Selection of abrasive wheels by surface topography of parts from hardened steel 30ChGSA
Abstract
In this research the surface roughness parameters: Ra, Rmax, Sm and – the flatness deviations EFEmax of plane parts made of hardened steel 30ChGSA, are used to estimate the cutting ability (CA) of grinding wheels made from traditional abrasives with different porosities. These parameters need to evaluate not only the position measures, but also the precision. Statistical methods allow predicting them individually, but do not have the ability to provide a comprehensive assessment. For these purposes, the fuzzy logic was attracted as an innovative direction of mechanical engineering. To realize the process of modeling a special bump pack of Fuzzy Logic Toolbox in MATLAB was used. As the results of fuzzy logic modeling in MATLAB, it was established that the wheel
5NQ46I6VS3 (i=5) (di=0.878) had the best comprehensive assessment by CA and the wheel 34AF60K6V5A (i=14) (di= 0.383) - the lowest rate. Therefore, the wheel 5NQ46I6VS3 can be selected as a basic wheel in the robust design of grinding operations. In manufacture, if there is
not a grinding wheel 5NQ46I6VS3, the tools, which also had integral assessment of cutting ability “very high”: 25AF46M10V-PO (i=7) or 5A46L10VAX (i=15) can replace it.
References
Ahmed A.D. Sarhan, M. Sayuti & M. Hamdi. 2012. A fuzzy logic based model to predict surface roughness of a machined surface in glass milling operation using CBN grinding tool. World academy of science, engineering and technology 6. 564-570.
Alexander W. Gray, Anthony S. Daniels & David J. Singer. 2010. Impacts of fuzzy logic modeling for constraints optimization. Naval engineers journal 2. 121-132.
Ali, Y.M. & Zhang, L.C. 1999. Surface roughness prediction of ground components using a fuzzy logic approach. Journal of Materials Processing Technology 211. 561–568.
Ali, Y.M. & Zhang, L.C. 2004. A fuzzy model for predicting burns in surface grinding of steel. Int J Mach Tool Manu 44. 563-571.
Faran Baig et al. 2013. Design and simulation of fuzzy logic based ELID grinding control system. International journal of advanced technology & engineering research (IJATER) 3. 79-88.
GOST 24631-81. 1981. Form Tolerances and surface positions. Numeric values. Instead of GOST 10356-63 (in terms of Sec. III). Introduced. 01.07.1981. Publishing House of Standards Moscow. Pp. 14.
GOST 24631-81. 1984. Form tolerances and surface positions. Basic concepts and symbols. Instead of GOST 10356-63. Introduced. 01.07.1981. Publishing House of Standards, Moscow. Pp. 68.
GOST 25142-82. 1982. The surface roughness. Terms and Definitions. Introduced. 01.01.1983. Publishing house standards, Moscow. Pp. 20.
GOST 2789-73. 1973. The surface roughness. Parameters, characteristics and symbols. Instead. GOST 2789-59. Introduced. 01.01.1975. Publishing house standards, Moscow. Pp. 10.
GOST R 52781-2007. 2007 Grinding and sharpening wheels. Specifications. Introduced. 29.11.2007. Standartinform, Moscow. Pp. 32.
Harrington, E.C. 1965. The desirability function. Industrial Quality Control 21. 494-498.
Hollander, M. & Wolfe, D.A. 1999. Nonparametric statistical methods, Second Edition. Wiley-Interscience. Pp. 787.
Jaya, A.S.M., Hashim, S.Z.M., and Rahman, M.N.A. 2010. Fuzzy logic-based for predicting roughness performance of TiAlN coating. Intelligent Systems Design and Applications (ISDA). 10th International Conference. 91-96.
Jiao, Y., Lei, S., Pei, Z.J. & Lee, E.S. 2004. Fuzzy adaptive networks in machining process modeling surface roughness prediction for turning operations. International Journal of Machine Tools and Manufacture 44. 1643–1651.
Latha, B. & Senthilkumar, V.S. 2010. Modeling and analysis of surface roughness parameters in drilling GFRP composites using fuzzy logic. Materials and manufacturing processes 25. 817-827.
Leonenkov, A.V. 2005. Fuzzy modeling in MATLAB and FuzzyTech. SPb BHV-Petersburg. Pp. 736.
Lin, Y.H., Lai, H.H. & Yeh, C.H. 2007. Consumer-oriented product form design based on fuzzy logic: a case study of mobile phones. International Journal of Industrial Ergonomics 37. 531–543.
Maity, S.R. & Chakraborty, S. 2013. Grinding wheel abrasive material selection using fuzzy TOPSIS method. Materials and manufacturing processes 28. 408-417.
Norton. 2009. The perfection of abrasive technologies.
Palanikumar, K. 2006. Cutting parameters optimization for surface roughness in machining of GFRP composites using Taguchi method. Journal of Reinforced Plastics and Composites 25. 1739–1751.
Samhouri, S. & Surgenor, W. 2005. Surface roughness in grinding: on-line prediction with adaptive neuro-fuzzy inference system. Transactions of NAMRI/SME 33. 57-64.
Sardar Sathpal Singh, Rishi Sayal & Venkat Rao. 2011. Analysis and usage of fuzzy logic for optimized evaluation of database queries. International journal of computer applications 16. 19-26.
Soler, Ya.I. & Nguyen, V.K. 2014. Predicting grinding efficiency of different porosity wheels from traditional and new abrasives by the criterion of Р9M4K8 plate shape accuracy. Bulletin ISTU 11 (94). 49-58.
Soler, Ya.I. & Nguyen, V.L. 2015. Selection of synthesis corundum Grain in Grinding flat parts from hardened steel 30ChGSA the macrogeometry criterion. Applied Mechanics and Material 788. 95-101.
Yang, L.D., Chen, J.C., Chow, H.M. & Lin, C.T. 2006. Fuzzy-nets based in-process surface roughness adaptive control system in end-milling operations. International Journal of Advanced Manufacturing Technology 28. 236–248.
Yilmaz, O., Eyercioglu, O. & Nabil N.Z. Gindy. 2006. A user-friendly fuzzy-based system for the selection of electro discharge machining process parameters. Journal of Materials Processing Technology 172. 363–371.
Zadeh, L. 1965. Fuzzy sets. Information and Control 8. 338–353.
