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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

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

References

  1. Ghosh A, Mallik AK (1986) Manufacturing Science. Ellis Horwood, 1986

  2. Lancaster JF (1984) The physics of welding. Phys Technol 15:73

    Article  Google Scholar 

  3. Groover MP (2014) Principles of Modern Manufacturing: SI Version. John Wiley & Sons, India

    Google Scholar 

  4. Guo J (2015) Solid state welding processes in manufacturing.Handb Manuf Eng Technol569–592

  5. da Cunha TV, Bohórquez CEN (2015) Ultrasound in arc welding: a review. Ultrasonics 56:201–209

    Article  Google Scholar 

  6. Maruyama T (2003) Arc welding technology for dissimilar joints. Weld Int 17:276–281

    Article  Google Scholar 

  7. Chen Y, Chen S, Li L (2010) Influence of interfacial reaction layer morphologies on crack initiation and propagation in Ti/Al joint by laser welding-brazing. Mater Des 31:227–233

    Article  Google Scholar 

  8. Fang Y, Jiang X, Mo D, Zhu D, Luo Z (2019) A review on dissimilar metals’ welding methods and mechanisms with interlayer. Int J Adv Manuf Technol 102:2845–2863

    Article  Google Scholar 

  9. Yu W, Yu W, Zhao H, Huang Z, Chen X, Aman Y, Li S, Zhai H, Guo Z, Xiong S (2017) Microstructure evolution and bonding mechanism of Ti2SnC-Ti6Al4V joint by using Cu pure foil interlayer. Mater Charact 127:53–59

    Article  Google Scholar 

  10. Kuang B, Shen Y, Chen W, Yao X, Xu H, Gao J, Zhang J (2015) The dissimilar friction stir lap welding of 1A99 Al to pure Cu using Zn as filler metal with “pinless” tool configuration. Mater Des 68:54–62

    Article  Google Scholar 

  11. Shiue RK, Wu SK, Chan CH, Huang CS (2006) Infrared brazing of Ti-6Al-4V and 17 – 4 PH stainless steel with a nickel barrier layer. Metall Mater Trans A 37:2207–2217

    Article  Google Scholar 

  12. Kato H, Shibata M, Yoshikawa K (1986) Diffusion welding of Ti/Ti and Ti/stainless steel rods under phase transformation in air. Mater Sci Technol 2:405–409

    Article  Google Scholar 

  13. Kundu S, Bhola SM, Mishra B, Chatterjee S (2014) Structure and properties of solid state diffusion bonding of 17-4PH stainless steel and titanium. Mater Sci Technol 30:248–256

    Article  Google Scholar 

  14. Ghosh M, Chatterjee S (2002) Characterization of transition joints of commercially pure titanium to 304 stainless steel. Mater Charact 48:393–399

    Article  Google Scholar 

  15. Kimura M, Iijima T, Kusaka M, Kaizu K, Fuji A (2016) Joining phenomena and tensile strength of friction welded joint between Ti-6Al-4V titanium alloy and low carbon steel. J Manuf Process 24:203–211

    Article  Google Scholar 

  16. Akbarimousavi SAA, GohariKia M (2011) Investigations on the mechanical properties and microstructure of dissimilar cp-titanium and AISI 316L austenitic stainless steel continuous friction welds. Mater Des 32:3066–3075

    Article  Google Scholar 

  17. Messler RW Jr (2008) Principles of Welding: Processes, Physics, Chemistry, and Metallurgy. John Wiley & Sons, Weinheim, Germany

    Google Scholar 

  18. Pal K, Pal SK (2011) Effect of pulse parameters on weld quality in pulsed gas metal arc welding: a review. J Mater Eng Perform 20:918–931

    Article  Google Scholar 

  19. Palani PK, Murugan N (2006) Development of mathematical models for prediction of weld bead geometry in cladding by flux cored arc welding. Int J Adv Manuf Technol 30:669–676

    Article  Google Scholar 

  20. Karadeniz E, Ozsarac U, Yildiz C (2007) The effect of process parameters on penetration in gas metal arc welding processes. Mater Des 28:649–656

    Article  Google Scholar 

  21. Short AB (2009) Gas tungsten arc welding of α + β titanium alloys: a review. Mater Sci Technol 25:309–324

    Article  Google Scholar 

  22. Ahmed N (2005) New Developments in Advanced Welding. Taylor & Francis, USA

    Book  Google Scholar 

  23. What is tungsten inert gas (GTAW or TIG), welding? https://www.twi-global.com/technical-knowledge/job-knowledge/tungsten-inert-gas-tig-or-gta-welding-006. Accessed on 19 Aug 2021

  24. Kulkarni A, Dwivedi DK, Vasudevan M (2018) Study of mechanism, microstructure and mechanical properties of activated flux TIG welded P91 steel-P22 steel dissimilar metal joint. Mater Sci Eng A 731:309–323

    Article  Google Scholar 

  25. Sharma P, Dwivedi DK (2019) A-TIG welding of dissimilar P92 steel and 304H austenitic stainless steel: Mechanisms, microstructure and mechanical properties. J Manuf Process 44:166–178

