Application of Taguchi and grey-fuzzy model to optimise the machining parameters of nanocrystalline structured chips production in high carbon steel

Abstract

Nanocrystalline materials is an area of interest for researchers all over the world due to its superior mechanical properties, however the production cost of nano crystals are higher due to the complexity and cost involved during its production. This paper focuses on the application of Taguchi method with grey-fuzzy model for optimising the machining parameters of nano-crystalline structured chips production in high carbon steel (HCS) through machining. To continuously improve the machining parameters capability Taguchi-based methodologies are proposed under the consideration of multiple responses performance characteristics. An orthogonal array, multi-response performance index, signals to noise ratio, grey fuzzy grade (GFG) and analysis of variance (ANOVA) 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 optimised with considerations of the multi-response performance characteristics. Using the Taguchi and grey fuzzy method optimum cutting conditions are identified in order to obtain the smallest nanocrystalline structure via machining. Optimising a multi-response problem by the Taguchi method involves the engineer's judgement which tends to increase the degree of uncertainty.