This paper focuses on the exploitation of the response surface methodology (RSM) to determine optimum cutting conditions leading to minimum surface roughness and cutting force components. The technique of RSM helps to create an efficient statistical model for studying the evolution of surface roughness and cutting forces according to cutting parameters: cutting speed, feed rate and depth of cut. For this purpose, turning tests of hardened steel alloy (AISI 4140) (56 HRC) were carried out using PVD – coated ceramic insert under different cutting conditions. The equations of surface roughness and cutting forces were achieved by using the experimental data and the technique of the analysis of variance (ANOVA). The obtained results are presented in terms of mean values and confidence levels. It is shown that feed rate and depth of cut are the most influential factors on surface roughness and cutting forces, respectively. In addition, it is underlined that the surface roughness is mainly related to the cutting speed, whereas depth of cut has the greatest effect on the evolution of cutting forces. The optimal machining parameters obtained in this study represent reductions about 6.88%, 3.65%, 19.05% in cutting force components (Fa, Fr, Ft), respectively. The latters are compared with the results of initial cutting parameters for machining AISI 4140 steel in the hard turning process.

Surface roughness and cutting forces are considered as important factors to determine machinability rate and the quality of product. A number of factors like cutting speed, feed rate, depth of cutting and tool noise radius influence the surface roughness and cutting forces in turning process. In this paper, an Artificial Neural Network (ANN) model was used to forecast surface roughness and cutting forces with related inputs, including cutting speed, feed rate, depth of cut and tool noise radius. The machined surface roughness and cutting force parameters related to input parameters are the outputs of the ANN model. In this work, 24 samples of experimental data were used to train the network. Moreover, eight other experimental tests were implemented to test the network. The study concludes that ANN was a reliable and accurate method for predicting machining parameters in CNC turning operation.

In this paper a molecular dynamics simulation of nano-metric cutting of copper with a diamond tool is presented. MD simulations require the determination of the interaction of the involved atoms through a function of potential for the materials involved in the analysis and the accurate topography of the studied area, leading to high demand of computational time. The models presented are taking into account the cubic lattice of copper, test two different potential functions and at the same time control the computational cost by introducing small models at realistic cutting conditions. This is realized by a novel code developed and allows focusing on the influence of several processes and modeling parameters on the outcome of the simulations. Models with and without thermostat atoms are investigated and the influence of cutting conditions and cutting tool geometry on chip morphology, cutting forces and cutting temperatures are studied.