Compliance Modelling and Optimization of Kerf during WEDM of Al7075/SiCP Metal Matrix Composite
Authors: Thella Babu Rao, A. Gopala Krishna
Abstract:
This investigation presents the formulation of kerf (width of slit) and optimal control parameter settings of wire electrochemical discharge machining which results minimum possible kerf while machining Al7075/SiCp MMCs. WEDM is proved its efficiency and effectiveness to cut the hard ceramic reinforced MMCs within the permissible budget. Among the distinct performance measures of WEDM process, kerf is an important performance characteristic which determines the dimensional accuracy of the machined component while producing high precision components. The lack of available of the machinability information such advanced MMCs result the more experimentation in the manufacturing industries. Therefore, extensive experimental investigations are essential to provide the database of effect of various control parameters on the kerf while machining such advanced MMCs in WEDM. Literature reviled the significance some of the electrical parameters which are prominent on kerf for machining distinct conventional materials. However, the significance of reinforced particulate size and volume fraction on kerf is highlighted in this work while machining MMCs along with the machining parameters of pulse-on time, pulse-off time and wire tension. Usually, the dimensional tolerances of machined components are decided at the design stage and a machinist pay attention to produce the required dimensional tolerances by setting appropriate machining control variables. However, it is highly difficult to determine the optimal machining settings for such advanced materials on the shop floor. Therefore, in the view of precision of cut, kerf (cutting width) is considered as the measure of performance for the model. It was found from the literature that, the machining conditions of higher fractions of large size SiCp resulting less kerf where as high values of pulse-on time result in a high kerf. A response surface model is used to predict the relative significance of various control variables on kerf. Consequently, a powerful artificial intelligence called genetic algorithms (GA) is used to determine the best combination of the control variable settings. In the next step the conformation test was conducted for the optimal parameter settings and found good agreement between the GA kerf and measured kerf. Hence, it is clearly reveal that the effectiveness and accuracy of the developed model and program to analyze the kerf and to determine its optimal process parameters. The results obtained in this work states that, the resulted optimized parameters are capable of machining the Al7075/SiCp MMCs more efficiently and with better dimensional accuracy.
Keywords: Al7075SiCP MMC, kerf, WEDM, optimization.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1336052
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2020References:
[1] M. Rosso, Ceramic and metal matrix composites: Routes and properties, Journal of Materials Processing Technology 175 (2006) 364–375.
[2] A. Manna Æ B. Bhattacharyya, Investigation for optimal parametric combination for achieving better surface finish during turning of Al /SiC-MMC, Int J Adv Manuf Technol (2004) 23: 658–665.
[3] Harlal Singh Mali, Alakesh Manna, Simulation of surface generated during abrasive flow finishing of Al/SiC p -MMC using neural networks, Int J Adv Manuf Technol (2012) 61:1263 – 1268.
[4] Ho KH, Newman ST, Rahimifard S, Allen RD (2004) State of the art in wire electrical discharge machining (WEDM). Int J Mach Tools Manuf 44:1247 – 1259.
[5] C. Satishkumar, M. Kanthababu, V. Vajjiravelu, R. Anburaj, N. Thirumalai Sundarrajan, H. Arul, Investigation of wire electrical discharge machining characteristics of Al6063/SiC p composites, Int J Adv Manuf Technol (2011) 56:975 – 986.
[6] Guo ZN, Wang X, Huang ZG, Yue TM (2002) Experimental investigation into shaping particles-reinforce material by WEDM HS. J Mater Process Technol 129:56 – 59.
[7] Yan BH, Tsai HC (2005) Examination of wire electrical discharge machining of Al2O3/6061Al composites. Int J Mach Tools Manuf 45(3):251 – 259.
[8] Aniza Alias, Bulan Abdullah, Norliana Mohd Abbas, Influence of machine feed rate in wedm of titanium Ti-6Al-4V with constant current (6a) using brass wire, Procedia Engineering, 41, ( 2012 ), 1806 – 1811.
[9] Kodalagara Puttanarasaiah Somashekhar & Nottath Ramachandran & Jose Mathew, Material removal characteristics of microslot (kerf) geometry in μ-WEDM on aluminium Int J Adv Manuf Technololgy, 2010, 51:611–626.
[10] Sangju Lee, Michael A. Scarpulla, Eberhard Bamberg, Effect of metal coating on machinability of high purity germanium using wire electrical discharge machining, Journal of Materials Processing Technology 213 (2013) 811–817.
[11] Nihat Tosun, Can Cogun and Gul Tosun, 2004, A study on kerf and material removal rate in wire electrical discharge machining based on Taguchi method, Journal of Materials Processing Technology – Journal of material processing and technology, vol. 152, no. 3, pp. 316-322, 2004.
[12] K. Palanikumar, R. Karthikeyan, Optimal machining conditions for turning of particulate metal matrix composites using Taguchi and response surface methodologies, Machining Science and Technology: An International Journal, 10:417–433.
[13] Montgomery, D. C., "Design and analysis of experiments”, 5th edition, John Wiley & Sons, INC, New York, 2003.
[14] Chen CJ, Tseng CS (1996) The path and location planning of workpiece by genetic algorithms. J Intell Manuf 7:69.
[15] Dereli T, Filiz IH, Baykasoglu A (2001) Optimizing cutting parameters in process planning of prismatic parts using genetic algorithms. Int J Prod Res 39:3303.
[16] P. Palanisamy, I. Rajendran, S. Shanmugasundaram, Optimization of machining parameters using genetic algorithm and experimental validation for end-milling operations, International Journal of Advanced Manufacturing Technology, 32: (2007) 644 – 655.
[17] Kalyanmoy Deb, Optimization for engineering design: Algorithms and Examples, PHI Learning Limited, New Delhi, 2010.
[18] Deb, K., "Multi objective optimization using evolutionary algorithms”, John Wiley & Sons (ASIA) Pte. Ltd., Singapore, 2001.