Multi-objective optimization of tool nose radius and machining conditions employing Taguchi-based grey relational analysis in milling of AISI 304

The main aim of current work was to reach an optimum order of parameters, namely tool nose radius, feed, cutting speed and axial depth of cut providing minimum tool wear, resultant force and surface roughness while milling of AISI 304 steel. Therefore, the present study has four steps: experiments, modelling, mono- and multi-objective optimization. At the first step (in experiments), tests were conducted by Taguchi experimental design. The influences of nose radius, feed, speed and depth on the force, surface roughness and wear were determined. In the second step (modelling), experimental results were employed to establish first-order models. In the third step (mono-objective optimization), force, surface roughness and wear were utilized solely. To minimize outputs, Taguchi's signal-to-noise ratio was employed. The influence of inputs on responses was found with variance analysis. From mono-objective optimization, it was explored that the optimum settings for minimizing wear were nose radius of 0.4 mm, speed of 100 m/min, feed of 0.20 mm/tooth and depth of 1.8 mm. The optimum setting of milling factors for resultant force was nose radius of 0.4 mm, speed of 100 m/min, feed of 0.20 mm/tooth and depth of 1.2 mm. For the lowest surface roughness, the optimum combinations were nose radius of 1.2 mm, speed of 100 m/min, feed of 0.20 mm/tooth and depth of 1.8 mm. In the fourth step (multi-objective optimization), outputs were simultaneously optimized with grey relational analysis. From multi-objective optimization, the best orders for minimizing the resultant force, surface roughness and tool wear were nose radius of 0.4 mm, feed of 0.20 mm/tooth, speed of 100 m/min and depth of 1.5 mm.