scholarly journals Comparative Study of Electrode Wear Estimation in Wire EDM using Multiple Regression Analysis and Group Method Data Handling Technique for EN-8 and EN-19

2014 ◽  
Vol 4 (2) ◽  
pp. 108-114
Author(s):  
Ugrasen G
Keyword(s):  
Regression Analysis ◽  
Comparative Study ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Group Method ◽  
Electrode Wear ◽  
Data Handling ◽  
Wire Edm ◽  
Handling Technique ◽  
Group Method Data Handling

Comparison of Machining Responses Using Multiple Regression Analysis and Group Method Data Handling Technique of EN-19 Material in WEDM

2014 ◽  
Vol 592-594 ◽  
pp. 97-101
Author(s):  
G. Ugrasen ◽  
H.V. Ravindra ◽  
G.V. Naveen Prakash ◽  
R. Keshavamurthy
Keyword(s):  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Machining Process ◽  
Group Method ◽  
Main Stream ◽  
Electrode Wear ◽  
Data Handling ◽  
Handling Technique ◽  
Group Method Data Handling

Wire Electrical Discharge Machining (WEDM) is a specialized thermal machining process capable of accurately machining parts with varying hardness or complex shapes, which have sharp edges that are very difficult to be machined by the main stream machining processes. This study outlines the development of model and its application to estimation and comparison of machining responses using Multiple Regression Analysis (MRA) and Group Method Data Handling Technique (GMDH). Experimentation was performed as per Taguchi’s L’16 orthogonal array for EN-19 material. Each experiment has been performed under different cutting conditions of pulse-on, pulse-off, current and bed speed. Among different process parameters voltage and flush rate were kept constant. Molybdenum wire having diameter of 0.18 mm was used as an electrode. Four responses namely accuracy, surface roughness, volumetric material removal rate and electrode wear have been considered for each experiment. Estimation and comparison of responses was carried out using MRA and GMDH.

Comparison of Machining Performances in Wire EDM for HCHCr Material Using Group Method Data Handling Technique and Artificial Neural Network

2015 ◽  
Author(s):  
Ugrasen Gonchikar ◽  
Ravindra Holalu Venkatadas ◽  
Naveen Prakash Goravi Vijaya Dev ◽  
Keshavamurthy Ramaiah ◽  
Giridhara Gudekota
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Group Method ◽  
Data Handling ◽  
Training Set ◽  
Work Material ◽  
Handling Technique ◽  
Artificial Neural ◽  
Group Method Data Handling ◽  
Testing Set

Wire Electrical Discharge Machining (WEDM) is a specialized thermo electrical machining process capable of accurately machining parts with varying hardness or complex shapes. Present study outlines the comparison of machining performances in the wire electric discharge machining using group method data handling technique and artificial neural network. HCHCr material was selected as a work material. This work material was machined using different process parameters based on Taguchi’s L27 standard orthogonal array. Parameters such as pulse-on time, pulse-off time, current and bed speed were varied. The response variables measured for the analysis are surface roughness, volumetric material removal rate and dimensional error. Machining performances were compared using sophisticated mathematical models viz., Group Method of Data Handling (GMDH) technique and Artificial Neural Network (ANN). GMDH is ideal for complex, unstructured systems where the investigator is only interested in obtaining a high-order input-output relationship. Also, the method is heuristic in nature and is not based on a solid foundation as regression analysis. The GMDH algorithm is designed to learn the process by training the algorithm with the experimental data. The experimental observations are divided into two sets viz., the training set and testing set. The training set is used to make the GMDH learn the process and the testing set will check the performance of GMDH. Different models were obtained by varying the percentage of data in the training set and the best model were selected from these, viz., 50%, 62.5% & 75%. The best model was selected from the said percentages of data. Number of variables selected at each layer is usually taken as a fixed number or a constantly increasing number. It is usually given as fractional increase in number of independent variables present in the previous level. Three different criterion functions, viz., Root Mean Square (Regularity) criterion, Unbiased criterion and Combined criterion were considered for estimation. The choice of the criterion for node selection is another important parameter for proper modeling. The Artificial Neural Network is used to study and predict the machining responses. Input data are fed into the neural network and corresponding weights and bias are extracted. Then weights and bias are integrated in the program which is used to calculate and predict the machining responses. Estimation of machining performances was obtained by using ANN for various cutting conditions. ANN estimates were obtained for various percentages of total data in the training set viz., 50%, 60% & 70%. The best model was selected from the said percentages of data. Estimation and comparison of machining performances were carried out using GMDH and ANN. Estimates from GMDH and ANN were compared and it was observed that ANN with 70% of data in training set gives better results than GMDH.

