EconPapers    
Economics at your fingertips  
 

A twin data and knowledge-driven intelligent process planning framework of aviation parts

Jingjing Li, Guanghui Zhou and Chao Zhang

International Journal of Production Research, 2022, vol. 60, issue 17, 5217-5234

Abstract: As the core link of intelligent manufacturing, the process planning of aviation parts still faces the challenges such as relying on manual experiences for process decision-making and lack of linkage between process design and manufacturing for process optimisation. Process knowledge could support scientific decision-making on process issues, while twin data, namely high-fidelity simulation data and feedback information of manufacturing site, could further verify the process plans and optimise process parameters, so as to continuously improve the quality of process plans. Consequently, this paper proposes a general framework for twin data and knowledge-driven intelligent process planning (TDKIPP) of aviation parts, and analyses four standard procedures that support the above-mentioned reference framework, namely mechanism-data fusion process digital twin model, dynamic process knowledge base, process decision-making and evaluation, machining quality prediction and process feedback optimisation. A thus constructed test bed of TDKIPP and its four application examples about the process planning of a micro turbojet engine integral impeller demonstrate the feasibility and effectiveness of the proposed approach.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2021.1951869 (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:60:y:2022:i:17:p:5217-5234

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

DOI: 10.1080/00207543.2021.1951869

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 ().

 
Page updated 2025-03-20
Handle: RePEc:taf:tprsxx:v:60:y:2022:i:17:p:5217-5234