Robust optimisation of surface roughness of AISI H13 hardened steel in the finishing milling using ball nose end mills.

The manufacturing of moulds and dies requires smooth surfaces and high-quality. This way, the surface roughness is an essential parameter in finishing milling to improve the quality of injected products, such as cell phones, laptops, aerospace and automotive parts which very much depend on the finishing of the moulds. Thus, this paper aims to robust optimisation of surface roughness of AISI H13 hardened steel in the finishing milling using ball nose end mills, by using a combination of methodologies such as robust parameter design (RPD), response surface methodology (RSM), normal boundary intersection (NBI), and mean square error (MSE). Based on this, eight input factors were used in the experimental tests, being four control factors (feed per tooth, axial depth of cut, radial depth of cut, and cutting speed) and four noise factors (tool overhang length, workpiece tilt angle, tool flank wear, and cutting strategy). The surface roughness evaluated in Ra pattern was measured in two cutting directions: in tool feed rate direction (Ra_f z) and in radial depth of cut direction (Ra_a e). The results showed that it was possible to generate a convex and equispaced Pareto frontier to MSE functions of surface roughness Ra and that the achieved optimum decrease the sensitivity of the milling process using ball nose end mills to the variability transmitted by the noise factors. • This paper presented a methodology to the bi-objective robust optimisation of surface roughness obtained by finishing milling of AISI H13 hardened steel. Since the mould and die market is becoming more demanding and competitive, the results achieved with this methodology are essential to attain a competitive advantage in the mould and die industry. • This experimental design and data collection will show moulds and dies manufacturers in the field of mechanical engineering design the relationship and effect between the cutting tool, the workpiece surface roughness and the machining parameters. • This unique multivariate experimental design considers eight factors that potentially influence the surface roughness of AISI H13 hardened steel in the finishing milling using ball nose end mills. • The obtained results show that it was possible to generate a convex and equispaced Pareto frontier to MSE functions of surface roughness Ra and that the achieved optimum lessens the sensitivity of the milling process using ball nose end mills to the variability transmitted by the noise factors. • The RPD-NBI-MSE methodology used in this paper may be applied in other manufacturing processes as a bi-objective robust modelling and optimisation procedure. [ABSTRACT FROM AUTHOR]