PREDICTION OF SURFACE HARDNESS IN A BURNISHING PROCESS USING TAGUCHI METHOD, FUZZY LOGIC MODEL AND REGRESSION ANALYSIS

The available work is aimed for comparison and estimation of surface hardness in ball burnishing process of aluminum alloy based upon the Taguchi technique, Fuzzy logic and regression models. The ball burnishing parameters like burnishing speed, force, feed rate and number of passes were designed using Taguchi L25 orthogonal design matrix. Taguchi’s signal to noise ratio was used to optimize the surface hardness. The effect of burnishing parameters on surface hardness was established by analysis of variance. Fuzzy logic was conducted using Matlab Toolbox. Taguchi technique, second order regression model and variance analysis were developed using MINITAB 17. The predicted hardness values of performance parameters were operated to compare the distinct models. The results of predicted models indicated that the consistent predictive model is the fuzzy logic model. With high correlation coefficient (R2= 97.52 %), the model was regarded adequately accurate.

___

  • [1] El-Tayeb, N. S. M., Low, K. O., Brevern, P. V., (2007) Influence of roller burnishing contact width and burnishing orientation on surface quality and tribological behaviour of Aluminium 6061, Journal of materials processing technology,186(1-3), 272-278.
  • [2] Esme, U., (2010) Use of Grey based Taguchi method in ball burnishing process for the optimization of surface roughness and microhardness of AA 7075 aluminum alloy, Materiali in Tehnologije, 44(3), 129-135.
  • [3] Bounouara, A., Hamadache, H., Amirat, A., (2018) Investigation on the effect of ball burnishing on fracture toughness in spiral API X70 pipeline steel, The International Journal of Advanced Manufacturing Technology, 94(9-12), 4543-4551.
  • [4] Basak, H., Goktas, H. H., (2009) Burnishing process on al-alloy and optimization of surface roughness and surface hardness by fuzzy logic, Materials & Design, 30(4), 1275-1281.
  • [5] Esme, U., Sagbas, A., Kahraman, F., Kulekci, M.K., (2008) Use of artificial neural networks in ball burnishing process for the prediction of surface roughness of AA 7075 aluminum alloy, Materiali in tehnologije, 42(5), 215-219.
  • [6] Kahraman, F., (2015) Application of the response surface methodology in the ball burnishing process for the prediction and analysis of surface hardness of the aluminum alloy AA 7075, Materials Testing, 57(4), 311-315.
  • [7] Hassan, A. M., Al-Dhifi, S. Z., (1999) Improvement in the wear resistance of brass components by the ball burnishing process, Journal of Materials Processing Technology, 96(1-3), 73-80.
  • [8] Klocke, F., Bäcker, V., Wegner, H., Zimmermann, M., (2011) Finite element analysis of the roller burnishing process for fatigue resistance increase of engine components, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 225(1), 2-11.
  • [9] Pałka, K., Weroński, A., Zalewski, K., (2006) Mechanical properties and corrosion resistance of burnished X5CrNi18-9 stainless steel, Journal of Achievements in Materials and Manufacturing Engineering, 16(1-2), 57-62.
  • [10] Yen, Y. C., Sartkulvanich, P., Altan, T., (2005) Finite element modeling of roller burnishing process, CIRP Annals-Manufacturing Technology, 54(1), 237-240.
  • [11] Sagbas, A., (2011) Analysis and optimization of surface roughness in the ball burnishing process using response surface methodology and desirabilty function, Advances in Engineering Software, 42(11), 992-998.
  • [12] El-Taweel, T. A., El-Axir, M. H., (2009) Analysis and optimization of the ball burnishing process through the Taguchi technique, The International Journal of Advanced Manufacturing Technology, 41(3-4), 301-310.
  • [13] Kurkute, V., Chavan, S. T., (2018) Modeling and Optimization of surface roughness and microhardness for roller burnishing process using response surface methodology for Aluminum 63400 alloy, Procedia Manufacturing, 20, 542-547.
  • [14] Kumar, P. S., Babu, B. S., Sugumaran, V., (2018) Comparative Modeling on Surface Roughness for Roller Burnishing Process, using Fuzzy Logic, International Journal of Mechanical and Production Engineering Research and Development, 8(1), 43-64.
  • [15] Sarhan, A. A., El-Tayeb, N. S. M., (2014) Investigating the surface quality of the burnished brass C3605—fuzzy rule-based approach, The International Journal of Advanced Manufacturing Technology, 71(5-8), 1143-1150.
  • [16] Esme, U., Kulekci, M. K., Ustun, D., Kahraman, F., Kazancoglu, Y., (2015) Grey-based fuzzy algorithm for the optimization of the ball burnishing process", Materials Testing, 57(7-8), 666-673.
  • [17] Sagbas, A., Kahraman, F., (2009) Determination of optimal ball burnishing parameters for surface hardness", Materiali in tehnologije, 43(5), 271-274.
  • [18] Dweiri, F., Hassan, A. M., Hader, A., Al-Wedyan, H., (2003) Surface finish optimization of roller burnished nonferrous components by fuzzy modeling, Materials and Manufacturing Processes, 18(6), 863-876.
  • [19] Çiçek, A., Kıvak, T., Ekici, E., (2015) Optimization of drilling parameters using Taguchi technique and response surface methodology (RSM) in drilling of AISI 304 steel with cryogenically treated HSS drills, Journal of Intelligent Manufacturing, 26(2), 295-305.
  • [20] Pinar, A. M., Gullu, A., (2010) Optimization of numerical controlled hydraulic driven positioning system via Taguchi method, Journal of the Faculty of Engineering and Architecture of Gazi University, 25(1), 93-100.
  • [21] Taguchi, G., Elsayed, E. A., Hsiang, T. C., (1989), Quality engineering in production systems, McGraw-Hill, New York, NY.
  • [22] Buldum, B. B., Cagan, S. C., (2018) Study of Ball Burnishing Process on the Surface Roughness and Microhardness of AZ91D Alloy, Experimental Techniques, 42(2), 233-241.
  • [23] Asiltürk, I., Akkuş, H., (2011) Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method, Measurement, 44(9), 1697-1704.
  • [24] Nalbant, M., Gökkaya, H., Sur, G., (2007) Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning", Materials & design, 28(4), 1379-1385.
  • [25] Kuram, E., Ozcelik, B., (2013) Fuzzy logic and regression modelling of cutting parameters in drilling using vegetable based cutting fluids, Indian Journal of Engineering & Materials Sciences, 20, 51–58.
  • [26] Hanafi, I., Khamlichi, A., Cabrera, F. M., López, P. J. N., Jabbouri, A., (2012) Fuzzy rule based predictive model for cutting force in turning of reinforced PEEK composite, Measurement, 45(6), 1424-1435.
  • [27] Cetin, M. H., Ozcelik, B., Kuram, E., Demirbas, E., (2011) Evaluation of vegetable based cutting fluids with extreme pressure and cutting parameters in turning of AISI 304L by Taguchi method, Journal of Cleaner Production,19(17-18), 2049-2056.
  • [28] Bilgili, M., Sahin, B., (2010) Comparative analysis of regression and artificial neural network models for wind speed prediction, Meteorology and atmospheric physics,109(1-2), 61-72.