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Application of response surface methodology and fuzzy logic based system for determining metal cutting temperature

Tanikić D., Marinković V., Manić M., Devedžić G., Ranđelović S.

Bulletin of the Polish Academy of Sciences. Technical Sciences

2016

Vol. 64, nr 2

435--445

The heat produced in metal cutting process has negative influence on the cutting tool and the machined part in many aspects. This paper deals with measurement of cutting temperature during single-point dry machining of the AISI 4140 steel, using an infrared camera. Various combinations of cutting parameters, i.e. cutting speed, feed rate and depth of cut lead to different values of the measured cutting temperature. Analysis of the measured data should explain the trends in temperature changes depending on changes in the cutting regimes. Furthermore, the temperature data is modelled using response surface methodology and fuzzy logic. The models obtained should determine the influence of cutting regimes on cutting temperature. The main objective is the reduction of cutting temperature, i.e. enabling metal cutting process in optimum conditions.

Determination of the Cutting Forces in a Face Milling Operation Using the Artificial Neural Networks

Manić M., Tanikić D., Nikolić V.

Machine Dynamics Problems

2005

Vol. 29, No. 3

51--58

The magnitude of the cutting force, which occurs during the face milling process, depends on many factors such as the cutting depth, the velocity of accessory motion, the number of revolutions of the main spindle, the feed rate, type of material of the cutting tool and the machined part etc. Anyway, calculating this force is not a simple problem and that is the reason why, in some cases, it is expedient to use empirical forms or interpolation formulas obtained by processing of measuring results in laboratory conditions. In order to calculate cutting force, the results have been processed in two ways: using a multifactorial experiment and using artificial neural networks. Results of this work show that an artificial neural network can be very useful in the cutting force prediction.