A novel approach to predicting surface roughness based on specific cutting energy consumption when slot milling Al-7075

Surface roughness is often considered as one of the most important technical requirements for a machined part in production. Traditional selection of conservative process parameters neither guarantees part surface quality nor attains high metal removal rates. An accurate prediction and effective control of surface roughness is of vital importance for improving machining efficiency and reducing machining cost. In this paper, a novel model is presented for predicting surface roughness when slot milling Al-7075. The model is developed using a hybrid approach combining analytical calculation of specific cutting energy consumption (SCEC) and empirical characterization of the relationship between the surface roughness and SCEC. The proposed model has been validated by experiments under various cutting conditions. Compared to Taguchi methodology for predicting surface roughness, the proposed model is more accurate and reliable. From a novel viewpoint of SCEC, the model may provide valuable information regarding the effects of the cutting parameters on the surface roughness. Besides, the implementation of this model is straightforward, thus providing great potential for surface roughness control in real production.