Estimation and Comparison of Machining Performances Using Group Method Data Handling Technique and ANN in Wire EDM of Stavax Material

2020 ◽  
Author(s):  
Ugrasen Gonchikar ◽  
Holalu Venkatdas Ravindra ◽  
Rudreshi Addamani ◽  
Prathik Jain Sudhir
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cutting Conditions ◽  
Group Method ◽  
Data Handling ◽  
Training Set ◽  
Handling Technique ◽  
Artificial Neural ◽  
Group Method Data Handling ◽  
Testing Set

Abstract Wire Electrical Discharge Machining (WEDM) is a specialized thermal machining process capable of accurately machining parts with varying hardness or complex shapes, which have sharp edges that are very difficult to be machined by the main stream machining processes. This study outlines the development of model and its application to estimation of machining performances using Group Method Data Handling Technique (GMDH) and Artificial Neural Network (ANN). Experimentation was performed as per Taguchi’s L’16 orthogonal array for Stavax (modified AISI 420 steel) material. Each experiment has been performed under different cutting conditions of pulse-on, pulse-off, current and bed speed. Among different process parameters voltage and flush rate were kept constant. Molybdenum wire having diameter of 0.18 mm was used as an electrode. Four responses namely accuracy, surface roughness, Volumetric Material Removal Rate (VMRR) and Electrode Wear (EW) have been considered for each experiment. Estimation and comparison of responses was carried out using GMDH and ANN. Group method data handling technique is ideal for complex, unstructured systems where the investigator is only interested in obtaining a high-order input-output relationship. Also, the method is heuristic in nature and is not based on a solid foundation as in regression analysis. The GMDH algorithm is designed to learn the process by training the algorithm with the experimental data. The experimental observations are divided into two sets viz., the training set and testing set. The training set is used to make the GMDH learn the process and the testing set will check the performance of GMDH. Different models can be obtained by varying the percentage of data in the training set and the best model can be selected from these, viz., 50%, 62.5% & 75%. The best model is selected from the said percentages of data. Number of variables selected at each layer is usually taken as a fixed number or a constantly increasing number. It is usually given as fractional increase in number of independent variables present in the previous level. Three different criterion functions, viz., Root Mean Square (Regularity) criterion, Unbiased criterion and Combined criterion were considered for estimation. The choice of the criterion for node selection is another important parameter for proper modeling. The Artificial Neural Network is used to study and predict the machining responses. Input data are fed into the neural network and corresponding weights and bias are extracted. Then weights and bias are integrated in the program which is used to calculate and predict the machining responses. Estimation of machining performances was obtained by using ANN for various cutting conditions. ANN estimates were obtained for various percentages of total data in the training set viz., 50%, 60% & 70%. The best model is selected from the said percentages of data. Estimation and comparison of machining performances were carried out using GMDH and ANN. Estimates from GMDH and ANN were compared and it was observed that ANN with 70% of data in training set gives better results than GMDH.

Comparative Study of Three Stretching Techniques

1970 ◽  
Vol 31 (2) ◽  
pp. 611-616 ◽  
Author(s):  
Laurence E. Holt ◽  
Thomas M. Travis ◽  
Ted Okita
Keyword(s):  
Regression Analysis ◽  
Comparative Study ◽  
Range Of Motion ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Serial Order ◽  
Normal Male ◽  
Order Effects ◽  
Post Exercise ◽  
Proprioceptive Neuromuscular Facilitation

This study involved comparison of three techniques for increasing range of motion. Fast stretch (ballistic), slow stretch, and IA-CA, a modified version of proprioceptive neuromuscular facilitation (PNF) were compared using 24 normal male Ss. Controlling for serial order effects, 6 groups of 4 Ss each were administered the treatments, and the differences between pre- and post-exercise measures were observed. Multiple regression analysis indicated the superiority of the IA-CA approach.

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