Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Application of artificial neural networks in abrasive waterjet cutting process
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Structural damage detection using fuzzy cognitive maps and Hebbian learning
Applied Soft Computing
Intelligent process modeling and optimization of die-sinking electric discharge machining
Applied Soft Computing
Optimization of process parameters in the abrasive waterjet machining using integrated SA-GA
Applied Soft Computing
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This paper presents a neuro-genetic approach proposed to suggest the process parameters for maintaining the desired depth of cut in abrasive waterjet (AWJ) cutting by considering the change in diameter of focusing nozzle, i.e. for adaptive control of AWJ cutting process. An artificial neural network (ANN) based model is developed for prediction of depth of cut by considering the diameter of focusing nozzle along with the controllable process parameters such as water pressure, abrasive flow rate, jet traverse rate. ANN model combined with genetic algorithm (GA), i.e. neuro-genetic approach, is proposed to suggest the process parameters. Further, the merits of the proposed approach is shown by comparing the results obtained with the proposed approach to the results obtained with fuzzy-genetic approach [P.S. Chakravarthy, N. Ramesh Babu, A hybrid approach for selection of optimal process parameters in abrasive water jet cutting, Proceedings of the Institution of Mechanical Engineers, Part B: J. Eng. Manuf. 214 (2000) 781-791]. Finally, the effectiveness of the proposed approach is assessed by conducting the experiments with the suggested process parameters and comparing them with the desired results.