Surface Roughness Prediction of High Speed Milling Based on Back Propagation Artificial Neural Network

2011 ◽  
Vol 201-203 ◽  
pp. 696-699
Author(s):  
Jin Ping Hu ◽  
Yan Li ◽  
Jing Chong Zhang
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
High Speed ◽  
Back Propagation ◽  
Quality Analysis ◽  
High Speed Milling ◽  
Surface Roughness Prediction ◽  
Roughness Prediction ◽  
Artificial Neural

Prediction of surface roughness is an important research for machining quality analysis. In order to predict surface roughness in machining, increasing productivity under ensuring milling, the artificial neural network is introduced into milling area. To build high-speed milling surface roughness prediction model using BP neural network. Prediction results are compared with experimental value, which shows that this method can achieve better prediction accuracy. It has certain significance for parameters selection of high-speed milling and quality control of the surface.

Prediction of Surface Roughness in High Speed Milling Process Using the Artificial Neural Networks

2007 ◽  
Vol 364-366 ◽  
pp. 713-718 ◽  
Author(s):  
Dong Woo Kim ◽  
Young Jae Shin ◽  
Kyoung Taik Park ◽  
Eung Sug Lee ◽  
Jong Hyun Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
High Speed ◽  
Back Propagation Algorithm ◽  
High Speed Milling ◽  
Artificial Neural ◽  
Cutting Path

The objective of this research was to apply the artificial neural network algorithm to predict the surface roughness in high speed milling operation. Tool length, feed rate, spindle speed, cutting path interval and run-out were used as five input neurons; and artificial neural networks model based on back-propagation algorithm was developed to predict the output neuron-surface roughness. A series of experiments was performed, and the results were estimated. The experimental results showed that the applied artificial neural network surface roughness prediction gave good accuracy in predicting the surface roughness under a variety of combinations of cutting conditions.

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.

scholarly journals SOFTWARE IMPLEMENTATION OF THE ARTIFICIAL NEURAL NETWORK FOR VIRTUAL OBJECTS СONTROL

2018 ◽  
pp. 72-78 ◽  
Author(s):  
Yu. B. Popova ◽  
S. V. Yatsynovich
Keyword(s):  
Neural Network ◽  
Mathematical Modeling ◽  
Artificial Neural Network ◽  
High Speed ◽  
Back Propagation ◽  
Computer Game ◽  
Training Sample ◽  
Software Implementation ◽  
Virtual Objects ◽  
Artificial Neural

Artificial neural networks (ANN) are now widely used in control and forecasting problems. The purpose of this work is the implementation of an artificial neural network for virtual objects control in a computer game of football. To achieve this goal, it is necessary to solve a number of problems related to mathematical modeling of ANN, algorithmization and software implementation. The paper deals with the mathematical modeling of an artificial neural network by the method of back propagation of an error, the algorithms for calculating neurons and for teaching ANN are presented. The software implementation of the artificial neural network was performed in the JavaScript language using the Node. js library, which assumed the role of a server for managing the game process. Some functions of the Underscore. js library were used to work with data arrays. The training sample consisted of more than 1000 sets of inputs and outputs, reflecting all possible situations. The results of software implementation of an artificial neural network are described on the example of virtual players control for a computer game. The results of the work show that ANN with a sufficiently high speed in real time gives the necessary direction for the player’s movement. The use of an artificial neural network has reduced the use of CPU time, which is extremely important in problems where rapid decision making is required, because complex calculations and prediction algorithms can not always be invested in 20 ms, which is fraught with skipping moves and losses. The simulated artificial neural network and the implemented algorithm of its learning can be used to solve other problems, for which only new data of the surrounding world are needed.

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