Parametric optimization for the production of nanostructure in high carbon steel chips via machining

Abstract

Nano crystalline materials are an area of interest for the researchers all over the world due to its superior mechanical properties such as high strength and high hardness. But the cost of nano-crystals is high because of the complexity and cost incurred during its production. This paper focuses on the application of Taguchi method with Fuzzy logic for optimizing the machining parameters of nano-crystalline structured chips production in High Carbon Steel (HCS) through machining. An orthogonal array, multi-response performance index, signals to noise ratio and analysis of variance are used to study the machining process with multi-response performance characteristics. The machining parameters namely rake angle, depth of cut, heat treatment, feed and cutting velocity are optimized with considerations of the multi-response performance characteristics. Using the Taguchi and Fuzzy logic method optimum cutting conditions are identified in order to obtain the smallest nanocrystalline structure via machining. Ó 2015 Faculty of Engineering, Ain Shams University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Application of Taguchi Method and Response Surface Methodology on Machining Parameters of Al MMCs 6063-TiO2

“…Dynamic experiments are a straightforward, systematic, and more effective way of determining the ideal machining settings. To produce nanocrystalline structured chips from High Carbon Steel (HCS), Ilangkumaran M. et al [25] examined the impact of machining settings on the machining parameters. The machining process with multi-response performance characteristics is studied using an orthogonal array, multi-response performance index, signals-to-noise ratio, and analysis of variance.…”