This paper presents a new approach for modelling the thrust force in the drilling of PA6–nanoclay (NC) nanocomposite materials, by using a particle-swarm-optimization-based neural network (PSONN). In this regard, the advantages of the statistical experimental algorithm technique, experimental measurements, particle swarm optimization, and artificial neural network were exploited in an integrated manner. For this purpose, numerous experiments for PA6 and PA6–NC nanocomposites were conducted to obtain the thrust force values using a high-speed steel drill 2 mm in diameter with a 118° point angle. Then, a predictive model for the thrust force was created using the PSONN algorithm. The training capacity of the PSONN was compared with that of the conventional neural network. The results indicate that, when the NC content in PA6 is increased, the thrust force of the drilling operation on this material is decreased significantly. Also, the results obtained about the modelling of the thrust force showed the good training capacity of the proposed PSONN algorithm, compared with that of a conventional neural network.
LuoJ.-J.DanielI. M.Characterization and modeling of mechanical behavior of polymer/clay nanocomposites. Composites Sci. Technol., 2003, 63, 1607–1616.
2.
FangF. Z.LiuY. C.On minimum exit-burr in micro cutting. J. Micromech. Microengng, 2004, 4, 984–988.
3.
DavimJ. P.ReisP.Machinability study on composite (polyetheretherketone reinforced with 30 per cent of glass fiber-PEEK GF30) using polycrystalline diamond (PCD) and cemented carbide (K20) tools. Int. J. Advd Mfg Technol., 2004, 23, 412–418.
4.
DavimJ. P.SilvaL. R.FestasA.AbraoA. M.Machinability study on precision turning of PA66 polyamide with and without glass fiber reinforcing. Mater. Des., 2009, 30, 228–234.
5.
DhokiaV. G.KumarS.VichareP.NewmanS. T.AllenR. D.Surface roughness prediction model for CNC machining of polypropylene. Proc. IMechE, Part B: J. Engineering Manufacture, 2008, 222(2), 137–157. DOI: 10.1243/09544054JEM88410.1243/09544054JEM884.
6.
DhokiaV. G.KumarS.VichareP.NewmanS. T.An intelligent approach for the prediction of surface roughness in ball-end machining of polypropylene. Robotics Comput.-Integrated Mfg, 2008, 24, 835–842.
7.
BenardosP. G.VosniakosG. C.Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments. Robotics Comput.-Integrated Mfg, 2002, 18, 343–354.
8.
RazfarM. R.Zanjani ZadehM. R.Optimum damage and surface roughness prediction in end milling glass fiber-reinforced plastics, using neural network and genetic algorithm. Proc. IMechE, Part B: J. Engineering Manufacture, 2009, 223(6), 653–664. DOI: 10.1243/09544054JEM140910.1243/09544054JEM1409.
9.
YuJ.WangS.XiL.Evolving artificial neural networks using an improved PSO and DPSO. Neurocomputing, 2008, 71, 1054–1060.
GuoY. W.MilehamA. R.OwenG. W.MaropoulosP. G.LiW. D.Operation sequencing optimization for five-axis prismatic parts using a particle swarm optimization approach. Proc. IMechE, Part B: J. Engineering Manufacture, 2008, 223(5), 485–497. DOI: 10.1243/09544054JEM122410.1243/09544054JEM1224.
12.
ChauK. W.Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun River. J. Hydrol., 2006, 329(3–4), 363–367.
13.
ChangQ.-L.ZhouH.-Q.HouC.-J.Using particle swarm optimization algorithm in an artificial neural network to forecast the strength of paste filling material. J. China Univ. Mining Technol., 2008, 18, 551–555.
14.
OnwuboluG. C.ClercM.Optimal path for automated drilling operations by a new heuristic approach using particle swarm optimization. Int. J. Prod. Res., 2004, 42(3), 473–491.
15.
ZhouJ.DuanZ.LiY.DengJ.YuD.PSO-based neural network optimization and its utilization in a boring machine. J. Mater. Processing Technol., 2006, 178, 19–23.
16.
NatarajanU.PeriasamyV. M.SaravananR.Application of particle swarm optimisation in artificial neural network for the prediction of tool life. Int. J. Advd Mfg Technol., 2007, 31, 871–876.
17.
CheZ. H.PSO-based back-propagation artificial neural network for product and mold cost estimation of plastic injection molding. Comput. Ind. Engng, 2010, 58, 625–637.
18.
RazfarM. R.AsadniaM. A.HaghshenasM.FarahnakianM.Optimum surface roughness prediction in face milling X20Cr13 using particle swarm optimization algorithm. Proc. IMechE, Part B: J. Engineering Manufacture, 2010, 224(11), 1645–1653. DOI: 10.1243/09544054JEM180910.1243/09544054JEM1809.
19.
StrenkowskiJ. S.HsiehC. C.ShihA. J.An analytical finite element technique for predicting thrust force and torque in drilling. Int. J. Mach. Tools Mf., 2004, 44(12–13), 1413–1421.
20.
ZitouneR.CollombetF.Numerical prediction of the thrust force responsible of delamination during the drilling of the long-fibre composite structures. Composites: Part A, 2007, 38, 858–866.
21.
TsaoaC. C.HochengH.Evaluation of thrust force and surface roughness in drilling composite material using Taguchi analysis and neural network. J. Mater. Processing Technol., 2008, 203, 342–348.
22.
HayajnehM. T.HassanA. M.MayyasA. T.Artificial neural network modeling of the drilling process of self-lubricated aluminum/alumina/graphite hybrid composites synthesized by powder metallurgy technique. J. Alloys Compounds, 2009, 478, 559–565.
23.
Vimal Sam SinghR.LathaB.SenthilkumarV. S.Modeling and analysis of thrust force and torque in drilling GFRP composites by multi-facet drill using fuzzy logic. Int. J. Recent Trends Engng, 2009, 1(5), 66–70.
24.
KennedyJ.EberhartR. C.Particle swarm optimization, Proceedings of the IEEE International Conference on Neural Networks. 1995, pp. 1942–1948 (IEEE, New York).
ChenK.LiT.CaoT.Tribe-PSO: a novel global optimization algorithm and its application in molecular docking. Chemometrics Intell. Lab. Systems, 2006, 82, 248–259.
28.
ZhangJ.LiuK.TanY.KNOB particle swarm optimizer6145Advances in Swarm IntelligenceProceedings of the First International Conference on Swarm Intelligence (ICSI 2010), Lecture Notes in Computer Science12–15 June 2010, Part I Berlin. 2010, pp. 78–85 (Springer, Beijing, People’s Republic of China) 12–15 June 2010, Part I Berlin, 12–15 June 2010, Part I Berlin.
29.
Southern Clay Products., A Subsidiary of Rockwood Specialties, Inc., Product Bulletin/Nanofil, http://www.scprod.com (Accessed April 2011).
30.
MoghriM.GarmabiH.Investigation of effect of formulation and processing parameters on properties of PA-6 nanocomposites using Taguchi method. Int. Polym. Processing, 2008, 23(5), 430–438.
31.
ChenW.-C.Some experimental investigation in the drilling of carbon fiber-reinforced plastic (CFRP) composite laminates. Int. J. Mach. Tools Mf., 1997, 37(8), 1097–1108.
ZhangQ.A study of high performance twist drill design and the associated predictive force models. PhD Thesis, 2007 (School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, New South Wales, Australia).
