Modeling and multi-response optimization of machining performance while turning hardened steel with self-propelled rotary tool

Abstract

There are many advanced tooling approaches in metal cutting to enhance the cutting tool performance for machining hard-to-cut materials. The self propelled rotary tool (SPRT) is one of the novel approaches to improve the cutting tool performance by providing cutting edge in the form of a disk, which rotates about its principal axis and provides a rest period for the cutting edge to cool and allow engaging a fresh cutting edge with the work piece. This paper aimed to present the cutting performance of SPRT while turning hardened EN24 steel and optimize the machining conditions. Surface roughness (R _a) and metal removal rate (r _MMR) are considered as machining performance parameters to evaluate, while the horizontal inclination angle of the SPRT, depth of cut, feed rate and spindle speed are considered as process variables. Initially, design of experiments (DOEs) is employed to minimize the number of experiments. For each set of chosen process variables, the machining experiments are conducted on computer numerical control (CNC) lathe to measure the machining responses. Then, the response surface methodology (RSM) is used to establish quantitative relationships for the output responses in terms of the input variables. Analysis of variance (ANOVA) is used to check the adequacy of the model. The influence of input variables on the output responses is also determined. Consequently, these models are formulated as a multi-response optimization problem to minimize the R _a and maximize the r _MMR simultaneously. Non-dominated sorting genetic algorithm-II (NSGA-II) is used to derive the set of Pareto-optimal solutions. The optimal results obtained through the proposed methodology are also compared with the results of validation experimental runs and good correlation is found between them.

Keywords

Self-propelled rotary turning / Empirical modeling / Response surface methodology (RSM) / Multi-objective formulation / Optimization / Non-dominated sorting genetic algorithm-II (NSGA-II)