An economic machining process model using fuzzy non-linear programming and neural network

Y. H. Lee

International Journal of Production Research, 1999, vol. 37, issue 4, 835-847

Abstract: This paper describes a computer-aided approach for the optimum selection of cutting conditions for a turning process. The approach includes a fuzzy non-linear programming and neural network model. In this paper, the fuzzy non-linear programming is employed to predict optimum cutting conditions in uncertain, ill-defined and vague situations of machining operations. The fuzzy non-linear programming provides ease of implementation, flexibility and a tolerant nature to arbitrary complexity in machining operations. In addition, in this paper, the output from the fuzzy non-linear programming model is also utilized to develop a neural network model for machinability assessment.

Date: 1999
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/002075499191553 (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:taf:tprsxx:v:37:y:1999:i:4:p:835-847

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TPRS20

DOI: 10.1080/002075499191553

Access Statistics for this article

International Journal of Production Research is currently edited by Professor A. Dolgui

More articles in International Journal of Production Research from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().