Email this article 
Experimental Investigation of Surface Roughness and Tool Life in Hard Turning of AISI M2 Steel Using CBN Insert
(*) Corresponding author
DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)
Abstract
This research work is focused on hard turning of AISI M2Steel using Cubic Boron Nitrate (CBN) cutting inserts. The experiments were planned using Response surface method (RSM) called Central Composite Design (CCD) procedure. The experiments were performed on a KIROLOSKAR model centre lathe using three input cutting parameters ,cutting speed, feed and depth of cut at lower levels(-1) and higher levels(+1). The output responses measured were surface roughness (Ra) and tool life (TL) .Mathematical model for these responses were developed using response surface methodology (RSM). An optimal process condition was identified such that to attain a good quality of hard turned surface. Models were developed using two methodologies, ie. response surface methodology (RSM) and fuzzy Logic (FL). In RSM, regression equations are obtained, which relates the hard turning parameters with each response. The adequacy of the model was checked using R-squared values. The response values predicted by developed model were compared against the experimental values and found that they are in good agreement with each other. In fuzzy logic, with the range of input machining parameters and if then rules, output response parameters are obtained and verified against the experimental values and found that they are also in good agreement with the each other. Triangular type membership functions of fuzzy logic were used in this study. Then both the methodologies were compared with the experimental values and best method was identified for modeling to achieve a better result. Then the interaction effects of various hard turning parameters on each response were also studied using analysis of variance (ANOVA) and interaction plots. Genetic algorithm(GA) was used to identify the optimal hard turning parameters in such a way to produce a good quality machined surface in hard turning AISI M2Steel using CBN(non coated) cutting tools
Copyright © 2014 Praise Worthy Prize - All rights reserved.
Keywords
References
M.Y Noordin, V.C Venkatesh, S Sharif, S Elting, A Abdullah, “Application of response surface methodology in describing the performance of coated carbide tools when turning AISI 1045 steel”, Journal of Materials Processing Technology, Volume :145, Issue 1, 1 January 2004, Pages 46-58.
Sahin Yusuf, Motorcu A.Riza, Surface Roughness Prediction Model in Machining of Carbon Steel by PVD Coated Cutting Tools, American Journal of Applied Sciences, 1 (2004): Pages 12-17.
Saparudin et al. “Focused on the analysis of optimum cutting conditions to get lowest surface roughness in turning SCM 440 alloy steel by Taguchi method.’ Sudan Engineering Society Journal, Volume :53,2004 Number 48, Pages. 25-30.
Lima J G, Ávila R F, Abrão A M, Faustino M and Davim J P 2005 Hard turning: AISI 4340 high strength low steel and AISI D2 cold work tool steel. J. Mater. Process. Technol. Volume :169: Pages 388–395
DoniaviA,Eskandarzade.M.,Tahmasebian.M., Empirical modeling of surface roughness in turning process of 1060 steel using factorial design methodology, Journal Applied Sciences 7(2007): Pages 2509-2513.
Horng J T, Liu N M and Chiang K T 2008 Investigation the machinability evaluation of Hadfield steelin the hard turning with Al2O3/TiC mixed ceramics tool based on the response surface methodology. J. Mater. Process. Technol, Volume 208, Pages 532–541
Gusri A.I., Che Hassan C.H., Jaharah A.G., Yanuar B.1, Yasir A.1, Nagi A, Application Of Taguchi method in optimizing turning parameters of titanium alloy, Seminar on Engineering Mathematics, 2008 Engineering Mathematics Group
Aggarwal A., Singh H., Kumar P. & Singh M (2008), “Optimizing surface roughness and ,tool life, tool wear and power consumption for CNC turned parts using response surface methodology and Taguchi technique –A comparative analysis”, Journal of material processing technology, Volume :200, Pages 373-384.
Lin W.S., Lee B.Y.& Wu C.L(2008), “Modeling the surface roughness and cutting force for turning,” Journal of Materials Processing Technology, Volume 108, Pages.286-293.
