High-accuracy wire electrical discharge machining using artificial neural networks and optimization techniques

Robotics and Computer-Integrated Manufacturing

Volumn 49, Issue , 2018, Pages 24-38

  Conde, A ^a,b   Arriandiaga, A ^b   Sanchez, J A ^b   Portillo, E ^b   Plaza, S ^b   Cabanes, I ^b  

^a Machine Tool Institute (IMH)   (Spain)

^b UNIVERSITY OF THE BASQUE COUNTRY UPV EHU   (Spain)

Author keywords

Advanced manufacturing; Artificial neural networks; Path optimization; Simulated annealing; Wire electrical discharge machining

Indexed keywords

DEFORMATION; ELECTRIC DISCHARGE MACHINING; ELECTRIC DISCHARGES; FORECASTING; MANUFACTURE; NETWORK LAYERS; NEURAL NETWORKS; RECURRENT NEURAL NETWORKS; SIMULATED ANNEALING;

ADVANCED MANUFACTURING; COEFFICIENT OF VARIATION; MANUFACTURING TECHNOLOGIES; NONCONVENTIONAL MACHINING; OPTIMIZATION TECHNIQUES; PATH OPTIMIZATIONS; PRECISION COMPONENTS; WIRE ELECTRICAL DISCHARGE MACHINING;

WIRE;

EID: 85019980913     PISSN: 07365845     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.rcim.2017.05.010     Document Type: Article

References (39)