    Article  Google Scholar 

  26. Khalifeh AR, Dehghan A, Hajjari E (2013) Dissimilar joining of AISI 304L/St37 steels by TIG welding process. Acta Metall Sin 26:721–727

    Article  Google Scholar 

  27. Thomas WM (1991) Friction stir butt welding. Int Pat No PCT/GB92/02203

  28. Nandan R, DebRoy T, Bhadeshia H (2008) Recent advances in friction-stir welding-process, weldment structure and properties. Prog Mater Sci 53:980–1023

    Article  Google Scholar 

  29. He X, Gu F, Ball A (2014) A review of numerical analysis of friction stir welding. Prog Mater Sci 65:1–66

    Article  Google Scholar 

  30. Wan L, Huang Y (2018) Friction stir welding of dissimilar aluminum alloys and steels: a review. Int J Adv Manuf Technol 99:1781–1811

    Article  Google Scholar 

  31. Uzun H, Dalle Donne C, Argagnotto A, Ghidini T, Gambaro C (2005) Friction stir welding of dissimilar Al 6013-T4 to X5CrNi18-10 stainless steel. Mater Des 26:41–46

    Article  Google Scholar 

  32. Rodriguez RI, Jordon JB, Allison PG, Rushing T, Garcia L (2015) Microstructure and mechanical properties of dissimilar friction stir welding of 6061-to-7050 aluminum alloys. Mater Des 83:60–65

    Article  Google Scholar 

  33. Fei X, Jin X, Ye Y, Xiu T, Yang H (2016) Effect of pre-hole offset on the property of the joint during laser-assisted friction stir welding of dissimilar metals steel and aluminum alloys. Mater Sci Eng A 653:43–52

    Article  Google Scholar 

  34. Tanaka T, Morishige T, Hirata T (2009) Comprehensive analysis of joint strength for dissimilar friction stir welds of mild steel to aluminum alloys. Scr Mater 61:756–759

    Article  Google Scholar 

  35. Maheshwari N, Choudhary J, Rath A, Shinde D, Kalita K (2021) Finite element analysis and multi-criteria decision-making (MCDM)-based optimal design parameter selection of solid ventilated brake disc. J Inst Eng Ser C 102:349–359

    Article  Google Scholar 

  36. Turner MJ, Clough RW, Martin HC, Topp LJ (1956) Stiffness and deflection analysis of complex structures. J Aeronaut Sci 23:805–823

    MATH  Article  Google Scholar 

  37. Clough RW (1990) Original formulation of the finite element method. Finite Elem Anal Des 7:89–101

    Article  Google Scholar 

  38. A brief review on finite element method. https://engibex.com/a-brief-review-on-finite-element-method. Accessed on 21 August 2021

  39. Yi H-J, Kim J-Y, Yoon J-H, Kang S-S (2011) Investigations on welding residual stress and distortion in a cylinder assembly by means of a 3D finite element method and experiments. J Mech Sci Technol 25:3185–3193

    Article  Google Scholar 

  40. Liu C, Zhang JX, Xue CB (2011) Numerical investigation on residual stress distribution and evolution during multipass narrow gap welding of thick-walled stainless steel pipes. Fusion Eng Des 86:288–295

    Article  Google Scholar 

  41. Buffa G, Ducato A, Fratini L (2013) FEM based prediction of phase transformations during friction stir welding of Ti6Al4V titanium alloy. Mater Sci Eng A 581:56–65

    Article  Google Scholar 

  42. Pashazadeh H, Masoumi A, Teimournezhad J (2013) A study on material flow pattern in friction stir welding using finite element method. Proc Inst Mech Eng Part B J Eng Manuf 227:1453–1466

    Article  Google Scholar 

  43. Shan X, Davies CM, Wangsdan T, O’Dowd NP, Nikbin KM (2009) Thermo-mechanical modelling of a single-bead-on-plate weld using the finite element method. Int J Press Vessel Pip 86:110–121

    Article  Google Scholar 

  44. Bachorski A, Painter MJ, Smailes AJ, Wahab MA (1999) Finite-element prediction of distortion during gas metal arc welding using the shrinkage volume approach. J Mater Process Technol 92:405–409

    Article  Google Scholar 

  45. Zhu W, Xu C, Zeng L (2010) Coupled finite element analysis of MIG welding assembly on auto-body high-strength steel panel and door hinge. Int J Adv Manuf Technol 51:551–559

    Article  Google Scholar 

  46. Farajkhah V, Liu Y (2016) Effect of metal inert gas welding on the behaviour and strength of aluminum stiffened plates. Mar Struct 50:95–110

    Article  Google Scholar 

  47. He K, Yang Q, Xiao D, Li X (2017) Analysis of thermo-elastic fracture problem during aluminium alloy MIG welding using the extended finite element method. Appl Sci 7:69