Thamizhmanii S., Omar B. Bin, Saparudin S., Hasan S.,Surfaceroughnessanalyseson hard martensitic stainless steel by turning, Journal of Achievements in Materials and Manufacturing Engineering Volume 26(2008): Pages 139-142.
Jenn-Tsong Horng, Nun-Ming Liu, Ko-Ta Chiang, “ Investigating the mach inability evaluation of Hadfield steel in the hard turning with Al2O3/TiC mixed ceramic tool based on the response surface methodology” Journal of Materials Processing Technology, Volume 208, Issues 1-3, 21 November 2008, Pages 532-541.
Yallese M A, Chaoui K, Zeghib N, Boulanouar L and Rigal J F 2009 Hard machining of hardened bearing steel using cubic boron nitride tool.J. Mater. Process. Technol. Volume :209, Pages 1092–1104
Gopalsamy, Bala Murugan, Mondal Biswanath, Ghosh Sukarnal,Taguchi method and ANOVA: An approach for process parameters optimization of hard machining while machining hardened steel, Journal of Scientific & Industrial Research Volume:68(2009): Pages 686-695
Khaider Bouacha K, Yallese M A, Mabrouki T, Rigal J-F 2010, “ Statistical analysis of surface roughness and cuttingforces using response surface methodology in hard turning of AISI 52100 bearing steel with CBN tool”. Int. J. Refractory Metals and Hard Materials Volume 28: Pages 349–361.
Fnides B, Yallese M A, Mabrouki T, Rigal J-F 2009 Surface roughness model in turning hardened hot work steel using mixed ceramic tool.Mechanika. Kaunas: Technologija, Nr.3,volume77: Pages 68–73
H. K. Dave, L. S. Patel, H. K. Raval et al Investigation of the machining characteristics of different grades of EN materials in hard turning process using Tin coated cutting tools.Growing science-Volume3
R.AMahdavinejadandS.Saeedy2011; -“Investigation of the influential parameters of machining of AISI 304 stainless steel” Indian Academy of Science Volume: 36, Part 6, Pages 963–970.
DuongXuan-TruongandTranMinh-Duc1 “Investigate the surface roughness of harden Inconel 718 material using PVD coated cutting tool in hard turning process through RSS models” International Journal of Advanced Engineering Technology July-Sep,2013, Pages 108-112
N. R. Abburi and U. S. Dixit, “A knowledge based system for the prediction of surface roughness in turning process”, Robotics and Computer Integrated Manufacturing, Volume; 22, 2006, Pages 363-372
M. Chandrasekaran, M. Muralidhar, C. M. Krishna and U.S. Dixit, “Application of soft computing techniques in machining performance prediction and optimization:a literature review” International Journal of Adv Manuf.Technol, Volume :46, 2010, Pages 445–464
T.Rajasekaran, K.Palanikumar and B.K Vinayagam, “Application of fuzzy logic for modeling surface roughness in turning CFRP composites using CBN tool”, Prod. Eng. Res. Devel, Volume: 5, 2011, Pages 191-199
Harun Akkus and Ilhan Asilturk, “Predicting surface roughness of AISI 4140 steel in hard turning process through artificial neural network, fuzzy logic and regression models”, Scientific Research and Essays, Volume. 6 (13), 2011, Pages 2729-2736
Krishnaraj V., Vijayaraghavan S., Suresh G., An investigation on high speed drilling on glass fiber reinforced plastics, Journal of Engineering and Material Science, volume:12, Pages 189-195
Fairuz, M.A., Hafiezal, M.R.M., Hussin, R., Adam, S.A., Aiman, A.F., Performance Study of WEDMed on Inconel 718 by Using Response Surface Methodology, (2013) International Review of Mechanical Engineering (IREME), 7 (4), pp. 716-720.
Philip Selvaraj, D., Chandramohan, P., Mohanraj, M., Rajesh, P.K., Experimental investigations on surface roughness, cutting force and tool wear of duplex stainless steel in end milling using Taguchi method, (2013) International Review of Mechanical Engineering (IREME), 7 (6), pp. 1133-1141.
Refbacks
- There are currently no refbacks.
Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize
