Hybrid knowledge model of process planning and its green extension

Qi Lei (), Hong Wang () and Yuchuan Song ()
Additional contact information
Qi Lei: Chongqing University
Hong Wang: Chongqing University
Yuchuan Song: Chongqing University

Journal of Intelligent Manufacturing, 2016, vol. 27, issue 5, No 5, 975-990

Abstract: Abstract The green process planning model was a necessary research field of the green manufacturing, which has drawn increasing attention from many scholars. This study proposes a multi-method [Backus–Naur Form (BNF) frame, binary tree,production rules, and objective-oriented methodology] hybrid frame model of process planning and reasoning mechanism. In this model, the hierarchical BNF frame was applied to modeling the structure of parts, the stages of process decisions and the existing green process indicators set. Then, two “procedure” programs were designed for the information exchange among the above models. This green-process planning model was proposed based on the traditional intelligent process planning model and was intended to introduce an overall (compared with the traditional partial green-process planning model) green-process decision mode. In the last section of this paper, a case study of the green-process planning for a stepped shaft is provided along with a number of essential knowledge models to illustrate the feasibility of this hybrid knowledge model.

Keywords: Green process; Process planning; Knowledge model; Modeling (search for similar items in EconPapers)
Date: 2016
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-014-0928-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:joinma:v:27:y:2016:i:5:d:10.1007_s10845-014-0928-1

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-014-0928-1

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().