Modeling of the Cutting Forces in Turning Process Using Various Methods of Cooling and Lubricating: An Artificial Intelligence Approach

Abstract

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.

Modelling, prediction and analysis of surface roughness in turning process with carbide tool when cutting steel C38 using artificial neural network

“…The increase in R a values with cutting speed (v = 280 m/min) is due to the possible tool wear caused by higher cutting speeds as mentioned by Ekiċi̇ et al (2014). Finally, greater f values gives big R a values, since increased f leads to increased thrust force involving more vibrations and hence, altering surface finish as was reported also by Davim et al (2008) and Cica et al (2013).…”

“…To reduce machining costs and to obtain required surface quality of the machined parts, much effort has been developed in understanding the effects of cutting conditions on R a through the creation of adequate models (Davim et al, 2008). The most frequently used models for prediction of machining performance are mathematical modelling, the regression technique and artificial intelligence (AI) technique (Cica et al, 2013). Recently, (AI)-based models, such as ANN approaches, have become the preferred trend as they are applied by most researchers to develop optimal machining conditions to predict performance measure (Hossain and Ahmad, 2014).…”

Analysis of Experimental Values Obtained at Different Cutting Parameters and MQL Flows with S/N Ratios and ANN

“…Günümüz endüstrisinde talaşlı imalat uygulamalarında önemli bir yer tutan tornalama operasyonlarında, işleme ve kesme parametrelerinin optimizasyonu, araştırmacıların odaklandığı başlıca konulardan birisidir. Bu nedenle Fc ve Ra sonuçlarının istatiksel analizi, modellenmesi ve optimizasyonu için Taguchi metodu ve YSA kullanılması çok sayıda araştırmaya konu olmuştur [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20].…”

Modeling of Machining Force in Hard Turning Process

“…Many parameters influence greatly the machining forces, so it is quite difficult to develop a theoretical model to describe efficiently the cutting process. The problem of modeling or predicting machining forces has been investigated by many researchers [1][2][3][4][5][6][7][8][9][10][11][12].…”