Authors: Abderazek, Hammoudi
Laouissi, Aissa
Nouioua, Mourad
Atanasovska, Ivana 
Affiliations: Mechanics 
Mathematical Institute of the Serbian Academy of Sciences and Arts 
Title: Optimization of turning process parameters using a new hybrid evolutionary algorithm
Journal: Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science
Issue Date: 2023
Rank: ~M23
ISSN: 0954-4062
DOI: 10.1177/09544062231195472
In this article, a new hybrid improved differential evolution and Nelder-Mead (IDE-NM) is introduced for optimizing the multi-objective machining process during the turning operation under three modes of lubrication conditions. The fitness functions are the tangential cutting force, the surface roughness, and the cutting power. Five mixed design variables are considered in the optimization procedure including the cutting speed, feed rate, and depth of cut, mode of lubrication, and the type of cutting material. The mathematical expressions of the three objectives are created based on experimental results and modeled using the artificial neural network (ANN). In the first step, the proposed method is examined by solving seven mechanical engineering design problems. The comparison results illustrate that the IDE-NM algorithm outperforms other state-of-the-art optimization methods considered in the literature. Moreover, for the turning operation problem, the results of IDE-NM are compared with those of four recent metaheuristics. The results show that the proposed method outperforms the four compared algorithms in terms of robustness, high success rate, and can provide effective solutions.
Keywords: differential evolution | eco-friendly machining | meta-heuristics | Multi-objective optimization | Nelder-Mead algorithm | turning
Publisher: SAGE, UK
Project: A part of the research has been supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia, Grant number: 451-03-68/2022-14/20029.

Show full item record


checked on May 20, 2024

Page view(s)

checked on May 9, 2024

Google ScholarTM




Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.