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A review on multiple responses process parameters optimization of turning Al-TiCp metal matrix composites

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: In this paper the Metal Matrix Composites of AL 7068 and TiC with different proportions of (2%, 4%, 6%, 8%, and 10%) by using stir casting process, mechanical properties and machining responses are investigating. Design/methodology/approach: The samples are fabricated using stir casting, machining of turning the samples by using Kilster Tool Lathe Dynamometer machining responses were identifying. Findings: In this research to find mechanical characterizations like flexural strength, Wear ratio, impact strength, hardness, microstructure and also machining responses like surface roughness, cutting force, cutting power, tool wear ratio and can be optimized by multiple responses. Research limitations/implications: This paper research about the mechanical characterization and machining of AL-7068&TiC that focuses on machining responses and microstructures. Originality/value: By surveying the research articles the gap will be identified in AA-7068&TiC depended on machining responses and mechanical characterizations.
Rocznik
Strony
32--40
Opis fizyczny
Bibliogr. 40 poz., rys., tab.
Twórcy
autor
  • RGM College of Engineering & Technology, Nandyal, India
  • RGM College of Engineering & Technology, Nandyal, India
autor
  • JNTU College of Engineering, Anantapuramu, India
Bibliografia
  • [1] J.S.S. Babu, C.G. Kang, H.H. Kim, Dry sliding wear behavior of aluminum based hybrid composites with graphite nano fiber-alumina fiber, Materials and Design 32 (2011) 3920-3925.
  • [2] A. Kumar, S. Lal, S. Kumar, Fabrication and characterization of A359/Al2O3 metal matrix composite using electromagnetic stir casting method, Journal of Materials Research and Technology 2/3 (2013) 250-254.
  • [3] B. Kumar et al., Wear Analysis of Aluminium Based Composites by Stir Casting Process: A Literature Review, International Journal of Innovative Research in Science, Engineering and Technology 4/8 (2015) 7253-7259.
  • [4] S. Gopalakrishnan, N. Murugan, Production and wear characterization of AA 6061 matrix titanium carbide particulate reinforced composite by enhanced stir casting method, Composites: Part B 43 (2012) 302-308.
  • [5] S.A. Sajjadi, H.R. Ezatpour, M.T. Parizi, Comparison of microstructure and mechanical properties of A356 aluminum alloy/Al2O3 composites fabricated by stir and compo-casting processes, Materials and Design 34 (2012) 106-111.
  • [6] J. Hashim, L. Looney, M.S.J. Hashmi, Metal matrix composites: production by the stir casting method, Journal of Materials Processing Technology 92/93 (1999) 1-7.
  • [7] Y.R. Patel, J.D. Suthar, A Review Paper On Optimization Of Turning Parameters For Surface Roughness And Material Removal Rate For SS 310, Indian Journal of Applied Research 5/1 (2015) 40-42.
  • [8] M. Pulla Reddy, et al., Experimental Investigation on Al 7075, TiB2, TiC, Metal Matrix Composites to find Power Consumption and MRR while Machining, International Journal of Innovative Research in Science Engineering and Technology 2/2 (2016) 1329-1337.
  • [9] R. Singh, G. Singh, Investigations of Al-SiC AMC prepared by vacuum moulding assisted stir casting, Journal of Manufacturing Processes 19 (2015) 142-147.
  • [10] V.C. Uvaraja, N. Natarajan, Comparision on Al6061 and Al7075 alloy with sic and B4C reinforcement hybrid metal matrix composites, International Journal of Advancements in Research and Technology 2/2 (2012) 1-12.
  • [11] M.H. Jokhio, M.I. Panhwar, M.A. Unar, Manufacturing of Aluminum Composite Material Using Stir Casting Process, Mehran University, Research Journal of Engineering & Technology 30/1 (2011) 53-64.
  • [12] M. Hajizamani, H. Baharvandi, Fabrication and Studying the Mechanical Properties of A356 Alloy Reinforced with Al2O3 - 10% vol. ZrO2 Nanoparticles through Stir Casting, Advances in Materials Physics and Chemistry 1/2 (2011) 26-30.
  • [13] N.E. Elzayady, R.M. Rashad, A. Elhabak, Mechanical Behavior of A356/Albite Composite Material, Journal of American Science 8/6 (2012) 53-64.
  • [14] G. Rajaram, S. Kumaran, T.S. Rao, Fabrication of Al-Si/graphite composites and their structure-property correlation, Journal of Composite Materials 45/26 (2011) 1-8.
  • [15] M.K. Surappa, Microstructure evolution during solidification of DRMMCs (Discontinuously reinforced metal matrix composites): State of art, Journal of Materials Processing Technology 63/1-3 (1997) 325-333.
  • [16] V. Suresh et al., Study and Investigation of Analysis of Metal Matrix Composite, International Journal of Mechanical Engineering and Technology 3/2 (2012) 171-188.
