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Multi-response optimization and characterization for micro-EDM drilling of stainless steel (AISI 304) using a hybrid DFA-fuzzy logic approach

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Abstract

Due to high strength, biocompatibility and higher corrosion resistance, stainless steels are widely used in aerospace, medical, chemical, and automotive sectors. However, conventional machining of these materials does not produce higher accuracy because of their characteristics, which lead to the tendency of build-up edge formation, high tool wear and poor surface quality. Among numerous non-conventional machining techniques, electrical discharge machining is considered one of the highly efficient and economically viable methods for machining stainless steel. In the present study, microelectric discharge drilling is conducted on an AISI 304 stainless steel sheet using a 496 µm diameter WC tool electrode. The parametric impact of feed rate, voltage, and capacitance on performance characteristics like material removal rate, overcut, circularity error, and taper angle was studied after conducting a full factorial of 48 experiments. Capacitance has been found the most essential process parameter for influencing all machining outcomes considered in this investigation. Furthermore, a desirability function integrated with fuzzy logic is used to optimize all performance measures simultaneously. The optimal setting proposed by this approach is at voltage = 100 V, capacitance = 100 pF and feed rate = 20 µm/s. The field emission scanning electron microscope (FESEM) micrographs illustrate that lower discharge energy settings result in better geometrical accuracy and surface integrity. The energy-dispersive X-ray spectroscopy (EDAX) analysis revealed the migration of elements from the tool material and dielectric medium.

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Abbreviations

DFA:

Desirability function approach

FESEM:

Field emission scanning electron microscope

EDAX:

Energy-dispersive X-ray spectroscopy

EDM:

Electrical discharge machining

WC:

Tungsten carbide

TWR:

Tool wear rate

ANN:

Artificial neural network

PCA:

Principal component analysis

GRA:

Grey relational analysis

PSO:

Particle swarm optimization

MOGA:

Multi-objective genetic algorism

RC:

Resistance capacitance

ANOVA:

Analysis of variance

DOE:

Design of experiments

MF:

Membership functions

DFRG:

Desirability fuzzy reasoning grade

R2 :

Coefficient of correlation

R2 (adj):

Adjusted coefficient of correlation

\(y\) :

Machining response

\({y}_{min}\) :

The lower acceptable limit of output

\({y}_{max}\) :

The upper acceptable limit of output

\({d}_{i}\) :

Individual desirability

\(r\) :

Desirability function index

\({D}_{o}\) :

Overall desirability

MRR:

Material removal rate

OC:

Overcut

CE:

Circularity error

TA:

Taper angle

V:

Voltage

C:

Capacitance

FR:

Feed rate

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Institute of Technology Silchar, Silchar, India

    Alemu Workie Kebede, Promod Kumar Patowari & Chinmaya Kumar Sahoo

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Kebede, A.W., Patowari, P.K. & Sahoo, C.K. Multi-response optimization and characterization for micro-EDM drilling of stainless steel (AISI 304) using a hybrid DFA-fuzzy logic approach. Int J Interact Des Manuf (2024). https://doi.org/10.1007/s12008-024-01873-4

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