Multi-Objective Optimization of Cutting Parameters in Hard Turning Using the NSGA-II Algorithm

Aslain Brisco Ngnassi Djami (), Martin Ndibi Mbozo’O, Joseph Nkongho Anyi, Wolfgang Nzié and Guy Edgar Ntamack
Additional contact information
Aslain Brisco Ngnassi Djami: University of Ngaoundere
Martin Ndibi Mbozo’O: National School of Agro-Industrial Sciences, University of Ngaoundere
Joseph Nkongho Anyi: University of Douala
Wolfgang Nzié: University of Ngaoundere
Guy Edgar Ntamack: University of Ngaoundere

SN Operations Research Forum, 2024, vol. 5, issue 4, 1-17

Abstract: Abstract Cutting tool machining occupies an important place among various material transformation techniques. It facilitates producing mechanical parts of complex shapes with very tight tolerances. However, the machining of hard materials suffers from the lack of knowledge on the physical phenomena involved during cutting. This paper describes a code based on the NSGA-II (non-dominated sorting genetic algorithm II) in order to choose optimal cutting parameter values. The objective functions expressing the three components of the cutting force are obtained by modeling experimental cutting data by multiple linear regression. The case study made it possible to prove the effectiveness of the described model. The result obtained is a set of optimal solutions (Pareto front) which offers the user many degrees of freedom and readability for the choice of a solution even more personalized to the user needs. The quality of the solutions obtained showcases possibilities of industrial applications.

Keywords: Multi-objective optimization; Pareto front; Hard turning; Genetic algorithm; NSGA-II (non-dominated sorting genetic algorithm II) (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s43069-024-00364-2 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:snopef:v:5:y:2024:i:4:d:10.1007_s43069-024-00364-2

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/43069

DOI: 10.1007/s43069-024-00364-2

Access Statistics for this article

SN Operations Research Forum is currently edited by Marco Lübbecke

More articles in SN Operations Research Forum from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().