In making products, regarding the aspect of the quality of machining, a special attention is devoted to their dimensional accuracy and the quality of machined surfaces, hence the measurement of both roughness parameters and accuracy parameters is especially important. The accuracy of machining comprises a precision of measures, accuracy of shapes of surfaces and accuracy of mutual relationships of two or more surfaces, while the quality of a machined surface is most often determined through a surface roughness. Since the quality of machining, besides the accuracy of measures, is completely determined by the values of parameters of the quality of a surface roughness and the parameters of form and location, this paper is oriented to the establishment of a model between parameters of quality of machined surface and parameters of form and position deviations. By the development of models based on neural networks by using experimental results, it is possible to analyse the quality of machining on the basis of the parameters of a surface roughness.
Krivokapić, Z., et al , give the application of a multiple regression and artificial neural networks (ANNs), and this paper describes the development of models for the predicting a surface roughness, linking an arithmetic mean deviation of a surface roughness to a torque as an input variable, in the process of drilling.
Paper Vučurević, R., et al  presents the models for prediction of an arithmetic mean deviation of a surface roughness using artificial neural networks, developed on the basis of parameters of drilling process and an axial force obtained for different values of blunting twist drills, based on the experimental results by using Taguchi design of experiment.
Peper Vučurević, R., et al  presents a comparative analysis of the models for the predicting a surface roughness developed by the application of a multiple regression and artificial neural networks. These models were developed using experimental data for an arithmetical mean deviation of a surface roughness and the axial cutting force obtained by implementing the Taguchi design of experiment.
Paper by Spaić, O., at al  presents the prediction of a tool condition by applying a family of artificial neural networks.
The book  and papers [6-12] aims at presenting the various methodologies and practices that are being employed for the prediction of surface roughness.
Çiçek, A., Kivak, T. and Samtaş, G. , by the application of Taguchi design of experiment, have found an optimal combination of the process parameters from the aspect of a surface roughness, while drilling an austenite stainless AISI 316 by twist drills of high-speed steel (HSS), in conventionally and cryogenically processes, varying a feed f [mm/o] and a cutting speed v [m/min] on two levels.
Raghunandan, B.V., Bhandarkar, S.L. and Pankaj, K.S.  also used a regression analysis to obtain a surface roughness model, while machining EN-19 material by turning with cemented carbide inserts.
Kumar, P.J. and Packiaraj, P.  used Tacuchi design of experiment, regression analysis and the analysis of a variance for the purposes of the researching the impact of drilling parameters such as a cutting speed v [m/min], feed f [mm/o] and a diameter of a twist drill d [mm], on a surface roughness and deviation of a hole diameter from rated values for drilling OHNS material, tool steel that is widely used in the production of tools, by twist drills made of high-speed steel (HSS).
Xu, Y., et al  present a very interesting paper related to the application of a back propagation wavelet neural network based prediction of a drill wear from thrust force and cutting torque signals.