EconPapers    
Economics at your fingertips  
 

Selection of optimal conditions in the surface grinding process using the quantum based optimisation method

Mahdi S. Alajmi (), Fawzan S. Alfares and Mohamed S. Alfares
Additional contact information
Mahdi S. Alajmi: Public Authority for Applied Education and Training
Fawzan S. Alfares: Public Authority for Applied Education and Training
Mohamed S. Alfares: Public Authority for Applied Education and Training

Journal of Intelligent Manufacturing, 2019, vol. 30, issue 3, No 31, 1469-1481

Abstract: Abstract A novel optimisation technique based on quantum computing principles, namely the quantum based optimisation method (QBOM), is proposed to solve the surface grinding process problem optimisation. In grinding process there is a trade-off between faster material removal rates, with a reduction in cutting time and its associated cost and shorter tool life or higher tool cost. The objective of the surface grinding optimisation problem is to determine the optimal machining conditions, which will minimize the unit production cost and unit production time with the finest possible surface finish but without violating any imposed constraints. The performance of QBOM is investigated against two test cases, one of a rough grinding process and the other of a finished grinding process and the computational results show that the proposed optimisation technique obtained better results than most of the methods presented in the literatures.

Keywords: Optimisation; Surface grinding; Manufacturing; Evolution algorithm; Quantum based optimisation method (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-017-1326-2 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:30:y:2019:i:3:d:10.1007_s10845-017-1326-2

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

DOI: 10.1007/s10845-017-1326-2

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 ().

 
Page updated 2025-03-20
Handle: RePEc:spr:joinma:v:30:y:2019:i:3:d:10.1007_s10845-017-1326-2