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Assessment of surface roughness in MQL assisted turning process of titanium alloy with application of TOPSIS-AHP method

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The optimization of surface roughness values considered as one of the most significant issues regarding turning process of titanium alloys with the use of minimum quantity lubrication (MQL) method. With such an aim in mind, the application of TOPSIS-AHP method is implemented in order to establish the most favourable cutting parameters for the following values of surface roughness: Ra, Rq and Rz in machining of titanium alloys regarding MQL conditions. The proposed methodology consists of the two stages. At the beginning, tests on turning process were performed on CNC lathe, taking feed rate, approach angle, and cutting speed as input parameters. Then, the TOPSIS-AHP method was applied on the given experimental data and the optimum machining parameters were determined. The findings from current investigations showed that, lower values of cutting speed, feed rate and middle value of approach angle shows the optimal results.
Rocznik
Strony
97--105
Opis fizyczny
Bibliogr. 18 poz., rys., tab.
Twórcy
autor
  • Faculty of Mechanical Engineering, Opole University of Technology, Opole, Poland
autor
  • MED, NIT, Hamirpur (H.P.), India
Bibliografia
  • [1] ROUTARA B., BANDYOPADHYAY A., SAHOO P., 2009, Roughness modeling and optimization in CNC end milling using response surface method: effect of workpiece material variation, Int. J. Adv. Manuf. Tech., 40, 1166-1180.
  • [2] GRZESIK W., 2016, Influence of surface textures produced by finishing operations on their functional properties, Journal of Machine Engineering, 16/1, 15-23.
  • [3] DAVIM J., GAITONDE V., KARNIK S., 2008, Investigations into the effect of cutting conditions on surface roughness in turning of free machining steel by ANN models, J Mater Process Tech, 205, 16-23.
  • [4] JAROSZ K., LÖSCHNER P., The effect of changes in depth of cut on surface roughness in machining of AISI 316 stainless steel, Journal of Machine Engineering, 18/1, 72-79.
  • [5] BHARDWAJ B., KUMAR R., SINGH P., 2014, Surface roughness (Ra) prediction model for turning of AISI 1019 steel using response surface methodology and Box–Cox transformation, P. I. Mech. Eng., J. Eng. Manuf. 228, 223-232.
  • [6] SHARMA V., DOGRA M., SURI N., 2009, Cooling techniques for improved productivity in turning, Int. J. Mach. Tool Manu., 49/6, 435-453.
  • [7] GUPTA M., SOOD P., SHARMA V., 2016, Investigations on surface roughness measurement in minimum quantity lubrication turning of titanium alloys using response surface methodology and Box–Cox transformation, J. Manuf. Sci. Prod., 16, 75-88.
  • [8] SINGH G., SHARMA V., 2016, Analyzing machining parameters for commercially pure titanium (Grade 2), cooled using minimum quantity lubrication assisted by a Ranque-Hilsch vortex tube, Int. J. Adv. Manuf. Technol, 88/9-12, 2921-2928.
  • [9] GUPTA M., SOOD P., SHARMA V., 2016, Machining parameters optimization of titanium alloy using response surface methodology and particle swarm optimization under minimum quantity lubrication environment, Mater. Manuf. Process., 31, 1671-1682.
  • [10] SHARMA V., SINGH G., SORBY K., 2015, A review on minimum quantity lubrication for machining processes, Mater. Manuf. Process., 30/8, 935-953.
  • [11] AYED Y., GERMAIN G., AMMAR A., FURET B., 2015, Tool wear analysis and improvement of cutting conditions using the high-pressure water-jet assistance when machining the Ti17 titanium alloy, Precision Eng. 42, 294-301.
  • [12] MIA M., DHAR N., 2015., Effect of high pressure coolant jet on cutting temperature, tool wear and surface finish in turning hardened (HRC 48) steel, J. Mech. Eng., 45/1, 1-6.
  • [13] ERTUGRUL I., KARAKA S., 2009, Performance Evaluation Of Turkish Ce-ment Firms With Fuzzy Analytic Hierarchy Process And TOPSIS Methods, Expert Syst. Appl., 36/1, 702-715.
  • [14] WANG Z., RAHMAN M., WONG Y., NEO K., SUN J., TAN C., ONOZUKA H., 2008, Study on orthogonal turning of titanium alloys with different coolant supply strategies, Int. J. Adv. Manuf. Tech., 42/7-8, 621-632.
  • [15] LAN T., 2009, Taguchi optimization of multi-objective CNC machining using TOPSIS, Inform. Tech. J., 8/6, 917-922.
  • [16] YUVARAJ N., KUMAR P., 2015, Multiresponse Optimization of Abrasive Water Jet Cutting Process Parameters Using TOPSIS Approach, Mater. Manuf. Process., 30/7, 882-889.
  • [17] SINGH A., DATTA S., MAHAPATRA S,. 2011, Application of TOPSIS in the Taguchi Method for Optimal Machining Parameter Selection, J. Manuf. Sci. Prod., 11, 49-60.
  • [18] RAO R., 2006, Machinability evaluation of work materials using a combined multiple attribute decision-making method, Int. J. Adv. Manuf. Tech., 28/3-4, 221-227.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-f404e8d0-93e9-409d-9973-e12c010d716a
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