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Grey Relational Analysis-Based Optimization of Machining Processes: a Comprehensive Review

  • Review Article
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Abstract

Process optimization deals with identifying the optimal settings of input parameters of different machining processes resulting in most efficient utilization of energy and other resources, and less perilous effects on the machine operators ensuring sustainable manufacturing environment. Various mathematical tools, mainly in the form of multi-criteria decision-making techniques, have been adopted for optimizing the machining processes. Among them, grey relational analysis (GRA) appears as the most potent one due to its simple computational steps and independency on criteria weights. In this paper, a comprehensive review is carried out on the applications of GRA technique for parametric optimization of some of the industrially well-accepted conventional and non-conventional machining processes. The essence of 227 research articles (published during 2002–2022) from Scopus and other databases is also critically analyzed with respect to adoption of different experimental design plans, work materials machined, input parameters and responses considered, combination or comparison of GRA with other methods, journal-wise publication, authors’ nationality, institution-wise publication, link with other keywords, and collaborative research works across the globe. This review paper would thus help the decision-making community in exploring the application potentiality of GRA in machining processes optimization leading to sustainable manufacturing environment. Some of the future research directions are also proposed.

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Data Availability

All data generated or analyzed during this study are included in this published article.

Abbreviations

AE:

Angular error

ANFIS:

Adaptive neuro-fuzzy inference system

ANP:

Analytic network process

CCD:

Central composite design

CFRP:

Carbon fiber reinforced polymer

CRITIC:

CRiteria importance through intercriteria correlation

DD:

Dimensional deviation

DOC:

Depth of cut

EDM:

Electrical discharge machining

FEA:

Finite element analysis

GFRP:

Glass fiber reinforced polymer

GRC:

Grey relational coefficient

HAZ:

Heat affected zone

LBM:

Laser beam machining

MCDM:

Multi-criteria decision-making

MMC:

Metal matrix composite

MOORA:

Multi-objective optimization on the basis of ratio analysis

MRR:

Material removal rate

MS:

Mild steel

OA:

Orthogonal array

PCA:

Principal component analysis

PEEK:

Poly-ether-ether-ketone

PSI:

Preference selection index

Ra:

Average surface roughness

Rmax:

Value of the highest peak

Rq:

Root mean square roughness

Rsm:

Mean width of profile elements

Rt:

Difference between the highest peak and deepest valley

SA:

Simulated annealing

SS:

Stainless steel

TOPSIS:

Technique for order of preference by similarity to ideal solution

UWD:

Upper width deviation

WEDM:

Wire electrical discharge machining

WPM:

Weighted product model

WSM:

Weighted sum model

AHP:

Analytic hierarchy process

ANN:

Artificial neural network

BBD:

Box-Behnken design

CE:

Circularity error

COPRAS:

COmplex PRoportional ASsessment

CYL:

Cylindricity

DFA:

Desirability function approach

ECM:

Electro-chemical machining

ELECTRE:

ELimination Et Choice Translating REality

GA:

Genetic algorithm

GRA:

Grey relational analysis

GRG:

Grey relational grade

KW:

Kerf width

LWD:

Lower width deviation

MH:

Microhardness

MOGA:

Multi-objective genetic algorithm

MQL:

Minimum quantity lubrication

MRSN:

Multiple response signal-to-noise

NSGA:

Non-dominated sorting genetic algorithm

OC:

Overcut

PE:

Perpendicularity error

PROMETHEE:

Preference Ranking Organization METHod for Enrichment evaluation

PSO:

Particle swarm optimization

Rku:

Kurtosis value of roughness

ROC:

Radial overcut

Rsk:

Skewness value of roughness

RSM:

Response surface methodology

Rz:

Ten-point surface roughness

SCD:

Surface crack density

TLBO:

Teaching learning-based optimization

TWR:

Tool wear rate

VIKOR:

VIekriterijumsko KOmpromisno Rangiranje

WLT:

White layer thickness

W-RCL:

Width of recast layer

WSN:

Weighted signal-to-noise

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Authors and Affiliations

  1. National Institute of Industrial Engineering, Mumbai, India

    Santonab Chakraborty

  2. Department of Production Engineering, Jadavpur University, Kolkata, West Bengal, India

    Himalaya Nirjhar Datta & Shankar Chakraborty

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  1. Santonab Chakraborty
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  2. Himalaya Nirjhar Datta
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  3. Shankar Chakraborty
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Correspondence to Shankar Chakraborty.

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Chakraborty, S., Datta, H.N. & Chakraborty, S. Grey Relational Analysis-Based Optimization of Machining Processes: a Comprehensive Review. Process Integr Optim Sustain 7, 609–639 (2023). https://doi.org/10.1007/s41660-023-00311-4

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