scholarly journals An enhanced neural network model for predictive control of granule quality characteristics

2011 ◽  
Vol 18 (3) ◽  
pp. 722-730 ◽  
Author(s):  
N. Neshat ◽  
H. Mahlooji ◽  
A. Kazemi
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Predictive Control ◽  
Neural Network Model ◽  
Quality Characteristics

Drilling of micro-holes on titanium alloy using pulsed Nd:YAG laser: Parametric appraisal and prediction of performance characteristics

2018 ◽  
Vol 233 (8) ◽  
pp. 1872-1889 ◽  
Author(s):  
Suman Chatterjee ◽  
Siba Sankar Mahapatra ◽  
Vijay Bharadwaj ◽  
Ambar Choubey ◽  
Brahma N Upadhyay ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Performance Measures ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Laser Energy ◽  
Quality Characteristics ◽  
Hole Quality ◽  
Artificial Neural

Laser drilling is a preferable method in drilling micro and slender holes because of its non-physical contact between the tool and the workpiece leading to elimination of the problems like chatter and vibration. In this article, an experimental investigation of the laser drilling process is carried out on thin sheet (0.5 mm) of titanium alloy (Ti6Al4V) using pulsed Nd:YAG laser. The study investigates the effect of laser parameters such as laser energy, pulse repetition rate, pulse width and gas pressure on hole quality characteristics, namely, circularity at entry and exit, taper and spatter area. Taguchi’s L27 orthogonal array is adopted for conducting the experiments to establish significance of control parameters on the performance measures with less number of experimental run. Analysis of variance shows that laser energy and pulse width significantly influence on circularity, taper and spatter area. Optimal parameter setting for all the performance measures is suggested. Statistically valid empirical models relating hole quality characteristics with laser parameters have been developed based on least square regression analysis. Backpropagation artificial neural network is used predict the output responses considering laser process parameters as inputs. A mean square error within 5% for both the training and testing data suggests the adequacy and robustness of the proposed artificial neural network model. Variation of performance measures with respect to process parameters is predicted through the well-trained artificial neural network model. The pattern of variation exhibits good agreement with the main effect plots obtained through Taguchi method. Artificial neural network model produces mean relative error of 0.001, 0.001, 0.0437 and 0.0234 for circularity at entry, circularity at exit, taper and spatter area, respectively, when compared with experimental values. Since small values of mean relative error are obtained, the proposed artificial neural network model can be satisfactorily used for prediction of quality characteristics of laser micro-drilled holes.

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