  • [17] R.A. Lindberg, Processes and Materials of Manufacture, Prentice Hall of India Pvt., 2008, 521-523.
  • [18] E.P. De Garmo, J.T. Black, R.A. Kohser, Materials and Processes in Manufacturing, Prentice Hall of India Pvt., 2008.
  • [19] R.K. Everett, R.J. Arsenault, Metal Matrix Composites: Mechanisms and Properties, Academic resPs, San Diego, 1991.
  • [20] A. Luo, J. Renaud, I. Nakatsugawa, J. Plourde, Magnesium castings for automotive applications, JOM: Journal of the Minerals, Metals, and Materials Society 47/7 (1995) 28-31.
  • [21] D.K. Chernov, Reports of The Imperial Russian Metallurgical Society, 1878.
  • [22] J. Hollinggrak, Casting Metals, UK Patent 4371, 1819.
  • [23] C.T. Lin, I.F. Chung, S.Y. Huang, Improvement of machining accuracy by fuzzy logic at corner parts of wire-EDM, Fuzzy Sets Systems 122 (2001) 499-511.
  • [24] G.C. Onwubolu, T. Kumalo, Optimization of mutipass turning operation with genetic algorithms. International Journal of Production Research 39/16 (2001) 3727-3745.
  • [25] Y. Huang, S.Y. Liang, Effect of Cutting Conditions on Tool Performance in CBN Hard Turning, Journal of Manufacturing Process 32 (2004) 511-518.
  • [26] S. Sarkar S. Mitra, B. Bhattacharyya, Parametric analysis and optimization of wire electrical discharge machining of -titanium aluminide alloy, Journal of Materials Processing Technology 159 (2005) 286-294.
  • [27] J. Antony et al., Multiple response optimization using Taguchi methodology and neuro-fuzzy based model, Journal of Manufacturing Technology Management 17/7 (2006) 908-925.
  • [28] B. Sidda Reddy et al., Prediction of surface roughness in turning using adaptive Neuro-Fuzzy inference system, Jordan Journal of Mechanical and Industrial Engineering 3/4 (2009) 252-259.
  • [29] C. Raidu et al., Fuzzy based cutting data selection for hard turning operation, in: Proceeding of the Frontiers in Automobile and Mechanical Engineering, Chennai, India, 2010, 229-234.
  • [30] R.P. Patel et al, Effect of machining parameters on surface roughness a Power consumption for 6063 Al TiC Composites, International Journal of Engineering Research and Applications 2/4 (2012) 295-300.
  • [31] U. Caydas, S. Ekick et al., Support Vector machines models for surface roughness prediction in CNC turning of AISI 304 austenite stainless steel, Journal of Intelligent Manufacturing 23 (2012) 639-650.
  • [32] R.A. Mahdavinejad, S. Saeedy, Investigation of the influential parameters of machining of AISI 304 stainless steel, Sadhana 36/6 (2011) 963-970.
  • [33] R.M. Homami et al., Optimization of Turning process Using artificial intelligence technology, International Journal of Advanced Manufacturing Technology 70/5-8 (2014) 1205-1217.
  • [34] M. Subramanian et al., Optimization of Cutting Parameters for Cutting Force in Shoulder Milling of Al7075-T6 Using Response Surface Methodology and Genetic Algorithm, International Conference on Design and Manufacturing, Procedia Engineering 64 (2013) 690-700.
  • [35] C.D. Kumar et al., Study of effect of tool nose radius on surface finish and optimization of machining parameters of turning process of an aerospace material, American International Journal of Research in Science, Technology, Engineering & Mathematics 7/3 (2014) 229-233.
  • [36] K. Krishna et al., Prediction of Material Removal Rate using Artifical Neural Nerwork in CNC Turning Process on Aluminum, International Journal of Engineering Technology, Management and Applied Science 3/2 (2015) 12-26.
  • [37] D. Biswajit et al., Development of an in-situ synthesized multi-component reinforced Al-45%CuTiC metal matrix composites by FAS techniqueOptimization of process parameters, Journal of Engineering and Technology, an International Journal 19/1 (2015) 279-291.
  • [38] G. Kant, Predictive Modelling and Optimization of Machining Parameters to Minimize Surface Roughness using Artificial Neural Network Coupled with Genetic Algorithm, Science Direct 31 (2015) 453-458.
  • [39] B. Arezzo et al., Selection of Cutting Tools and Conditions of machining Operations Using an Expert system, Computers Industry 42/1 (2000) 43-58.
  • [40] R. Arularasan et al., Optimization of Process Parameters in Titanium Alloy by Using Genetic Algoritm, International Journal of Research in Aeronautical and Mechanical Engineering 3/4 (2015) 61-66.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-88079641-e4fe-4168-a40f-275e82f3f004
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