Machinability performance of Al–NiTi and Al–NiTi–nano SiC composites with parametric optimization using GSA
Source abstract
Abstract
The manuscript discusses the abrasive water jet machining (AWJM) of Al–NiTi and Al–NiTi–nano SiC composites to understand the influences and the effect of each parameter and to indentify optimal combination of AWJM parameters. The experiments are planned and conducted based on L27 orthogonal array. Pressure, standoff distance, and transverse feed rate are considered as input parameters; surface roughness and kerf angle are considered as output parameters. Gravitational search algorithm (GSA) is employed to identify the best possible combination of AWJM parameters. Regression model is used to develop the surface roughness and kerf angle model for Al–NiTi and Al–NiTi–nano SiC composites. The developed mathematical model is used as fitness function in GSA. It is found from the result of GSA that the optimal value for surface roughness of Al–NiTi composite is set at pressure 176 kPa, standoff distance 1 mm, and transverse feed 20 mm/min and for Al–NiTi–nano SiC composite is set at pressure 180 kPa, standoff distance 1.1 mm, and transverse feed 20 mm/min. Similarly, for kerf angle, the optimal value for Al–NiTi composite is set at pressure 260, standoff distance 1 mm, and transverse feed 20 mm/min and for Al–NiTi–nano SiC composite is set at pressure 255 kPa, standoff distance 1 mm, and transverse feed 20 mm/min. Analysis of variance is also performed to understand the effect of each input parameter on output response.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Erosion and Abrasive Machining · Advanced Surface Polishing Techniques · Advanced machining processes and optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (29)
Show source-provided references
1. Sick, B.: Online and indirect tool wear monitoring in turning with artificial neural networks: a review of more than a decade of research. Mech Syst Signal Process. 16(4), 487–546 (2002)10.1006/mssp.2001.1460 ↗
2. Persson, H.: Machining guidelines of Al/SiC particulate MMC. MMC-assess-thematic network MMC-Assess Consortium. (2001)
3. Teti, R.: Machining of composite materials. CIRP Ann Manuf Technol. 51(2), 611–634 (2002)10.1016/S0007-8506(07)61703-X ↗
4. Akalin, O., Ezirmik K.V., Urgen, M., Newaz G.M.: Wear characteristics of NiTi/Al6061 short fiber metal matrix composite reinforced with SiC particulates. J. Tribol. 132 (4) (2010)10.1115/1.4002332 ↗
5. Narendranath, S., Manjaiah, M., Basavarajappa, S., Gaitonde, V.N.: Experimental investigations on performance characteristics in wire electro discharge machining of Ti50Ni42. 4Cu7. 6 shape memory alloy. Proc Inst Mech Eng B J Eng Manuf. 227(8), 1180–1187 (2013)10.1177/0954405413478771 ↗
6. Abdizadeh, H., Ashuri, M., Moghadam, P.T., Nouribahadory, A., Baharvandi, H.R.: Improvement in physical and mechanical properties of aluminum/zircon composites fabricated by powder metallurgy method. Mater Des. 32(8), 4417–4423 (2011)10.1016/j.matdes.2011.03.071 ↗
7. Rahimian, M., Parvin, N., Ehsani, N.: The effect of production parameters on microstructure and wear resistance of powder metallurgy Al–Al2O3 composite. Mater Des. 32(2), 1031–1038 (2011)10.1016/j.matdes.2010.07.016 ↗
8. Pandey, A., Singh, S.: Current research trends in variants of electrical discharge machining: a review. Int J Eng Sci Technol. 2(6), 2172–2191 (2010)
9. Pfeifer, R., Herzog, D., Hustedt, M., Barcikowski, S.: Pulsed Nd: YAG laser cutting of NiTi shape memory alloys—influence of process parameters. J Mater Process Technol. 210(14), 1918–1925 (2010)10.1016/j.jmatprotec.2010.07.004 ↗
10. Li, H., Wang, J.: An experimental study of abrasive waterjet machining of Ti-6Al-4V. Int J Adv Manuf Technol. 81(1–4), 361–369 (2015)10.1007/s00170-015-7245-5 ↗
