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Finite Element Modelling, Predictive Modelling and Optimization of Metal Inert Gas, Tungsten Inert Gas and Friction Stir Welding Processes: A Comprehensive Review

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Abstract

Welding is an essential fabrication process in any of the construction or manufacturing industries. Over the years, numerous welding techniques have been developed to fulfil the continuously changing requirements of the manufacturers as well as to keep up with the evolving materials. Any welding operation is associated with a number of input parameters, their interactions with the achievable weld quality and material properties that determine the success of the overall process. Due to time consuming nature of physical experiments and advancements in computing power and theories, in-silico parametric analysis and optimization of welding processes has gained significant attention in recent years. This paper endeavours to provide a timely comprehensive review of the existing literature on finite element modelling, predictive modelling and optimization of the welding processes. Due to numerous welding processes, an exhaustive review of all of them would exceed the limit of a single research paper. Thus, only three widely popular and versatile welding processes, i.e. metal inert gas (MIG) welding, tungsten inert gas (TIG) welding and friction stir welding (FSW) are considered in this review paper. The essence of more than 225 research articles is concisely presented here, which would make this paper an asset to the future researchers and practitioners. Thermo-mechanical finite element analysis of welding processes, and use of traditional experiment design plans, like orthogonal array, Box-Behnken design and central composite design are found to be extremely popular in the literature.

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Abbreviations

ALE:

Arbitrary Lagrangian-Eulerian

ANFIS:

Adaptive Neuro Fuzzy Inference System

ANOVA:

Analysis of Variance

ANN:

Artificial Neural Network

BA:

Bat Algorithm

BBD:

Box-Behnken Design

BPNN:

Back Propagation Neural Network

BR:

Bayesian Regression

CCD:

Central Composite Design

COPRAS:

Complex Proportional Assessment

CS:

Cuckoo Search

DEMO:

Differential Evolution for Multi-Objective

DF:

Desirability Function

DFA:

Dragon Fly Algorithm

DOE:

Design of Experiments

DT:

Decision Tree

ENR:

Elastic Net Regression

ESWSA:

Elephant Swarm Water Search Algorithm

FCCD:

Face-centered Central Composite Design

FEA:

Finite Element Analysis

FEM:

Finite Element Method

FFD:

Full Factorial Design

FPA:

Flower Pollination Algorithm

FSW:

Friction Stir Welding

GA:

Genetic Algorithm

GPR:

Gaussian Process Regression

GRA:

Grey Relation Analysis

GTM:

Grey Taguchi Method

GWO:

Grey Wolf Optimizer

HAZ:

Heat Affected Zone

HSTA:

Heat Transfer Search Algorithm

IS:

Impact Strength

KNN:

K-Nearest Neighbours

LARS:

Least Angle Regression

LR:

Lasso Regression

MIG:

Metal Inert Gas

ML:

Machine Learning

MLR:

Multi-Linear Regression

MOGA:

Multi Objective Genetic Algorithm

MOORA:

Multi Objective Optimization by Ratio Analysis

MOPSO:

Multi Objective Particle Swarm Optimization

MS:

Mild Steel

MV:

Machine Vision

NN:

Neural Network

NSGA-II:

Non-dominated Sorting Genetic Algorithm-II

OA:

Orthogonal Array

PCA:

Principal Component Analysis

PSO:

Particle Swarm Optimization

RA:

Regression Analysis

RR:

Ridge Regression

RSM:

Response Surface Methodology

SA:

Simulated Annealing

SS:

Stainless Steel

SVM:

Support Vector Machine

TIG:

Tungsten Inert Gas

TLBO:

Teaching-Learning-Based Optimization

TM:

Taguchi Methodology

TOPSIS:

Technique for Order Preference by Similarity to Ideal Solution

XFEM:

Extended Finite Element Method

µ-H:

Micro hardness

A B :

Bevel angle

A G :

Groove angle

A t :

Torch angle (referred as tool pin angle in FSW)

BH:

Bead height

BS:

Bending strength

BW:

Bead width

BBW:

Back bead width

d np :

Nozzle-to-plate distance

d sp :

Shoulder plunge depth

d f :

Filler diameter (also referred to as electrode diameter)

D S :

Shoulder diameter

D tp :

Tool pin diameter

DOP:

Depth of penetration (also referred to as bead penetration)

EEL:

Electrode extension length

f :

Feed rate in FSW

f p :

Pulse frequency

f T :

Table feed rate

f w :

Wire feed rate

F a :

Axial force

F G :

Gas flow rate

FMT:

Type of filler material

H:

Hardness

I :

Welding current

I b :

Base current

I P :

Peak current

I rms :

RMS current

l a :

rc length (also referred to as arc gap)

N :

Rotational speed

P :

Power

P L :

Laser power

PPV:

Pulse peak variation

S w :

Welding speed (referred to as traverse speed in FSW)

T :

Pre-heat temperature

t :

Plate or workpiece thickness

V :

Voltage

V a :

Arc voltage

V b :

Back-ground voltage

V OC :

Open circuit voltage

V rms :

RMS voltage

T on :

Pulse-on time

TPG:

Tool pin geometry

UTS:

Ultimate tensile strength

YS:

Yield Strength

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  1. Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, India

    Kanak Kalita & Dinesh Burande

  2. Department of Mechanical Engineering, Sikkim Manipal Institute of Technology, Rangpo, India

    Ranjan Kumar Ghadai

  3. Production Engineering Department, Jadavpur University, Kolkata, India

    Shankar Chakraborty

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Kalita, K., Burande, D., Ghadai, R.K. et al. Finite Element Modelling, Predictive Modelling and Optimization of Metal Inert Gas, Tungsten Inert Gas and Friction Stir Welding Processes: A Comprehensive Review. Arch Computat Methods Eng 30, 271–299 (2023). https://doi.org/10.1007/s11831-022-09797-6

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  • DOI: https://doi.org/10.1007/s11831-022-09797-6

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