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Multi-Objective Particle Swarm Optimization of Machining Parameters for End Milling Titanium Alloy Ti-6AL-4V

[+] Author Affiliations
Durul Ulutan, Abram Pleta, Laine Mears

Clemson University, Greenville, SC

Paper No. MSEC2014-4145, pp. V001T04A041; 6 pages
  • ASME 2014 International Manufacturing Science and Engineering Conference collocated with the JSME 2014 International Conference on Materials and Processing and the 42nd North American Manufacturing Research Conference
  • Volume 1: Materials; Micro and Nano Technologies; Properties, Applications and Systems; Sustainable Manufacturing
  • Detroit, Michigan, USA, June 9–13, 2014
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-4580-6
  • Copyright © 2014 by ASME


Titanium alloy Ti-6Al-4V is a material with superior properties such as high mechanical strength, corrosion and creep resistance, and high strength-to-weight ratio, which make it an attractive material for various industries such as automotive, aerospace, power generation, and biomedical industries. However, these superior properties as well as its low thermal conductivity and chemical reactivity make it a challenge to machine Ti-6Al-4V at optimal conditions. In order to overcome this challenge, researchers constantly develop new tools and new techniques, but the extent of machining rates that can be used efficiently with those tools and techniques are usually not clear. Considering only one variable in the process and optimizing according to that variable is not sufficient because of the interactions between parameters. Also, selecting one objective function from a pool of many is not beneficial since those objectives are in conflict with one another. Therefore, this study proposes the use of a combined optimization algorithm in order to account for three major variables in end milling of Ti-6Al-4V: cutting speed, feed, and depth of cut. These variables are optimized for multiple objectives. Although it is possible to optimize the process for many different objectives, some of them are heavily correlated to each other, hence two objectives representing machinability and efficiency are selected: tool flank wear and material removal rate. The study aims to establish an optimal Pareto front of machining parameters that would optimize the conflicting outputs of the process, utilizing the multi-objective particle swarm optimization technique.

Copyright © 2014 by ASME



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