Prediction and optimisation of tool wear for end milling operation using artificial neural networks and simulated annealing algorithm

2013 ◽  
Vol 14 (2) ◽  
pp. 142 ◽  
Author(s):  
S. Kalidass ◽  
P. Palanisamy ◽  
V. Muthukumaran
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Simulated Annealing ◽  
Tool Wear ◽  
Simulated Annealing Algorithm ◽  
End Milling ◽  
Milling Operation ◽  
Annealing Algorithm ◽  
Artificial Neural

Simulation of the Mean-Zero-Up-Crossing Wave Period Using Artificial Neural Networks Trained With Simulated Annealing Algorithm

2006 ◽  
Author(s):  
H. Bazargan ◽  
H. Bahai ◽  
F. Aryana ◽  
S. F. Yasseri
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
North East ◽  
The North ◽  
Wave Heights ◽  
Significant Wave Heights ◽  
Annealing Algorithm ◽  
Artificial Neural

The aim of this work is to simulate the 3-houly mean zero-up-crossing wave periods (Tzs) of the sea-states of a future period for a location in the North East Pacific. Seven multi-layer artificial neural networks (ANNs) were trained with simulated annealing algorithm. The output of each ANN was used for estimating each of the 7 parameters of a new distribution, described in Appendix A, called hepta-parameter spline proposed for the conditional distribution of the Tz given some significant wave heights and mean zero-up-crossing wave periods. After estimating the parameters of the conditional distributions, the Tzs have been forecasted from the hepta-parameter spline distributions corresponding to the Tzs of the period.

Optimal Training of Artificial Neural Networks to Forecast Power System State Variables

2014 ◽  
Vol 3 (1) ◽  
pp. 65-82 ◽  
Author(s):  
Victor Kurbatsky ◽  
Denis Sidorov ◽  
Nikita Tomin ◽  
Vadim Spiryaev
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Simulated Annealing ◽  
Power System ◽  
Simulated Annealing Algorithm ◽  
Active Power ◽  
Support Vector ◽  
State Variables ◽  
Annealing Algorithm ◽  
Artificial Neural

The problem of forecasting state variables of electric power system is studied. The paper suggests data-driven adaptive approach based on hybrid-genetic algorithm which combines the advantages of genetic algorithm and simulated annealing algorithm. The proposed method has two stages. At the first stage the input signal is decomposed into orthogonal basis functions based on the Hilbert-Huang transform. The genetic algorithm and simulated annealing algorithm are applied to optimal training of the artificial neural network and support vector machine at the second stage. The results of applying the developed approach for the short-term forecasts of active power flows in the electric networks are presented. The best efficiency of proposed approach is demonstrated on real retrospective data of active power flow forecast using the hybrid-genetic support vector machine algorithm.

Modeling & Analysis of End Milling Process Parameters Using Artificial Neural Networks

2014 ◽  
Vol 592-594 ◽  
pp. 2733-2737 ◽  
Author(s):  
G. Harinath Gowd ◽  
K. Divya Theja ◽  
Peyyala Rayudu ◽  
M. Venugopal Goud ◽  
M .Subba Roa
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Process Parameters ◽  
End Milling ◽  
Milling Process ◽  
Machining Process ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Artificial Neural ◽  
End Milling Process

For modeling and optimizing the process parameters of manufacturing problems in the present days, numerical and Artificial Neural Networks (ANN) methods are widely using. In manufacturing environments, main focus is given to the finding of Optimum machining parameters. Therefore the present research is aimed at finding the optimal process parameters for End milling process. The End milling process is a widely used machining process because it is used for the rough and finish machining of many features such as slots, pockets, peripheries and faces of components. The present work involves the estimation of optimal values of the process variables like, speed, feed and depth of cut, whereas the metal removal rate (MRR) and tool wear resistance were taken as the output .Experimental design is planned using DOE. Optimum machining parameters for End milling process were found out using ANN and compared to the experimental results. The obtained results provβed the ability of ANN method for End milling process modeling and optimization.

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