Modelling and optimization of the surface roughness in the dry turning of the cold rolled alloyed steel using regression analysis

surface quality of the machined parts is one of the most important product quality indicators and one of the most frequent customer requirements. the average surface roughness (ra) represents a measure of the surface quality, and it is mostly influenced by the following cutting parameters: the cutting speed, the feed rate, and the depth of cut. quantifying the relationship between surface roughness and cutting parameters is a very important task. in this study regression analysis was used for modelling and optimization of the surface roughness in dry single-point turning of the alloyed steel, using coated tungsten carbide inserts. the experiment has been designed and carried out on the basis of a three-level full factorial design. the linear, the quadratic and the power (non-linear) mathematical models were selected for the analysis. obtained results are in good accordance with the experimentally obtained data, confirming the effectiveness of regression analysis in modelling and optimization of surface roughness in the turning process. the general conclusion is that the surface roughness has a clear downward trend with the cutting speed increase and decrease in the feed rate and the depth of cut.