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Modeling of the Cutting Forces in Turning Process Using Various Methods of Cooling and Lubricating: An Artificial Intelligence Approach
Author(s) -
Djordje Cica,
Branislav Sredanović,
Gordana Lakic-Globocki,
Davorin Kramar
Publication year - 2013
Publication title -
advances in mechanical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.318
H-Index - 40
eISSN - 1687-8140
pISSN - 1687-8132
DOI - 10.1155/2013/798597
Subject(s) - machining , artificial neural network , lubrication , adaptive neuro fuzzy inference system , process (computing) , mechanical engineering , focus (optics) , experimental data , soft computing , computer science , water cooling , work (physics) , fuzzy inference , fuzzy logic , engineering , artificial intelligence , fuzzy control system , mathematics , statistics , physics , optics , operating system
Cutting forces are one of the inherent phenomena and a very significant indicator of the metal cutting process. The work presented in this paper is an investigation of the prediction of these parameters in turning using soft computing techniques. During the experimental research focus is placed on the application of various methods of cooling and lubricating of the cutting zone. On this occasion were used the conventional method of cooling and lubricating, high pressure jet assisted machining, and minimal quantity lubrication technique. The data obtained by experiment are used to create two different models, namely, artificial neural network and adaptive networks based fuzzy inference systems for prediction of cutting forces. Furthermore, both models are compared with the experimental data and results are indicated

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