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Investigation and Prediction of Material Removal Rate and Surface Roughness in CNC Turning of EN24 Alloy Steel

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Every manufacturing or production unit should concern about the quality of the product. Apart from quality, there exists other criterion, called productivity which is directly proportional to the profit level. Every manufacturing industry aims at producing a large number of products in relatively lesser time. In any machining process, it is most important to determine the optimal settings of machining parameters aiming at reduction of production costs and achieving the desired product quality. If the problem is related to a single quality attribute then it is called single objective optimization. If more than one attribute comes into consideration it is very difficult to select the optimal setting which can achieve all quality requirements simultaneously. In this work, EN-24 alloy steel work pieces were turned on Computer Numerical Controlled (CNC) lathe by using Cemented carbide tool (coated). The influence of four cutting parameters, cutting speed, feed rate, depth of cut, and tool nose radius on minuscule surface roughness and material removal rate (MRR) were analyzed on the basis of Response Surface Methodology approach. The experimental results were collected by following the Taguchi’s L16 mixed Orthogonal Array design.
Rocznik
Strony
451--466
Opis fizyczny
Bibliogr. 26 poz.
Twórcy
autor
  • Department of Mechanical Engineering K. Ramakrishnan College of Technology Tiruchirapalli, Tamil Nadu, India
autor
  • Department of Mechanical Engineering K. Ramakrishnan College of Technology Tiruchirapalli, Tamil Nadu, India
autor
  • Department of Mechanical Engineering K. Ramakrishnan College of Technology Tiruchirapalli, Tamil Nadu, India
autor
  • Department of Mechanical Engineering Shivani College of Engineering and Technology Samayapuram, Trichy, Tamilnadu, India
Bibliografia
  • [1] Datta, S., Nandi, G., Bandyopadhyay, A. and Pal, P. K.: Application of PCA based hybrid Taguchi method for multi–criteria optimization of submerged arc weld: A case study, International Journal of Advanced Manufacturing Technology, 2009.
  • [2] Fnides, B., Aouici, H. and Yallese, M. A.: Cutting forces and surface roughness in hard turning of hot work steel X38CrMoV5-1 using mixed ceramic, Mechanika, 2, 70, 73–78 2008.
  • [3] Fu, P. and Hope, A. D.: A Hybrid Pattern Recognition Architecture for Cutting Tool Condition Monitoring, Technology and Applications, 24, 4, 548–558, 2008.
  • [4] Lan, T.-S., Lo, C. Y., Wang, M.-Y. and Yen, A.-Y.: Multi Quality Prediction Model of CNC Turning Using Back Propagation Network, Information Technology Journal, 7, 6, 911–917, 2008.
  • [5] Reddy, B. S., Padmanabhan, G. and Reddy, K. V. K.: Surface Roughness Prediction Techniques for CNC turning, Asian Journal of Scienti c Research, 1, 3, 256–264, 2008.
  • [6] Datta, S., Bandyopadhyay, A. and Pal, P. K.: Application of Taguchi Philosophy for Parametric Optimization of Bead Geometry and HAZ Width in Submerged Arc Welding Using Mixture of Fresh Flux and Fused Slag, International Journal of Advanced Manufacturing Technology, 36, 689–698, 2008.
  • [7] Biswas, C. K., Chawla, B. S., Das, N. S., Srinivas, E. R.: Tool Wear Prediction using Neuro-Fuzzy System, Institution of Engineers (India) Journal (PR), 89, 42–46 2008.
  • [8] Kassab, S. Y. and Khoshnaw, Y. K.: The Effect of Cutting Tool Vibration on Surface Roughness of Work piece in Dry Turning Operation, Engineering and Technology, 25, 7, 879–889, 2007.
  • [9] Ozel, T., Karpat, Y., Figueira, L. and Davim, J. P.: Modeling of surface finish and tool flank wear in turning of AISI D2 steel with ceramic wiper inserts, Journal of Materials Processing Technology, 89, 192–198, 2007.
  • [10] Doniavi, A., Eskanderzade, M. and Tahmsebian, M.: Empirical Modeling of Surface Roughness in Turning Process of 1060 steel using Factorial Design Methodology, Journal of Applied Sciences, 7, 17, 2509–2513, 2007.
  • [11] Natarajan, U., Arun, P. and Periasamy, V. M.: On–line Tool Wear Monitoring in Turning by Hidden Markov Model (HMM), Institution of Engineers (India) Journal (PR), 87, 31–35, 2007.
  • [12] Al-Ahmari, A. M. A.: Predictive machinability models for a selected hard material in turning operations, Journal of Materials Processing Technology, 190, 305–311, 2007.
  • [13] Kumanan, S., Saheb, S. K. N. and Jesuthanam, C. P.: Prediction of Machining Forces using Neural Networks Trained by a Genetic Algorithm’, Institution of Engineers (India) Journal, 87, 11–15, 2006.
  • [14] Mahmoud, E. A. E. and Abdelkarim, H. A.: Optimum Cutting Parameters in Turning Operations using HSS Cutting Tool with 450 Approach Angle, Sudan Engineering Society Journal, 53, 48, 25–30, 2006.
  • [15] Ahmed, S. G.: Development of a Prediction Model for Surface Roughness in Finish Turning of Aluminium, Sudan Engineering Society Journal, 52, 45, 1–5, 2006.
  • [16] Abburi, N. R. and Dixit, U. S.: A knowledge–based system for the prediction of surface roughness in turning process, Robotics and Computer Integrated Manufacturing, 22, 363–372, 2006.
  • [17] Kohli, A. and Dixit, U. S.: A neural network–based methodology for the prediction of surface roughness in a turning process’, International Journal of Advanced Manufacturing Technology, 25, 118–129, 2005.
  • [18] Ozel, T. and Karpat, Y.:Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks, International Journal of Machine Tools and Manufacture, 45, 467–479, 2005.
  • [19] Pal, S. K. and Chakraborty, D.: Surface roughness prediction in turning using artificial neural network, Neural Computing and Application, 14, 319–324, 2005.
  • [20] Kirby, E. D., Zhang, Z. and Chen, J. C.: Development of an accelerometer based surface roughness Prediction System in Turning Operation Using Multiple Regression Techniques, Journal of Industrial Technology, 20, 4, 1–8, 2004.
  • [21] Choudhury, S. K. and Bartarya, G.: Role of temperature and surface finish in predicting tool wear using neural network and design of experiments, International Journal of Machine Tools and Manufacture, 43, 747–753, 2003.
  • [22] Chien, W.-T. and Tsai, C.-S.: The investigation on the prediction of tool wear and the determination of optimum cutting conditions in machining 17-4PH stainless steel’, Journal of Materials Processing Technology, 140, 340–345, 2003.
  • [23] Lee, S. S. and Chen, J. C.: Online surface roughness recognition system using artificial neural networks system in turning operations’ International Journal of Advanced Manufacturing Technology, 22, 498–509, 2003.
  • [24] Feng, C. X. and Wang, X.: Development of Empirical Models for Surface Roughness Prediction in Finish Turning, International Journal of Advanced Manufacturing Technology, 20, 348–356, 2002.
  • [25] Lin, W. S., Lee, B. Y., Wu, C. L.: Modeling the surface roughness and cutting force for turning, Journal of Materials Processing Technology, 108, 286293, 2001.
  • [26] Antony, J.: Multi-response optimization in industrial experiments using Taguchi’s quality loss function and Principal Component Analysis, Quality and Reliability Engineering International, 16, 3–8, 2000.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3b23a367-c485-4f01-8c4b-0a5254e3e156
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