1. Ho, K.H., Newman, S.T., Rahimifard, S., Allen, R.D., State of the art in wire electrical discharge machining (WEDM). Int. J. Mach. Tools Manuf 44 (2014), 1247–1259.
2. Welling, D., Results of surface integrity and fatigue study of Wire-EDM compared to Broaching and Grinding for demanding jet engine components made of Inconel 718. Proc. CIRP 13 (2014), 339–344.
3. Han, F., Kunieda, M., Sendai, T., Imai, Y., Simulation on influence of electrostatic force on machining characteristics in WEDM. Int. J. Electr. Mach., 2002, 31–36.
4. H. Obara, T. Ishizu, T Ohsumi, Y. Iwata. Simulation of wire EDM, Int Symp ElectroMach XII 1998; 99–108.
5. Tomura, S., Kunieda, M., An analysis of electromagnetic force in wire-EDM. Precis. Eng. 33 (2009), 255–262.
6. Puri, A.B., Bhattacharyya, B., Modelling and analysis of the wire-tool vibration in wire-cut EDM. J. Mater. Process. Technol., 2003, 295–301.
7. Chen, Z., Huang, Y., Huang, H., Zhang, Z., Zhang, G., Three-dimensional characteristics analysis of the wire-tool vibration considering spatial temperature field and electromagnetic field in WEDM. Int. J. Mach. Tools Manuf 92 (2015), 85–96.
8. Habib, S., Okada, A., Study on the movement of wire electrode during fine wire electrical discharge machining process. J. Mater. Process. Technol. 227 (2016), 147–152.
9. Beltrami, I., Berthoolds, A., Dauw, D., A simplified post process for wire cut EDM. J. Mater. Process. Technol. 58 (1996), 385–389.
10. Puri, A.B., Bhattacharyya, B., An analysis and optimisation of the geometrical inaccuracy due to wire lag phenomenon in WEDM. Int. J. Mach. Tools Manuf, 2003, 151–159.
11. Sarkar, S., Sekh, M., Mitra, S., Bhattacharyya, B., A novel method of determination of wire lag for enhanced profile accuracy in WEDM. Precis. Eng. 25 (2011), 339–347.
12. Hsue, W.J., Liao, Y.S., Lu, S.S., Fundamental geometry analysis of wire electrical discharge machining in corner cutting. Int. J. Mach. Tools Manuf 39 (1999), 651–667.
13. Sanchez, J.A., Rodil, J.L., Herrero, A., Lopez de Lacalle, L.N., Lamikiz, A., On the influence of cutting speed limitation on the accuracy of wire-EDM corner-cutting. J. Mater. Process. Technol. 182 (2007), 574–579.
14. Dekeyser, W.L., Snoeys, R., Geometrical accuracy of wire EDM. Int. Symp. Electro. Mach. IX (1989), 226–232.
15. Sanchez, J.A., Lopez de Lacalle, L.N., Lamikiz, A., A computer-aided system for the optimization of the accuracy of the wire electro-discharge machining process. Int. J. Comput. Integr. Manuf. 17 (2004), 413–420.
16. Han, F., Zhang, J., Soichiro, I., Corner error simulation of rough cutting in wire EDM. Precis. Eng. 31 (2007), 331–336.
17. Chen, Z., Huang, Y., Zhang, Z., Li, H., Ming, W., Zhang, G., An analysis and optimization of the geometrical inaccuracy in WEDM rough corner cutting. Int. J. Adv. Manuf. Technol. 74 (2014), 917–929.
18. Coit, D.W., Smith, A.E., Using Designed Experiments to Produce Robust Neural Network Models of Manufacturing Processes. 4th Industrial Engineering Research Conference Proceedings, 1995, 229–238.
19. Benardos, P.G., Vosniakos, G.C., Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments. Rob. Comput. Integr. Manuf. 18 (2002), 343–354.
20. Benardos, P.G., Mosialos, S., Vosniakos, G.C., Prediction of workpiece elastic deflections under cutting forces in turning. Rob. Comput. Integr. Manuf. 22 (2006), 505–514.
21. Cabanes, I., Portillo, E., Marcos, M., Sanchez, J.A., On-line prevention of wire breakage in wire electro-discharge machining. Rob. Cim-Int Manuf 24 (2008), 287–298.
22. Rao, G.K.M., Rangajanardhaa, G., Rao, G.D.H., Rao, M.S., Development of hybrid model and optimization of surface roughness in electric discharge machining using artificial neural networks and genetic algorithm. J. Mater. Process. Technol. 3 (2009), 1512–1520.
23. Khan, M.A.R., Khan, R., Rahman, M.M., Kadirgama, Neural network modeling and analysis for surface characteristics in electrical discharge machining. Proc. Eng. 90 (2014), 631–636.
24. Ugrasen, G., Ravindra, H.V., Prakash, G.V.N., Keshavamurthy, K., Process optimization and estimation of machining performances using artificial neural network in wire EDM. Proc. Mater. Sci. 6 (2014), 1752–1760.
25. Shandilyaa, P., Jain, P.K., Jain, N.K., RSM and ANN modeling approaches for predicting average cutting speed during WEDM of SiCp/6061 Al MMC. Proc. Eng. 64 (2013), 767–774.
26. Arriandiaga, A., Portillo, E., Sánchez, J.A., Cabanes, I., Pombo, I., A new approach for dynamic modelling of energy consumption in the grinding process using recurrent neural networks. Neural Comput. Appl., 2015 (on-line).
27. Shayan, A.V., Afza, R.A., Teimouri, R., Parametric study along with selection of optimal solutions in dry wire cut machining of cemented tungsten carbide (WC-Co). J Manuf. Process. 15 (2013), 644–658.
28. Chen, H.C., Lin, J.C., Yang, Y.K., Tsai, C.H., Optimization of wire electrical discharge machining for pure tungsten using a neural network integrated simulated annealing approach. Expert Syst. Appl. 37 (2010), 7147–7153.
29. Joshi, S.N., Pande, S.S., Intelligent process modelling and optimization of die-sinking electric discharge machining. Appl. Soft Comput. 11 (2011), 2743–2755.
30. Shakeri, S., Ghassemi, A., Hassani, M., Hajian, A., Investigation of material removal rate and surface roughness in wire electro discharge machining process for cementation alloy steel using artificial neural network. Int. J. Adv. Manuf. Technol. 82 (2016), 549–557.
31. Haykin, S., Neural Networks: A Comprehensive Foundation. 2nd ed., 1998, Prentice-Hall, Upper Saddle River, NJ.
32. Beale, M.H., Hagan, M.T., Demuth HB Neural Network Toolbox User's Guide. 1992, 2012, The MathWorks Inc.
33. Hagan, M.T., Menhaj, M., Training feed-forward networks with the Marquardt algorithm. IEEE Trans. Neural Networks 5 (1994), 989–993.
34. Dan Foresse, F., Hagan MT Gauss-Newton approximation to Bayesian learning. International Conference on Neural Networks, Houston, U.S.A., 1997, 9–12 Jun 1997.
35. Werbos, PJ., Backpropagation through time: What it does and how to do it. Proc. IEEE 78:10 (1990), 1550–1560.
36. Nguyen, D., Widrow, B., Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights. International Joint Conference on Neural Networks (IJCNN), 3, 1990, 21–26.
37. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P., Optimization by simulated annealing. Science, new series 220:4598 (1983), 671–680.
38. Bertsimas, D., Tsitsiklis, J., Simulated Annealing. Stat. Sci. 8:1 (1993), 10–16.
39. Rasdi Rere, L.M., Fanany, M.I., Arymurthy, A.M., Simulated annealing algorithm for deep learning. Procedia Comput. Sci. 72 (2015), 137–144.