Helical milling is a hole-making process which has been applied in hardened materials. Due to the difficulties on achieving high-quality boreholes in these materials, the influence of noise factors, and multi-quality performance outcomes, this work aims the multi-objective robust design of hole quality on AISI H13 hardened steel. Experiments were carried out through a central composite design considering process and noise factors. The process factors were the axial and tangential feed per tooth of the helix, and the cutting velocity. The noise factors considered were the tool overhang length, the material hardness and the borehole height of measurement. Response models were obtained through response surface methodology for roughness and roundness outcomes. The models presented good explanation of data variability and good prediction capability. Mean and variance models were derived through robust parameter design for all responses. Similarity analysis through cluster analysis was realised, and average surface roughness and total roundness were selected to multi-objective optimisation. Mean square error optimisation was performed to achieve bias and variance minimization. Multi-objective optimisation through normalized normal constraint was performed to achieve a robust Pareto set for the hole quality outcomes. The normalized normal constraint optimisation results outperformed the results of other methods in terms of evenness of the Pareto solutions and number of Pareto optimal solutions. The most compromise solution was selected considering the lowest Euclidian distance to the utopia point in the normalized space. Individual and moving range control charts were used to confirm the robustness achievement with regard to noise factors in the most compromise Pareto optimal solution. The methodology applied for robust modelling and optimisation of helical milling of AISI H13 hardened steel was confirmed and may be applied to other manufacturing processes. KeywordsHelical milling; AISI H13 hardened steel; Multi-objective robust optimization; Robust parameter design; Normalized normal constraint method. R 2 coefficient of determinationRadj 2 adjusted coefficient of determination Radj 2 prediction coefficient of determination