 2007. 9 p.

[16] Vyatchenin D.A. Nechetkiye metody avtomaticheskoy klassifikatsii [Fuzzy Methods of Automatic Classification]. Minsk, UP Tekhnoprint Publ., 2004. 219 p.

[17] Kofman A. Vvedeniye v teoriyu nechetkikh mnozhestv [Introduction to the Theory of Fuzzy Sets]. Moscow, Radio i svyaz’ Publ., 1982. 432 p.

[18] Leonenkov A.V. Nechetkoye modelirovaniye v srede MATLAB i FuzzyTech [Fuzzy Modeling in MATLAB and FuzzyTech]. St. Petersburg, BHV-Petersburg, 2005. 736 p.

[19] Mandrov B.I., Baklanov S.D., Baklanov D.D. Application of the Desirability Function at Harrington Extrusion Welding Sheets of Polyethylene Grade HDPE. Polzunovsky Almanac, 2012, no. 1, pp. 62-64 (in Russ.).

[20] Jaya A.S.M., Hashim S.Z.M., Rahman M.N.A. Fuzzy Logic-Based for Predicting Roughness Performance of TiAlN Coating. In Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on, 2010, pp. 91-96.