Proceedings of SerbiaTrib '13

From the analysis of the diagram it can beconcluded that the output values of both types ofmodel correspond to experimental values. Meanrelative error of predicted values for forces F c , F fand F p in the model based on ANN is 1.01%,2.24% and 1.71% respectively, while for regressionmodel error is 1.85%, 3.55% and 2.92%respectively.Figure 20. Output values of the main cutting force fromregression model and ANN modelFigure 21. Output values of the feed cutting force fromregression model and ANN modelFigures 20, 21 and 22 give the measured andpredicted values for all cutting forces respectively.The results of predicting with the model based onANN show that the developed models can be usedfor modelling the cutting force, although the set oflearning, validation and testing include a relativelysmall number of combinations of input and outputvalues. In order to analyse the accuracy of themultiple regression model the output values of themodel are also shown on the same chart (Table 7).Figure 23. Modelled curves of tool wear for differentlubrication technique in machining steel C45EModelling of tool wear was performed with athird order polynomial function. It can beconcluded from Fig. 23 that matching of outputvalues of model with the experimental values isexcellent.The parameters of the surface roughness R a andR y were modelled with linear function dependingon the machining times and depending on the toolwear using regression analysis.Table 8. Models of surface roughness in dependence oftool wear for different lubrication techniquesMaterial: C45E; a = 2.0 mm; vc = 320 m/minTool: SNMG 1204 08 NXMConv.MQLR y = 15.15 · VB + 1.45 + (f 2 · 10 3 / (8 · r))R y = 30.50 · VB + 1.18 + (f 2 · 10 3 / (8 · r))Figure 22. Output values of the penetration cutting forcefrom regression model and ANN model13 th International Conference on Tribology – Serbiatrib’13 299