PREDICTION OF FLANK WEAR IN DRILLING USING DIFFERENTIAL EVOLUTION TRAINED NEURAL NETWORKS
Keywords:
Flank Wear, Drilling, Differential Evolution, Neural NetworkAbstract
The rising demand for enhanced performance of manufacturing system has led to new challenges for the development of complex tool condition monitoring techniques. Estimation of tool life generally requires considerable time and relatively expensive. In the present paper, Differential Evolution trained Neural Network (DE-NN) has been developed to predict the flank wear in drilling operation. In DE-NN, the flank wear prediction problem of a drilling operation has been modeled using an NN, whose weights and bias values are optimized offline, using DE. The performance of the developed approach has been compared in terms of their prediction accuracy.
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References
Brinksmeier E (1990), “Prediction of Tool Fracture in Drilling, ANN”, CIRP, Vol. 39(1), 97-100.
Oh Y T, Kwon W T and Chu C N (2004), “Drilling Torque Control using Spindle Motor Current and its Effect on Tool Wear”, International Journal of Advanced Manufacturing Technology, Vol. 24, 327-334.
Zhang M Z, Liu Y B and Zhou H (2001), “Wear Mechanism Maps of Uncoated HSS Tools Drilling Die-Cast Aluminium Alloy”, Tribology International, Vol. 34, 727-731.
Nede V and Himburg T (1986), “Prediction of tool Fracture in Drilling”, Automatisiteerte Uberwachung Des Bohrens, VDI-Z, Vol. 128 ( 17), . 651-657.
Wardeny T I E I, Goa D and Elbestawi M A (1996), “Tool Condition Monitoring in Drilling using Vibration Signature Analysis”, International Journal of Machine Tools and Manufacturing, Vol. 36 (6), 687-711.
Xiaoli L and Tso S K (1999), “Drill Wear Monitoring based on Current Signals”, Wear, Vol. 231, 172-178.
Chowdary S K, Raju G (2000), “Investigation into Crater Wear in Drilling”, International Journal of Machine Tolls and Manufacturing, Vol. 40, 887-889.
Vishy K, Tossapol K (2003), “Tool Condition Monitoring in Drilling using Artificial Neural Networks”, Proc. of the Conference Artificial Intelligence, Perth 3-5, 293-301.
Tsao C C (2002), “Prediction of Flank Wear of Different Coated Drills for JISSUS Stainless Steel using Neural Networks”, Journal of Material Processing Technology, Vol. 123, 354-360.
Yukum, Ertunc H M (2006), “Tool Wear Condition Monitoring in Drilling Process using Fuzzy Logic”, ICONIP, Part III, LNCS 4234, 508-517.
Panda S S, Chakraborthy D and Pal P K (2008), “Flank Wear Prediction in Drilling using Back Propagation Neural Network and Radial Basis Function Network”, Alied Soft Computing, Vol. 8, 858-871.
Alagumuthii N (2009), “Work Roll Grinding - Modeling and Optimization using Artificial Neural Networks and Genetic Algorithm”, Journal of Manufacturing Engineering, Vol. 4(1), 45-51.
Desai C K and Shaikh A A (2006), “Drill Wear Monitoring using Artificial Neural Network with Differential Evolution Learning”, Proc. Of IEEE International Conference on Industrial Technology, 2019-2022.
Storn R and Prince K (1997), “Differential Evolution – A Sample ans Heuristic for Global Optimization over Continuous Spaces”, Journal of Global Optimization, Vol. 11(4), 1997, 341-359.
