A combinatorial optimisation approach for recognising interacting machining features in mill-turn parts

Wenbo Wu, Zhengdong Huang, Qinghua Liu and Lianhua Liu

International Journal of Production Research, 2018, vol. 56, issue 11, 3757-3780

Abstract: Feature interactions may result in many process alternatives in part machining. Traditional process planning methods only identify one of the options, which is usually not optimal in the sense of engineering. This paper presents an optimisation approach to handle the interacting feature recognition problem in mill-turn parts. The approach subdivides the material removal volume into cells first and then it combines the cells into features. Here, a two-level cell combination method is developed. On the lower level, individual features are formed by searching the combinations of cells near a given part face; on the upper level, the feature distributions are explored by rearranging the order of part faces for feature formation. In order to optimise the feature distribution, a novel optimisation model is proposed, which quantitatively distinguishes its options by considering the factors of feature numbers, tool approaching directions, cutting directions and surface roughness. The combinatorial optimisation problem is solved with the simulated annealing algorithm. Instead of searching cell combinations directly, the proposed method explores different part face sequences, which drastically reduces the search space. The case studies show that the proposed approach can effectively handle the traditional difficulty in recognising the interacting features for mill-turn parts.

Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207543.2018.1425016 (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:56:y:2018:i:11:p:3757-3780

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

DOI: 10.1080/00207543.2018.1425016

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 ().