Optimisation of spark erosion machining process parameters using hybrid grey relational analysis and artificial neural network model
by N. Manikandan; Ramesh Raju; D. Palanisamy; J.S. Binoj
International Journal of Machining and Machinability of Materials (IJMMM), Vol. 22, No. 1, 2020

Abstract: Hastealloy C276 is hard to machine superalloy and extensively used in various engineering applications. It possess good strength and lower thermal conductivity which results in decreased tool life and poor machinability by conventional machining. Advanced machining processes have developed to overcome these difficulties and claimed as an alternative methods. Electrical Discharge Machining (EDM) is one of the advanced method used for machining of hard materials. This article details an investigation on EDM process and development of hybrid Grey ANN model. Taguchi method and ANOVA are used for designing the experiments and statistical analysis respectively. Grey Relational Analysis is adopted for determining the Grey Relational Grade (GRG) to represent the multi aspect optimization model and a neural network has been evolved to predict GRG by feeding the Grey Relational Co-efficient (GRC) values as input to developed neural network model. A comparison has been done between the experimental values and predicted values.

Online publication date: Fri, 06-Dec-2019

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Machining and Machinability of Materials (IJMMM):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com