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Title: Optimum process parameters for efficient and quality thin wall machining using firefly algorithm

Authors: Akash Dutta; Argha Das; Shrikrishna N. Joshi

Addresses: Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati, 781039, Assam, India ' Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati, 781039, Assam, India ' Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati, 781039, Assam, India

Abstract: The manufacturing requirements of the aerospace industry makes it imperative to use thin wall machining techniques to machine parts that would otherwise have to be assembled from a number of parts. To achieve high productivity, there must be increase in material removal rate, which is constrained by the geometrical accuracy and surface finish requirements. Thus, a compromise must be made between productivity and product quality. This paper presents an optimisation scheme to improve the productivity while keeping the surface finish within acceptable limits during thin-wall machining operations. Initially full factorial experiments were carried out on machining of closed thin walled pocket by varying feed, cutting speed and tool diameter. Surface roughness and material removal values for all experiments were recorded. Analysis of variance was carried out to find out the most significant process parameter. Later firefly algorithm, a nature inspired swarm optimisation technique was employed to obtain the optimum process parameters for desired performance. A confirmation experiment was carried out which indicates an error of 1.27% and 1.03% between predicted and experimental results of surface roughness and material removal rate respectively.

Keywords: thin wall machining; surface roughness; material removal rate; MRR; analysis of variance; ANOVA; interaction; optimisation; firefly algorithm; process parameters; aerospace industry; surface quality; productivity improvement; feed rate; cutting speed; tool diameter; swarm intelligence; metaheuristics.

DOI: 10.1504/IJASMM.2017.10003951

International Journal of Additive and Subtractive Materials Manufacturing, 2017 Vol.1 No.1, pp.3 - 22

Received: 24 Jul 2015
Accepted: 24 Mar 2016

Published online: 17 Mar 2017 *

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