scholarly journals Investigation of the effect of cutting speed on the Surface Roughness parameters in CNC End Milling using Artificial Neural Network

2013 ◽  
Vol 53 ◽  
pp. 012089 ◽  
Author(s):  
Muataz H F Al Hazza ◽  
Erry Y T Adesta
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
End Milling ◽  
Cutting Speed ◽  
Roughness Parameters ◽  
Surface Roughness Parameters ◽  
Artificial Neural ◽  
Cnc End Milling

scholarly journals The influence of technological parameters on surface roughness during turning and roughness prediction using artificial neutral networks

Mechanik ◽  
2018 ◽  
Vol 91 (10) ◽  
pp. 898-900 ◽  
Author(s):  
Ireneusz Zagórski ◽  
Monika Kulisz ◽  
Tomasz Warda
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Aluminum Alloy ◽  
Roughness Parameter ◽  
Cutting Speed ◽  
Roughness Parameters ◽  
Technological Parameters ◽  
Surface Roughness Parameters ◽  
Roughness Prediction

The purpose of this investigation was to determine whether and to what extent the technological parameters of turning (feed, cutting speed) affect selected surface roughness parameters of aluminum alloy EN-AW 7075 (AlZn5.5MgCu). The principal findings indicate a significant impact of feed and show on the surface roughness and simultaneously show that cutting speed has no effect on the value of surface roughness parameters under investigation. An artificial neural network was employed to evaluate the prediction of surface roughness parameter Rz in turning.

Prediction of Cutting Temperatures by Using Back Propagation Neural Network Modeling when Cutting Hardened H-13 Steel in CNC End Milling

2012 ◽  
Vol 576 ◽  
pp. 91-94 ◽  
Author(s):  
Erry Yulian Triblas Adesta ◽  
Muataz H.F. Al Hazza ◽  
M.Y. Suprianto ◽  
Muhammad Riza
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
End Milling ◽  
Back Propagation ◽  
Cutting Temperature ◽  
Neural Network Modeling ◽  
Feed Forward Back Propagation ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Cnc End Milling

Machining of hardened steel at high cutting speeds produces high temperatures in the cutting zone, which affects the surface quality and cutting tool life. Thus, predicting the temperature in early stage becomes utmost importance. This research presents a neural network model for predicting the cutting temperature in the CNC end milling process. The Artificial Neural Network (ANN) was applied as an effective tool for modeling and predicting the cutting temperature. A set of sparse experimental data for finish end milling on AISI H13 at hardness of 48 HRC have been conducted to measure the cutting temperature. The artificial neural network (ANN) was applied to predict the cutting temperature. Twenty hidden layer has been used with feed forward back propagation hierarchical neural networks were designed with Matlab2009b Neural Network Toolbox. The results show a high correlation between the predicted and the observed temperature which indicates the validity of the models.

Surface Roughness Prediction in Turning of Free Machining Steel 1215 by Artificial Neural Network

2011 ◽  
Vol 188 ◽  
pp. 535-541
Author(s):  
Xiao Jiang Cai ◽  
Z.Q. Liu ◽  
Q.C. Wang ◽  
Shu Han ◽  
Qing Long An ◽  
...  
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Mean Squared Error ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Ann Model ◽  
Roughness Prediction ◽  
Artificial Neural

Surface roughness is a significant aspect of the surface integrity concept. It is efficient to predict the surface roughness in advance by a prediction model. In this study, artificial neural network is used to model the surface roughness in turning of free machining steel 1215. The inputs considered in the prediction ANN model were cutting speed, feed rate and depth of cut, and the output was Ra. Several feed-forward neural networks with different architectures were compared in terms of prediction accuracy, and then the best prediction model, a 3-4-1-1 ANN was capable of predicting Ra with a mean squared error 5.46%, was presented.

Artificial Intelligence Model of Surface Roughness for End Milling Operation of Steel and its Verification by Genetic Algorithm

2011 ◽  
Vol 110-116 ◽  
pp. 3459-3464
Author(s):  
Mohammed Anayet Ullah Patwari ◽  
A.K.M. Nurul Amin
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
End Milling ◽  
Depth Of Cut ◽  
Efficient Production ◽  
Statistical Experimental Design ◽  
Artificial Neural ◽  
Coated Carbide

Surface roughness is important for evaluating the machined surface quality. In this work, an Artificial Neural Network (ANN) surface roughness prediction model was developed by coupling it with Response Surface Methodology (RSM). For this interpretation, advantages of statistical experimental design techniques, experimental measurements, and artificial neural network were exploited in an integrated manner. Cutting experiments were designed based on small centre composite design technique to develop a RSM model. The input cutting parameters were: cutting speed, feed, and axial depth of cut, and the output parameter was surface roughness. The predictive model was created using a feed-forward back-propagation neural network exploiting the experimental data. The network was trained with pairs of inputs/outputs datasets generated by end milling medium carbon steel with TiN coated carbide inserts. The model can be used for the analysis and prediction of the complex relationships between cutting conditions and surface roughness, in metal-cutting operations, with the ultimate goal of efficient production. The ANN model was verified with the optimized parameters predicted by a coupled genetic algorithm (GA) and RSM technique also developed by the authors.

Optimisation of End Milling Parameters on Aluminium/SiC Composites Using Response Surface and Artificial Neural Network Methodologies

2013 ◽  
Vol 766 ◽  
pp. 59-75
Author(s):  
Vijayan Krishnaraj
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface ◽  
End Milling ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Total Force ◽  
Machining Parameters ◽  
Cnc Milling ◽  
Artificial Neural

In recent years, the utilization of metal matrix composites (MMC) have increased in engineering fields of automobile, aviation, aerospace, construction and microelectronics. In conjunction with innovations in these advanced materials, machining them to obtain good dimensional accuracy and surface integrity has become a challenge. In this present work, 15 % (by weight) SiC particle reinforced aluminium is synthesized by stir casting technique. The synthesized MMC is end milled using Ø16 mm carbide end mill in a CNC milling machine and the machining parameters explicitly cutting speed, feed rate and depth of cut are optimised for minimum surface roughness and total force acting on the tool using response surface methodology (RSM) and artificial neural network (ANN). The un-coated cutting tool insert was compared with the nanocomposite coated insert on tool wear at optimised cutting conditions.

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