Development of Piercing Technology for CVJ Cage with Thick and Uneven Piercing Holes

2013 ◽  
Vol 773-774 ◽  
pp. 95-103
Author(s):  
Dong Hwan Kim ◽  
Se Je Park
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Tool Life ◽  
Significant Role ◽  
Deformation Behavior ◽  
Fe Simulation ◽  
Simulation Technique ◽  
Dimension Precision ◽  
Artificial Neural

In this paper, to design the piercing punch for thick and uneven steel in piercing process, FE simulation technique has been proposed to predict the deformation behavior of CVJ cage through the simulation of the piercing process. In this research, an optimizing technique based on artificial neural network has been applied for the thick and uneven steel in piercing processes to construct database of the dimension precision depending on the shape of punch. It is indicated that shape of punch plays a significant role to keep the dimension precision and to increase the tool life in the uneven piercing process.

COMPARISON BETWEEN PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM IN ARTIFICIAL NEURAL NETWORK FOR LIFE PREDICTION OF NC TOOLS

2008 ◽  
Vol 07 (01) ◽  
pp. 1-7 ◽  
Author(s):  
SHILONG WANG ◽  
FEI ZHENG ◽  
LING XU
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Particle Swarm Optimization ◽  
Convergence Rate ◽  
Tool Life ◽  
Life Prediction ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Artificial Neural

Accurate life prediction of NC (Numeric Control) tools is very essential in an advanced manufacturing system. In this paper, tool life prediction in a drilling process was researched. An Artificial Neural Network (ANN) has been established for prediction, with drill diameter, cutting speed and feed rate as input parameters and tool life as an output parameter. To improve the performance of the network, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) were applied independently to train the network instead of standard Backward Propagation (BP) algorithm, which has drawbacks of low convergence rate and weak generalization capacity. And the two methods were compared in terms of algorithm complexity, convergence rate and prediction accuracy, with reference to standard BP method.

scholarly journals Predicting Fabric Drapability Property by Using an Artificial Neural Network

2018 ◽  
Vol 13 (3) ◽  
pp. 155892501801300 ◽  
Author(s):  
Amine Hadj Taieb ◽  
Slah Mshali ◽  
Faouzi Sakli
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Significant Role ◽  
Woven Fabric ◽  
Artificial Neural

Drape, one of the most important properties of fabric, has played significant role in providing graceful aesthetic effects in garments. Drapability of textiles is judged subjectively and is dependent on people's skill and experience, which render difficulties during drape comparisons, especially when judged by different people. This work reports the results of a study on predicting the drapability of woven fabric using an artificial neural network. It was established that drapability could be predicted from the mechanical properties of fabric at low-stress.

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