Abstract
Machining of particle-reinforced metal matrix composites has been considerably difficult due to the extremely abrasive nature of the reinforcements that causes rapid tool wear and high machining cost. Abrasive water jet (AWJ) machining has proven to be a viable technique to machine such materials compared to conventional machining processes. The present study is focused on the surface roughness of AWJ cut surfaces and genetic expression programming (GEP) was proposed to predict surface roughness in AWJ machining of 7075 Al alloy composites reinforced with Al2O3 particles. In the development predictive models, characteristics of materials such as size and weight fraction of reinforcement particles, and depth of cut were considered as model variables. The training and testing data sets were obtained from the well-established machining test results. The weight fraction of particle, size of particle, and depth of cut were used as independent input variables, while arithmetic mean of surface roughness, maximum roughness of profile height, and mean spacing of profile irregularity as dependent output variables. Different models for the output variables were predicted on the basis of training data set using GEP and accuracy of the best model was proved with testing data set. The test results showed that output variables increased with increasing input variables. The predicted results were compared with experimental results and found to be in good agreement with the experimentally observed ones.
Similar content being viewed by others
References
Müller F, Monaghan J (2000) Non-conventional machining of particle reinforced metal matrix composites. Int J Mach Tool Manuf 40:1351–1366
Manna A, Bhattacharayya B (2003) A study on machinability of Al/SiC-MMC. J Mater Process Technol 140:711–716
Kannan S, Kishawy HA, Deiab IM, Surappa MK (2006) On the role of reinforcements on tool performance during cutting of metal matrix composites. J Manuf Process 8(2):67–75
Brazil D, Monaghan J, Aspinwall DK, Ng EG (1997) Wear characterization of various diamond tooling when single point turning a particle reinforced metal matrix composite. Proc. of the IMC-14 Conf, Dublin, Ireland, pp. 143–152
Weinert K (1993) A consideration of tool wear mechanism when machining metal matrix composites (MIMC). Ann CIRP 42(1):95–98
Hung NP, Yang LJ, Leong KW (1994) Electro discharge machining of cast metal matrix composites. J Mater Process Technol 44:229–236
Le Roux T, Wise MLH, Aspinwall DK (1993) The effect of electro discharge machining on the surface integrity of an aluminium-silicon carbide metal matrix composite. J Pro Adv Mater 3:233–241
Müller F, Monaghan J (1998) Electro discharge machining of a particle reinforced metal matrix composite. Proc of the 12th Int Sym for Electro Machining (ISEM XII), VDI-Berichte 1405, Aachen, pp. 513–522
Lau WS, Lee WB, Pang SQ (1990) Pulsed ND: YAG laser cutting of carbon fiber composite materials. Ann CIRP 39(1):179–182
Müller F, Monaghan J (1998) Laser cutting of particle reinforced metal matrix composites. Proc 6th SheMet Conf, Twente, Netherlands, pp. 93–101
Hamatani G, Ramulu M (1990) Machinability of high temperature composite by abrasive waterjet. ASME J Eng Mat Tech 122:381–386
Shanmugam DK, Chen FL, Siores E, Brandt M (2002) Comparative study of jetting machining technologies over laser machining technology for cutting composite materials. J Compos Struct 57:289–296
Parikh PJ, Lam SS (2009) Parameter estimation for abrasive water jet machining process using neural networks. Int J Adv Manuf Technol 40:497–502
Momber AW, Kovacevic R (1998) Principles of abrasive waterjet machining. Springer, London
Hashish M (1991) Optimization factors in abrasive waterjet machining. Transact ASME J Eng Ind 113:29–37
Jegaraj JJR, Babu NR (2005) A strategy for efficient and quality cutting of materials with abrasive waterjets considering the variation in orifice and focusing nozzle diameter. Int J Mach Tool Manuf 45:1443–1450
Babu MK, Chetty OVK (2006) A study on the use of single mesh size abrasives in abrasive waterjet machining. Int J Adv Manuf Technol 29:532–540
Akkurt A, Kulekci MK, Seker U, Ercan F (2004) Effect of feed rate on surface roughness in abrasive waterjet cutting applications. J Mater Process Technol 147:389–396
Hascalik A, Çaydaş U, Gürün H (2007) Effect of traverse speed on abrasive waterjet machining of Ti-6Al-4V alloy. Mater Des 28:1953–1957
Çaydaş U, Hasçalık A (2008) A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method. J Mater Process Technol 202:574–582
Hashish M (1984) A modeling study of metal cutting with abrasive waterjets. J Eng Mater Technol 106:88–100
Arola D, Ramulu M (1993) Mechanism of material removal in abrasive waterjet machining of common aerospace materials. In: Proceedings of the seventh American waterjet conference, Seattle (WA), pp. 43–64
Blickwedel H, Guo NS, Haferkamp H, Louis H (1991) Prediction of abrasive jet cutting performance and quality. Jet Cutting Technol 163–179
Shanmugam DK, Masood SH (2009) An investigation on kerf characteristics in abrasive waterjet cutting of layered composites. J Mater Process Technol 209:3887–3893
Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT, Cambridge
Davidson JV, Savic DA, Walters GA (2003) Symbolic and numerical regression: experiments and application. Inf Sci 150(1/2):95–117
Ong CS, Huang JJ, Tzeng GH (2005) Expert Syst Appl 29(1):41–47
Asbour AF, Alvarez LF, Toropov VV (2003) Comput Struct 81(5):331–338
Ferreira C (2001) Compl Syst 13(2):87–129
Ferreira C (2001) Gene Expression Programming in Problem Solving. In: Invited Tutorial of the 6th Online World Conference on Soft Computing in Industrial Applications, pp. 10–24
Ferreira C (2002) Gene expression programming: mathematical modelling by an artificial intelligence
Kok M (2005) Production and mechanical properties of Al2O3 particle-reinforced 2024 aluminium alloy composites. J Mater Process Technol 161:381–387
Sahin Y, Kok M, Celik H (2002) Tool wear and surface roughness of Al2O3 particle-reinforced aluminium alloy composites. J Mater Process Technol 128:280–291
Eskil M, Kanca E (2008) A new formulation for martensite start temperature of Fe–Mn–Sishape memory alloys using genetic programming. Comput Mater Sci 43:774–784
Baykasoglu A, Oztas A, Ozbay E (2009) Prediction and multi-objective optimization of high-strength concrete parameters via soft computing approaches. Expert Syst Appl 36(3):6145–6155, April Part 2
Eyercioglu O, Kanca E, Pala M, Ozbay E (2008) Prediction of martensite and austenite start temperatures of the Fe-based shape memory alloys by artifical neural networks. J Mater Process Technol 200:146–152
Kanca E, Eskil M (2009) Comparison of new formulations for Martensite start temperature of Fe–Mn–Si shape memory alloys using geneting programming and neural networks. Comp, Mater Cont 270(1):1–31
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kök, M., Kanca, E. & Eyercioğlu, Ö. Prediction of surface roughness in abrasive waterjet machining of particle reinforced MMCs using genetic expression programming. Int J Adv Manuf Technol 55, 955–968 (2011). https://doi.org/10.1007/s00170-010-3122-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-010-3122-4