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Predictive Analysis on Responses in WEDM of Titanium Grade 6 Using General Regression Neural Network (GRNN) and Multiple Regression Analysis (MRA)

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Abstract

In the present investigation two smart prediction tools, namely the general regression neural network (GRNN) and multiple regression analysis (MRA) models were developed to predict and compare some of the key machinability aspects like average kerf width, average surface roughness and material removal rate in the wire electrical discharge machining process of titanium grade 6. Pulse-on time, pulse-off time, wire feed and wire tension were considered as machining variables to develop the predictive model. In order to curtail cross-validation error in GRNN, optimized kernel bandwidth was utilized using the grid search method. The neural network and regression models were trained, validated and tested with measured data. A mathematical model was developed using multiple regression analysis. The ANOVA test was also conducted to determine the significant parameters affecting the responses. The results indicated that the predicted responses lie within ± 5% and ± 10% error for GRNN and MRA, respectively, which suggests that the GRNN model is more reliable and adequate than the regression model. A comparative study with previous research work was also done to confirm the novelty along with application potential of the proposed model.

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Abbreviations

NTM:

non-traditional machining

WEDM:

wire electrical discharge machining

EDM:

electrical discharge machining

MR:

multiple regression

MRA:

multiple regression analysis

CNC:

computer numerical control

TON :

pulse-on time

TOFF :

pulse-off time

WF:

wire feed

WT:

wire tension

MRR:

material removal rate

EDS:

energy dispersive spectroscopy

ANN:

artificial neural network

GRNN:

general regression neural network

NSGA-II:

non-dominated sorting genetic algorithm-II

BPNN:

back propagation neural network

FFBPNN:

feed forward back propagation neural network

GA:

genetic algorithm

PSO:

particle swarm optimization

AWJM:

abrasive waterjet machining

ANOVA:

analysis of variance

GRA:

grey relational analysis

TWR:

tool wear rate

WWR:

weight wear ratio

RSM:

response surface methodology

HSLA:

high strength low alloy

BPN:

back propagation

EDS:

energy dispersive spectroscopy

SEM:

scanning electron microscope

PH:

precipitation hardening

NF:

neuro fuzzy

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Institute of Technology, Rourkela, 769008, Odisha, India

    H. Majumder & K. P. Maity

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  1. H. Majumder
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Majumder, H., Maity, K.P. Predictive Analysis on Responses in WEDM of Titanium Grade 6 Using General Regression Neural Network (GRNN) and Multiple Regression Analysis (MRA). Silicon 10, 1763–1776 (2018). https://doi.org/10.1007/s12633-017-9667-1

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