Evaluation of parametric models in predicting the machining performance
by M. Anthony Xavior; M. Adithan
International Journal of Industrial and Systems Engineering (IJISE), Vol. 11, No. 4, 2012

Abstract: The performance of the machining (turning) process is evaluated in terms of tool life, surface roughness, tool-shim interface temperature developed and metal removal rate during the process. It is very important for the manufacturing engineers to know the performance of the turning process for a set of cutting (input) parameters. In this paper, parametric models based on multiple regression analysis (MRA), neural networks (NNs) and case-based reasoning (CBR) are developed for predicting the machining performance, i.e. the output parameters. An experimental database containing 114 data sets are used for developing the three models. Each data set contains nine input and four output parameters. About 20 machining trials are exclusively conducted with various combinations of input parameters, and their corresponding output values are compared with the predicted values of the developed models. Descriptive statistics of the errors are calculated for the three models and it was found that the CBR model provided better prediction capability than MRA and NN models.

Online publication date: Sat, 20-Dec-2014

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 Industrial and Systems Engineering (IJISE):
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