Neural network modeling and particle swarm optimization (PSO) of process parameters in pulsed laser micromachining of hardened AISI H13 steel

Materials and Manufacturing Processes

Volumn 24, Issue 3, 2009, Pages 358-368

  Ciurana, J ^a   Arias, G ^b   Ozel, T ^c  

^a UNIVERSITY OF GIRONA   (Spain)

^b UNIVERSITAT POLITÈCNICA DE CATALUNYA   (Spain)

^c DEPARTMENT OF INDUSTRIAL AND SYSTEMS ENGINEERING   (United States)

Author keywords

Laser technology; Mold making; Neural network models; Surface roughness

Indexed keywords

BACKPROPAGATION; COMPOSITE MICROMECHANICS; CONTINUOUS WAVE LASERS; HARDENING; KNOWLEDGE BASED SYSTEMS; METAL ANALYSIS; METAL RECOVERY; MICROMACHINING; MOLDS; MULTIOBJECTIVE OPTIMIZATION; NEURAL NETWORKS; PARTICLE SWARM OPTIMIZATION (PSO); PULSED LASER APPLICATIONS; SURFACE PROPERTIES; SURFACE ROUGHNESS; THREE DIMENSIONAL; TOOL STEEL; ULTRASHORT PULSES;

AISI H13; AISI H13 STEELS; ARTIFICIAL NEURAL NETWORKS; CONTINUOUS WAVES; CONVENTIONAL MACHINING; DESIGNED EXPERIMENTS; GEOMETRICAL FEATURES; HIGH PRECISION; LASER MICRO-MACHINING; LASER TECHNOLOGY; MATERIAL ABSORPTIONS; MATERIAL REMOVALS; MOLD CAVITIES; MOLD MAKING; MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATIONS; NEURAL NETWORK MODELING; NEURAL NETWORK MODELS; OPERATIONAL PARAMETERS; PARTICLE-SWARM OPTIMIZATIONS; PROCESS PARAMETERS; PROCESS SETTINGS; PULSED LASERS; PULSED ND:YAG LASERS; QUALITY CHARACTERISTICS; SWARM OPTIMIZATIONS; WORK MATERIALS;

NEODYMIUM LASERS;

EID: 61449183703     PISSN: 10426914     EISSN: 15322475     Source Type: Journal    
DOI: 10.1080/10426910802679568     Document Type: Article

References (23)

1. Dubey, A.K.; Yadava, V. Laser beam machining-A review. International Journal of Machine Tools and Manufacture 2008, 48, 609-628.
2. Pham, D.T.; Dimov, S.S.; Petkov, P.V.; Dobrev, T. Laser milling as a 'rapid' micromanufacturing process. Proceedings of the I MECH E Part B. Journal of Engineering Manufacture 2004, 218 (1), 1-7.
3. 2 laser cut quality of 4130 steel. International Journal of Machine Tools and Manufacture 2003, 43 (4), 351-358.
4. Abdel Ghany, K.; Newishy, M. Cutting of 1.2mm thick austenitic stainless steel sheet using pulsed and CW Nd: YAG laser. Journal of Materials Processing Technology 2005, 168 (3), 438-447.
5. Yilbas, B.S. Laser cutting of thick sheet metals: Effects of cutting parameters on kerf size variations. Journal of Materials Processing Technology 2008, 201 (1-3), 285-290.
6. Thawari, G.; Sarin Sundar, J.K.; Sundararajan, G.; Joshi, S.V. Influence of process parameters during pulsed Nd:YAG laser cutting of nickel-base superalloys. Journal of Materials Processing Technology 2005, 170 (1-2), 229-239.
7. Bordatchev, E.V.; Nikumb, S.K. An experimental study and statistical analysis of the effect of laser pulse energy on geometric quality during laser precision machining. Machining Science and Technology 2003, 7 (1), 83-104.
8. Dobrev, T.; Dimov, S.S.; Thomas, A.J. Laser milling: modelling crater and surface formation. Proceedings I Mech E Part C: Journal of Mechanical Engineering 2006, 220 (6), 1685-1696.
9. Petkov, P.V.; Dimov, S.S.; Minev, R.M.; Pham, D.T. Laser milling: Pulse duration effects on surface integrity. Proceedings of the I MECH E Part B. Journal of Engineering Manufacture 2008, 222 (1), 35-45.
10. Bandyopadhyaya, S.; Gokhaleb, H.; Sarin Sundar, J.K.; Sundararajanc, G.; Joshi, S.V. A statistical approach to determine process parameter impact in Nd:YAG laser drilling of IN718 and Ti-6A1-4V sheets. Optics and Lasers in Engineering 2005, 43 (2), 163-182.
11. Li, C.H.; Tsai, M.J.; Yang, C.D. Study of optimal laser parameters for cutting QFN packages by Taguchi's matrix method. Optics and Laser Technology 2007, 39 (4), 786-795.
12. Quintero, F.; Pou, J.; Lusquiños, F.; Boutinguiza, M.; Soto, R.; Perez-Amor, M. Quantitative evaluation of the quality of the cuts performed on mullite-alumina by Nd:YAG laser. Optics and Lasers in Engineering 2004, 42 (3), 327-340.
13. Campanelli, S.L.; Ludovico, A.D.; Bonserio, C.; Cavalluzzi, P.; Cinquepalmi, M. Experimental analysis of the laser milling process parameters. Journal of Materials Processing Technology 2007, 191, 220-223.
14. Tsai, M.J.; Li, C.H.; Chen, C.C. Optimal laser-cutting parameters for QFN packages by utilizing artificial neural networks and genetic algorithm. Journal of Materials Processing Technology 2008, 168 (3), 438-447.
15. Yousef, B.F. Neural network modeling and analysis of the material removal process during laser machining. International Journal of Advanced Manufacturing Technology 2003, 22 (1-2), 41-53.
16. Matlab User's Guide. Neural Network Toolbox, The MathWorks, 2002.
17. Ozel, T.; Karpat, Y. Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. International Journal of Machine Tools and Manufacture 2005, 45 (4-5), 467-479.
18. Ozel, T.; Karpat, Y.; Figueira, L.; Davim, J.P. Modelling of surface finish and tool flank wear in turning of AISI D2 steel with ceramic wiper inserts. Journal of Materials Processing Technology 2007, 189 (1-3), 192-198.
19. Eberhart, R.C.; Kennedy, J. Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ, 1995; 1942-1948.
20. Karpat, Y.; Özel, T. Multi-objective optimization for turning processes using neural network modeling and dynamicneighborhood particle Swarm optimization. International Journal of Advanced Manufacturing Technology 2007, 35 (3-4), 234-247.
21. Chakraborti, N.; Jayakanth, R.; Das, S.; Calisir, E.A.; Erkoc, S. Evolutionary and genetic algorithms applied to Li+-C system: Calculations using differential evolution and particle swarm algorithm. Journal of Phase Equilibria and Diffusion 2007, 28 (2), 140-149.
22. + ions contained in carbon nanotubes: An investigation using particle swarm optimization and differential evolution along with molecular dynamics. Materials and Manufacturing Processes 2007, 22 (5-6), 562-569.
23. Hu, X.; Eberhart, R. Multiobjective optimization using dynamic neighborhood particle swarm optimization. Proceeedings of IEEE Swarm Intelligence Symposium 2002; 1404-1411.