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A Sco** Review on the Applications of MCDM Techniques for Parametric Optimization of Machining Processes

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Abstract

Determination of the optimal input parameters for any of the machining processes plays a pivotal role in achieving the most suitable response values while fulfilling the requirements of both the manufacturers and end users. Among the present-day research community, different multi-criteria decision making (MCDM) techniques have become quite popular as effective multi-objective optimization tools to identify the most appropriate parametric combinations of different machining processes based on real-time experimental data. In this paper, more than 120 research articles (searched through Sciencedirect, Scopus and Web of Science) are reviewed while exploring the applications of different MCDM techniques in solving parametric optimization problems of turning, drilling and milling processes. This review paper would act as a knowledge-base to the decision making practitioners and process engineers in deciding the most appropriate experimental design plan to be deployed (Taguchi’s L9, L18 or L27 orthogonal array); difficult-to-cut advanced engineering materials to be machined (composites, and aluminum and titanium and their alloys); input parameters for turning, drilling and milling processes (cutting speed, feed rate and depth of cut), and corresponding responses (material removal rate and surface roughness) to study their interaction effects, MCDM tools (grey relational analysis and TOPSIS), and subjective (analytic hierarchy process) and objective (entropy method) criteria weighting techniques to be employed; and possibility of integration with other mathematical tools to deal with uncertain decision making environment. The essence of all the reviewed articles is concisely presented in succinct tabular forms, which would make this paper an asset to the researchers and practitioners. Future directions are also provided to help them in optimization of manufacturing processes leading to attainment of more pragmatic solutions.

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Abbreviations

AHP:

Analytic hierarchy process

ANN:

Artificial neural network

ARAS:

Additive ratio assessment

BWM:

Best worst method

CCD:

Central composite design

CFRP:

Carbon fiber reinforced polymer

CNC:

Computer numerical control

CODAS:

Combinative distanced-based assessment

COPRAS:

Complex proportional assessment

CRITIC:

Criteria importance through intercriteria correlation

CSA:

Cuckoo search algorithm

DF:

Desirability function

DOC:

Depth of cut

EDAS:

Evaluation based on distance from average solution

FFD:

Full factorial design

FUCOM:

Full consistency method

GA:

Genetic algorithm

GFRP:

Glass fiber reinforced polymer

GRA:

Grey relational analysis

GTMA:

Graph theory and matrix approach

MCDM:

Multi-criteria decision making

MMC:

Metal matrix composite

MOORA:

Multi-objective optimization on the basis of ratio analysis

MARICA:

Multi-attributive real–ideal comparative analysis

MARCOS:

Measurement alternatives and ranking according to compromise solution

MABAC:

Multi-attributive border approximation area comparison

MRR:

Material removal rate

NSGA-II:

Non-dominated sorting genetic algorithm-II

OA:

Orthogonal array

PSI:

Preference Selection Index

PCA:

Principal component analysis

PEEK:

Poly-ether-ether-ketone

PSO:

Particle swarm optimization

PTFE:

Poly tetra fluoro ethylene

Ra:

Average surface roughness

Rku:

Kurtosis of surface roughness distribution

Rq:

Root-mean-square roughness

Rt:

Maximum height of the profile

Rsm:

Mean width of profile elements

Rz:

Distance between the highest peak and the deepest valley

RIM:

Reference ideal method

SD:

Standard deviation

SR:

Surface roughness

TOPSIS:

Technique for order of preference by similarity to ideal solution

WASPAS:

Weighted aggregated sum product assessment

WSM:

Weighted sum method

VIKOR:

Vlsekriterijumska optimizacija I kompromisno resenje

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  1. National Institute of Industrial Engineering, Mumbai, India

    Santonab Chakraborty

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

    Shankar Chakraborty

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Chakraborty, S., Chakraborty, S. A Sco** Review on the Applications of MCDM Techniques for Parametric Optimization of Machining Processes. Arch Computat Methods Eng 29, 4165–4186 (2022). https://doi.org/10.1007/s11831-022-09731-w

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