This paper presents a hybrid approach combining fuzzy logic principles and a genetic approach for selecting optimal parameters in abrasive water jet cutting of any material with predetermined thickness. A fuzzy model is built with the knowledge base formed by means of experimental data that is generated by varying the process parameters such as water jet pressure, jet traverse rate and abrasive flowrate at five levels each. This particular model predicts the depth of cut achievable with any given set of process parameters. A genetic algorithm employed in combination with a fuzzy model automatically determines the best combination of process parameters in abrasive water jet cutting of any material. The effectiveness of the proposed approach is demonstrated by a case study dealing with abrasive water jet cutting of black granite. © IMechE 2000.

N. Ramesh Babu

Department of Mechanical Engineering

Abrasive cutting

Fuzzy control

Genetic algorithms

Mathematical models

Optimization

Process control

Abrasive water jet cutting

Process engineering

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

A hybrid approach for selection of optimal process parameters in abrasive water jet cutting

Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture

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.

International Journal of Advanced Manufacturing Technology

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 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.

Applied Soft Computing Journal

A neuro-genetic approach for selection of process parameters in abrasive waterjet cutting considering variation in diameter of focusing nozzle

This paper covers the investigations conducted to study the influence of jet impact angle on the geometry of part produced by Abrasive Waterjet (AWJ) turning process. Preliminary investigations were conducted on a specially designed cylindrical test specimen to examine the influence of jet impact angle and other process parameters on the depth of penetration of AWJs into the material. Based on the results of these investigations, the set of conditions suitable for efficient material removal were determined. The same conditions were chosen for turning of cylindrical parts with AWJs with a view to analyse the influence of these conditions on the material removal and the geometry of part produced. The material removal was characterised by the reduction in the diameter of part, and the geometry of part was assessed by means of roundness error and finish on the surface of part turned with AWJs. © 2008, Inderscience Publishers.

International Journal of Machining and Machinability of Materials

Influence of jet impact angle on part geometry in abrasive waterjet turning of aluminium alloys

Choice Reviews Online

Genetic algorithms in search, optimization, and machine learning

Who's Who

Owens, Prof. David Howard, (born 23 April 1948), Professor of Control and Systems Engineering, and Head of Department, Automatic Control and Systems Engineering, University of Sheffield, 1999–2009, now Emeritus; Professor of Engineering, Zhengzhou University, China, since 2016

Journal	Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture
Publisher	Professional Engineering Publishing Ltd.
ISSN	09544054
Open Access	No