NDLI: An adaptive control strategy for the abrasive waterjet cutting process with the integration of vision-based monitoring and a neuro-genetic control strategy

Content Provider	Springer Nature Link
Author	Srinivasu, D. S. Babu, N. Ramesh
Copyright Year	2007
Abstract	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.
Starting Page	514
Ending Page	523
Page Count	10
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	38
Issue Number	5-6
e-ISSN	14333015
Language	English
Publisher	Springer-Verlag
Publisher Date	2007-11-27
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Abrasive waterjet cutting Machine vision Artificial neural networks Genetic algorithms Control strategy Computer-Aided Engineering (CAD, CAE) and Design Mechanical Engineering Production/Logistics Industrial and Production Engineering
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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