Application of artificial neural network and optimization algorithms for optimizing surface roughness, tool life and cutting forces in turning operation

2013 ◽  
Vol 27 (5) ◽  
pp. 1469-1477 ◽  
Author(s):  
F. Jafarian ◽  
M. Taghipour ◽  
H. Amirabadi
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Tool Life ◽  
Cutting Forces ◽  
Optimization Algorithms ◽  
Turning Operation ◽  
Artificial Neural

scholarly journals Prediction of Cutting Force in Turning Process by Using Artificial Neural Network

2020 ◽  
Vol 16 (2) ◽  
pp. 34-46
Author(s):  
Marwa Qasim Ibraheem
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Cutting Force ◽  
Cutting Forces ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Turning Operation ◽  
Artificial Neural

        Cutting forces are important factors for determining machine serviceability and product quality. Factors such as speed feed, depth of cut and tool noise radius affect on surface roughness and cutting forces in turning operation. The artificial neural network model was used to predict cutting forces with related to inputs including cutting speed (m/min), feed rate (mm/rev), depth of cut (mm) and work piece hardness (Map). The outputs of the ANN model are the machined cutting force parameters, the neural network showed that all (outputs) of all components of the processing force cutting force FT (N), feed force FA (N) and radial force FR (N) perfect accordance with the experimental data. Twenty-five samples of experimental data were used, including nineteen to train the network. Moreover six other experimental tests were implemented to test the network. The study concludes that ANN was a dependable and precise method for predicting machining parameters in CNC turning operation.

Prediction of Surface Roughness and Coefficient of Friction Using Artificial Neural Network in Tribotesting of Bio-Lubricants

2019 ◽  
Vol 895 ◽  
pp. 52-57 ◽  
Author(s):  
Prasanna Vineeth Bharadwaj ◽  
T.P. Jeevan ◽  
P.S. Suvin ◽  
S.R. Jayaram
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Coefficient Of Friction ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Trained Neural Network ◽  
Lubrication Conditions ◽  
Good Agreement

Tribotesting is necessary to understand the behaviour of the material under various operating lubrication conditions. This paper deals with the training of an artificial neural network (ANN) model with Bio-lubricant properties and machining conditions for prediction of surface roughness and coefficient of friction in Tribotesting by Tool chip Tribometer. Experimental results obtained from Tool chip tribometer for tested bio-lubricants are compared with those obtained by ANN prediction. A good agreement in results recommends that a well trained neural network is competent enough to predict the parameters in Tribotesting process.

Prediction of machining performance using RSM and ANN models in hard turning of martensitic stainless steel AISI 420

2019 ◽  
Vol 233 (13) ◽  
pp. 4439-4462 ◽  
Author(s):  
Abderrahmen Zerti ◽  
Mohamed Athmane Yallese ◽  
Oussama Zerti ◽  
Mourad Nouioua ◽  
Riad Khettabi
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Stainless Steel ◽  
Response Surface Methodology ◽  
Martensitic Stainless Steel ◽  
Machining Parameters ◽  
Steel Aisi ◽  
Artificial Neural ◽  
Stainless Steel Aisi

The purpose of this experimental work is to study the impact of the machining parameters ( Vc, ap, and f) on the surface roughness criteria ( Ra, Rz, and Rt) as well as on the cutting force components ( Fx, Fy, and Fz), during dry turning of martensitic stainless steel (AISI 420) treated at 59 hardness Rockwell cone. The machining tests were carried out using the coated mixed ceramic cutting-insert (CC6050) according to the Taguchi design (L25). Analysis of the variance (ANOVA) as well as Pareto graphs made it possible to quantify the contributions of ( Vc, ap, and f) on the output parameters. The response surface methodology and the artificial neural networks approach were used for output modeling. Finally, the optimization of the machining parameters was performed using desirability function (DF) minimizing the surface roughness and the cutting forces simultaneously. The results indicated that the roughness is strongly affected by the feed rate ( f) with contributions of (80.71%, 80.26%, and 81.80%) for ( Ra, Rz, and Rt) respectively, and that the depth of cut ( ap) is the factor having the major influence on the cutting forces ( Fx = 53.76%, Fy = 50.79%, and Fz = 65.31%). Furthermore, artificial neural network and response surface methodology models correlate very well with experimental data. However, artificial neural network models show better accuracy. The optimum machining setting for multi-objective optimization is Vc = 80 m/min, f = 0.08 mm/rev and ap = 0.141 mm.

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