The aim of this paper is to conduct an experimental study in order to obtain a roughness (Ra) prediction model for dry end-milling (with an AlTiCrSiN PVD-coated tool) of the Co–28Cr–6Mo and Co–20Cr–15W–10Ni biomedical alloys, a model that can contribute to more quickly obtaining the desired surface quality and shortening the manufacturing process time. An experimental plan based on the central composite design method was adopted to determine the influence of the axial depth of cut, feed per tooth and cutting speed process parameters (input variables) on the Ra surface roughness (response variable) which was recorded after machining for both alloys. To develop the prediction models, statistical techniques were used first and three prediction equations were obtained for each alloy, the best results being achieved using response surface methodology. However, for obtaining a higher accuracy of prediction, ANN models were developed with the help of an application made in LabView for roughness (Ra) prediction. The primary results of this research consist of the Co–28Cr–6Mo and Co–20Cr–15W–10Ni prediction models and the developed application. The modeling results show that the ANN model can predict the surface roughness with high accuracy for the considered Co–Cr alloys.

This paper presents a research conducted in order to identify the cutting parameters effect on turning cutting forces and on the resulted machined surface quality for a CoCrWNi alloy. This alloy is a biomaterial used in medical applications for implants manufacturing. The main objective of the research is the development of prediction models for the turning cutting forces and the Ra roughness parameter for dry longitudinal turning with TiAlN PVD coated inserts. In order to achieve this objective, thirteen processing experiments were carried out, during which the cutting forces and roughness parameters were registered. The research results consist of the prediction models for cutting forces and Ra roughness parameter.

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