Modeling of metal removal rate in machining of aluminum matrix composite using artificial neural network

2011 ◽  
Vol 45 (22) ◽  
pp. 2309-2316 ◽  
Author(s):  
K.L. Senthil Kumar ◽  
R. Sivasubramanian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Matrix Composite ◽  
Metal Removal ◽  
Removal Rate ◽  
Aluminum Matrix ◽  
Aluminum Matrix Composite ◽  
Metal Removal Rate ◽  
Artificial Neural

Ascertaining of chatter stability using wavelet denoising and artificial neural network

2018 ◽  
Vol 233 (1) ◽  
pp. 39-62 ◽  
Author(s):  
Shailendra Kumar ◽  
Bhagat Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Metal Removal ◽  
Removal Rate ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Metal Removal Rate ◽  
Artificial Neural ◽  
Propagation Network ◽  
Tool Chatter

In modern machining industries, tool chatter detection and suppression along with maximized metal removal rate is a challenging task. Inexpedient vibration between cutting tool and work piece promotes unstable cutting. This results in enhanced detritions of tool and poor surface finish along with unpredictable metal removal rate. In the present work, effect of machining parameters such as depth of cut ( d), feed rate ( f) and spindle speed ( N) on chatter severity and metal removal rate have been ascertained experimentally. Experimentally recorded raw chatter signals have been denoised using wavelet transform. An artificial neural network model based on feed forward back propagation network has been proposed for predicting stable cutting zone and metal removal rate in turning process. It has been deduced that Tangent Sigmoid activation function in an artificial neural network is the best option to achieve the aforesaid objectives. Well correlation between the artificial neural network predicted results and experimental ones validate the developed technique of ascertaining the tool chatter severity.

Use of artificial neural network and multi-objective genetic algorithm approach to predict and ascertain stable cutting zone in conventional turning process

2018 ◽  
Vol 49 (5) ◽  
pp. 191-214 ◽  
Author(s):  
Shailendra Kumar ◽  
Bhagat Singh
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Metal Removal ◽  
Removal Rate ◽  
Metal Removal Rate ◽  
Multi Objective Genetic Algorithm ◽  
Cutting Zone ◽  
Artificial Neural ◽  
Chatter Index

This article is focused on the investigation of stable cutting zone in turning operation. Experiments have been conducted to acquire raw chatter signals. Generally, raw chatter signals are contaminated with ambient noise. Wavelet transform has been used for pre-processing and denoising these signals. In order to predict the severity of tool chatter, a new parameter denoted as chatter index has been evaluated considering the aforesaid denoised signals. In the present work, mathematical models have been developed for chatter index and metal removal rate using feedforward backpropagation–based artificial neural network considering three activation functions: TANSIG, LOGSIG and PURELIN. Furthermore, multi-objective genetic algorithm technique has been applied to evaluate stable cutting zones with maximized metal removal rate. TANSIG activation function found to be best option to achieve the aforesaid objectives. Good correlation between the artificial neural network predicted results and experimental ones validate the developed technique.

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