11. Parikh Pratik, J., Lam, S.S.: Parameter estimation for abrasive water jet machining process using neural networks. Int J Adv Manuf Technol. 40(5–6), 497–502 (2009)10.1007/s00170-007-1363-7 ↗
12. Rao, R.V., Kalyankar, V.D.: Parameter optimization of modern machining processes using teaching–learning-based optimization algorithm. Eng Appl Artif Intell. 26(1), 524–531 (2013)10.1016/j.engappai.2012.06.007 ↗
13. Jain, N.K., Jain, V.K., Deb, K.: Optimization of process parameters of mechanical type advanced machining processes using genetic algorithms. Int J Mach Tools Manuf. 47(6), 900–919 (2007)10.1016/j.ijmachtools.2006.08.001 ↗
14. Zain, A.M., Haron, H., Sharif, S.: Estimation of the minimum machining performance in the abrasive waterjet machining using integrated ANN-SA. Expert Syst Appl. 38(7), 8316–8326 (2011)10.1016/j.eswa.2011.01.019 ↗
15. Zain, A.M., Haron, H., Sharif, S.: Optimization of process parameters in the abrasive waterjet machining using integrated SA–GA. Appl Soft Comput. 11(8), 5350–5359 (2011)10.1016/j.asoc.2011.05.024 ↗
16. 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(9–12), 955–968 (2011)10.1007/s00170-010-3122-4 ↗
17. Manjaiah, M., Narendranath, S., Basavarajappa, S., Gaitonde, V.N.: Investigation on material removal rate, surface and subsurface characteristics in wire electro discharge machining of Ti50Ni50-xCux shape memory alloy. Proc. Inst. Mech. Eng., Part L: J. Mater.: Des. Applic. (2015)10.1016/j.precisioneng.2015.01.008 ↗
18. Al-Zubaidi, S., Ghani, J.A., Haron, C.H.C.: Optimization of cutting conditions for end milling of Ti6Al4V alloy by using a gravitational search algorithm (GSA). Meccanica. 48(7), 1701–1715 (2013)10.1007/s11012-013-9702-2 ↗
19. Goswami, D., Chakraborty, S.: Parametric optimization of ultrasonic machining process using gravitational search and fireworks algorithms. Ain Shams Eng J. 6(1), 315–331 (2015)10.1016/j.asej.2014.10.009 ↗
20. Kalirasu, S., Rajini, N., Winowlin Jappes, J.T., Uthayakumar, M., Rajesh, S.: Mechanical and machining performance of glass and coconut sheath fibre polyester composites using AWJM. J Reinf Plast Compos. 34(7), 564–580 (2015)10.1177/0731684415574870 ↗
21. Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans Evol Comput. 1(1), 67–82 (1997)10.1109/4235.585893 ↗
22. Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: GSA: a gravitational search algorithm. Inf Sci. 179(13), 2232–2248 (2009)10.1016/j.ins.2009.03.004 ↗
23. Karabulut, Ş.: Optimization of surface roughness and cutting force during AA7039/Al2O3 metal matrix composites milling using neural networks and Taguchi method. Measurement. 66, 139–149 (2015)10.1016/j.measurement.2015.01.027 ↗
24. Çaydaş, U., Hasçalık, A.: A study on surface roughness in abrasive water jet machining process using artificial neural networks and regression analysis method. J Mater Process Technol. 202(1), 574–582 (2008)10.1016/j.jmatprotec.2007.10.024 ↗
25. Sasikumar, K. S. K., K. P. Arulshri, K. Ponappa, and M. Uthayakumar.: A study on kerf characteristics of hybrid aluminium 7075 metal matrix composites machined using abrasive water jet machining technology. Proc. Inst. Mech. Eng., Part B: J. Eng. Manuf. (2016): 0954405416654085.10.1177/0954405416654085 ↗
26. Arola, D., Ramulu, M.: Material removal in abrasive waterjet machining of metals surface integrity and texture. Wear. 210(1–2), 50–58 (1997)10.1016/S0043-1648(97)00087-2 ↗
27. Akkurt, A., Kemal Kulekci, M., Seker, U., Ercan, F.: Effect of feed rate on surface roughness in abrasive waterjet cutting applications. J Mater Process Technol. 147(3), 389–396 (2004)10.1016/j.jmatprotec.2004.01.013 ↗
28. Ritter, J. E., K. Jakus, M. Viens, K. Breder.: Effect of microstructure on impact damage of polycrystalline alumina. In Proc. Seventh Int. Conf. on Erosion by Liquid and Solid Impact, 55 (1) (1987)
29. Stoić, Antun, M. Duspara, B. Kosec, M. Stoić, and I. Samardžić.Application of water jetfor cutting polymer materials. Meta 52 (2), 255–258 (2013)