A neuro-genetic approach for selection of process parameters in abrasive waterjet cutting considering variation in diameter of focusing nozzle
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. © 2007 Elsevier B.V. All rights reserved.
An adaptive control strategy for the abrasive waterjet cutting process with the integration of vision-based monitoring and a neuro-genetic control strategy
This paper presents an integrated approach for the monitoring and control of abrasive waterjet (AWJ) cutting process. A machine-vision-based monitoring approach was proposed to obtain the bore diameter of the focusing nozzle from time to time. A neuro-genetic approach, proposed by Srinivasu and Ramesh Babu (Appl Soft Comput 8(1):809-819, 2008) was employed as a control strategy to modify the process parameters, such as water pressure, abrasive flow rate, and jet traverse rate, so as to maintain the desired depth of cut, with changes in the diameter of the focusing nozzle monitored with a machine vision system. By combining the monitoring and control strategies, an integrated approach for adaptive control of AWJ cutting process is realized. The effectiveness of the proposed integrated approach for adaptive control of AWJ cutting process was shown by comparing the results obtained from the experiments with the process parameters suggested by the control strategy to achieve the desired depth of cut. From the results of the study, it is seen that the proposed monitoring system is capable of monitoring the focusing nozzle diameter with a mean absolute deviation of 0.05 mm and that the neuro-genetic strategy is capable of modifying the controllable process parameters to maintain the desired depth of cut with a mean absolute deviation of 0.87 mm. © 2007 Springer-Verlag London Limited.