The full text of this article

 

Modelling and process optimisation for wire electric discharge machining of metal matrix composites
by Pragya Shandilya; P.K. Jain; N.K. Jain
International Journal of Machining and Machinability of Materials (IJMMM), Vol. 18, No. 4, 2016

 

Abstract: This paper describes the process modelling and optimisation of wire electric discharge machining (WEDM) of SiCp/6061 Al metal matrix composite (MMC) through response surface methodology (RSM) and artificial neural network (ANN) approach. The experiments were planned and carried out based on the design of experiments (DOE). Four WEDM input process parameters namely servo voltage (SV), pulse-on time (TON), pulse-off time (TOFF) and wire feed rate (WF) were chosen as machining process parameters. Two response criteria [i.e., material removal rate (MRR) and cutting width (kerf)] were selected during optimisation. The analysis of variance (ANOVA) was carried out to study the effect of process parameters on response variables and models have also been developed for response parameters. RSM was used to determine the optimal values of input process parameters maximum MRR and minimum kerf. The output of the RSM model was used to develop the ANN predictive model. ANN model was validated through experimentation conducted at the RSM optimal setting of input parameters and results show that ANN predictive model and the actual experimental observations are very close to each other which give a good agreement between the two. Comparisons of ANN models and RSM models show that ANN predictions are more accurate than RSM predictions.

Online publication date: Wed, 13-Jul-2016

 

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