Parametric modelling on prediction of surface finish in turning of difficult-to-machine steels

M. Anthony Xavior

International Journal of Service and Computing Oriented Manufacturing, 2013, vol. 1, issue 1, 61-80

Abstract: Parametric modelling based on multiple regression analysis (MRA), artificial neural networks (ANN) and case-based reasoning (CBR) is developed to predict surface finish during the turning process. Experiments are conducted on difficult-to-machine steels such as AISI 504, AISI D2 and AISI 52100 under different machining conditions with cutting tools viz., multicoated carbide, cermet and alumina inserts. The influence of each input (machining) parameter on surface finish obtained on the workpiece has been determined using analysis of variance (ANOVA) technique. 114 experimental data sets are used for developing the parametric models. 20 sets of validation experiments are conducted in order to evaluate the performance of the developed models. The models are compared based on certain quantitative (statistical measures) and qualitative aspects. It is concluded that CBR model outperformed the other two models in predicting surface finish for the machining conditions considered to a reasonable accuracy.

Keywords: parametric modelling; multiple regression analysis; MRA; artificial neural networks; ANNs; case-based reasoning; CBR; surface finish; turning; difficult-to-machine steels; analysis of variance; ANOVA. (search for similar items in EconPapers)
Date: 2013
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=52228 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijscom:v:1:y:2013:i:1:p:61-80

Access Statistics for this article

More articles in International Journal of Service and Computing Oriented Manufacturing from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().