Optimization of Correlated and Conflicting Responses of ECM Process Using Flower Pollination Algorithm

Bappa Acherjee, Debanjan Maity and Arunanshu S. Kuar
Additional contact information
Bappa Acherjee: Department of Production Engineering, Birla Institute of Technology Mesra, Ranchi, India
Debanjan Maity: Department of Mechanical Engineering, IIT Kharagpur, India
Arunanshu S. Kuar: Department of Production Engineering, Jadavpur University, Kolkata, India

International Journal of Applied Metaheuristic Computing (IJAMC), 2020, vol. 11, issue 4, 1-15

Abstract: The electrochemical machining (ECM) process has been investigated in this article to achieve the desired process performances by optimizing the machining parameters using the flower pollination algorithm (FPA). Two major process performances namely: material removal rate (MRR) and surface roughness (Ra), which are correlated and conflicting in nature, are optimized with respect to the key process parameters. The regression equations developed by using experimental data are used as objective functions in the flower pollination algorithm. Objectives are set to find the optimal set of process parameters to fulfil a single objective as well as multiple objectives. The performance of the algorithm is checked in terms of accuracy, convergence speed, number of optimized populations, and computational time. The mean values of functional evaluations for MRR and Ra obtained are close to their respective optimal results, which ensures the quality of the convergence. It is further seen that FPA can predict the true overall parametric trends as it does not require keeping any parameter as constant during the analysis.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJAMC.2020100101 (application/pdf)

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:igg:jamc00:v:11:y:2020:i:4:p:1-15

Access Statistics for this article

International Journal of Applied Metaheuristic Computing (IJAMC) is currently edited by Peng-Yeng Yin

More articles in International Journal of Applied Metaheuristic Computing (IJAMC) from IGI Global
Bibliographic data for series maintained by Journal Editor ().