Single and multiple quality characteristics optimization, expanded to the machinability assessment at the optimal cutting combinations across Taguchi OA, GRA and BBD: an overall view

Abstract

Abstract The Inconel 718 is among the most used alloy in several industrial fields, so it was necessary to develop appropriate modeling, optimization, and prediction methods during the turning of this material; in terms of minimum experiments for giving good competitiveness to final products. In this research paper, turning experiments have been performed under a dry environment with different conditions of cutting speed, feed rate, and depth of cut, using Taguchi design along with Grey Relational Analysis and Box-Behnken design when turning of Inconel 718. Initially, design evaluation is achieved based on their fraction of design space graphs. Then, optimization single and multi-objective are satisfied, and a general comparison of short and long-term is achieved apropos optimal cutting regimes. Mono-objective optimization through Taguchi orthogonal array shows the carelessness of responses outside the optimization object. On the other hand, multi-objective optimization relating to Taguchi-Grey produces better results regarding its reduced number of experiments. The Box-Behnken design implements a good fit for multi-attribute optimization. Nevertheless, its higher number of experiments must be considered regarding their costs. Moreover, a comparative study for the short and long term can demonstrate a suitable and accurate approach regarding its optimal cutting combination. The Grey-Taguchi approach displays good results in terms of multi-attribute optimization against the Box-Behnken design. This judgment is based on the balancing of the obtained accuracy regarding the smallest design. However, the tool wear long-term test is integrated as a criterion of better machinability relating to the optimal cutting combinations.