Int. J. of Industrial and Systems Engineering   »   2012 Vol.11, No.4

 

 

Title: Evaluation of parametric models in predicting the machining performance

 

Authors: M. Anthony Xavior; M. Adithan

 

Addresses:
School of Mechanical and Building Sciences, VIT University, Vellore 632014, Tamil Nadu, India
School of Mechanical and Building Sciences, VIT University, Vellore 632014, Tamil Nadu, India

 

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.

 

Keywords: turning; performance prediction; MRA; multiple regression analysis; NNs; neural networks; CBR; case-based reasoning; parametric modelling; machining performance; tool life; surface roughness; tool-shim interface temperature; MRR; metal removal rate.

 

DOI: 10.1504/IJISE.2012.047544

 

Int. J. of Industrial and Systems Engineering, 2012 Vol.11, No.4, pp.406 - 427

 

Available online: 27 Jun 2012

 

 

Editors Full text accessAccess for SubscribersPurchase this articleComment on this article