    Article  Google Scholar 

  48. Zhan XZ, Liu X, Wei Y, Ou W, Chen J, Liu H (2017) Numerical simulation on backward deformation of MIG multi-layer and multi-pass welding of thick Invar alloy. Int J Adv Manuf Technol 92:1001–1012

    Article  Google Scholar 

  49. Lee SH, Kim ES, Park JY, Choi J (2018) Numerical analysis of thermal deformation and residual stress in automotive muffler by MIG welding. J Comput Des Eng 5:382–390

    Google Scholar 

  50. Zhan XH, Wu Y, Kang Y, Liu X, Chen X (2019) Simulated and experimental studies of laser-MIG hybrid welding for plate-pipe dissimilar steel. Int J Adv Manuf Technol 101:1611–1622

    Article  Google Scholar 

  51. Chen F, Wang Y, Sun S, Ma Z, Huang X (2019) Multi-objective optimization of mechanical quality and stability during micro resistance spot welding. Int J Adv Manuf Technol 101:1903–1913

    Article  Google Scholar 

  52. Li C, Chen Z, Gao H, Zhang D, Han X (2021) Numerical simulation of the metal inert gas welding process that considers grain heterogeneity. Proc Inst Mech Eng Part L J Mater Des Appl 235:42–58

    Google Scholar 

  53. Chen Z, Li C, Han X, Gao X, Gao H (2021) Sensitivity analysis of the MIG welding process parameters based on response surface method. J Adhes Sci Technol 35:590–609

    Article  Google Scholar 

  54. Ma M, Lai R, Qin J, Wang B, Liu H, Yi D (2021) Effect of weld reinforcement on tensile and fatigue properties of 5083 aluminum metal inert gas (MIG) welded joint: Experiments and numerical simulations. Int J Fatigue 144:106046

    Article  Google Scholar 

  55. Varghese VJ, Suresh MR, Kumar DS (2013) Recent developments in modeling of heat transfer during TIG welding - a review. Int J Adv Manuf Technol 64:749–754

    Article  Google Scholar 

  56. Ahmad AS, Wu Y, Gong H, Nie L (2019) Finite element prediction of residual stress and deformation induced by double-pass TIG welding of Al 2219 plate. Mater (Basel) 12:2251

    Article  Google Scholar 

  57. Asadi P, Alimohammadi S, Kohantorabi O, Fazil A, Akbari M (2020) Effects of material type, preheating and weld pass number on residual stress of welded steel pipes by multi-pass TIG welding (C-Mn, SUS304, SUS316). Therm Sci Eng Prog 16:100462

    Article  Google Scholar 

  58. Bag S, De A (2008) Development of a three-dimensional heat-transfer model for the gas tungsten arc welding process using the finite element method coupled with a genetic algorithm-based identification of uncertain input parameters. Metall Mater Trans A 39:2698–2710

    Article  Google Scholar 

  59. Casalino G, Michele D, Perulli P (2020) FEM model for TIG hybrid laser butt welding of 6 mm thick austenitic to martensitic stainless steels. Procedia CIRP 88:116–121

    Article  Google Scholar 

  60. Deng D (2009) FEM prediction of welding residual stress and distortion in carbon steel considering phase transformation effects. Mater Des 30:359–366

    Article  Google Scholar 

  61. del Coz Díaz JJ, Rodríguez PM, Nieto PJG, Castro-Fresno D (2010) Comparative analysis of TIG welding distortions between austenitic and duplex stainless steels by FEM. Appl Therm Eng 30:2448–2459

    Article  Google Scholar 

  62. Ganesh KC, Vasudevan M, Balasubramanian KR, Chandrasekhar N, Mahadevan S, Vasantharaja P, Jayakumar T (2014) Modeling, prediction and validation of thermal cycles, residual stresses and distortion in type 316 LN stainless steel weld joint made by TIG welding process. Procedia Eng 86:767–774

    Article  Google Scholar 

  63. Guimarães PB, Pedrosa PMA, Yadava YP, Barbosa JMA, Filho AVS, Ferreira RAS (2013) Determination of residual stresses numerically obtained in ASTM AH36 steel welded by TIG process. Mater Sci Appl 4:268–274

    Google Scholar 

  64. Huang H, Yin X, Feng Z, Ma N (2019) Finite element analysis and in-situ measurement of out-of-plane distortion in thin plate TIG welding. Mater (Basel) 12:141

    Article  Google Scholar 

  65. Javadi Y (2014) Investigation of clamping effect on the welding residual stress and deformation of monel plates by using the ultrasonic stress measurement and finite element method. J Press Vessel Technol 137:011501

    Article  Google Scholar 

  66. Zhang J, Yu L, Liu Y, Ma Z, Li H, Liu C, Wu J, Ma J, Li Z (2018) Analysis of the effect of tungsten inert gas welding sequences on residual stress and distortion of CFETR vacuum vessel using finite element simulations. Metals 8:912

    Article  Google Scholar 

  67. Reda R, Magdy M, Rady M (2020) Ti-6Al-4V TIG weld analysis using FEM simulation and experimental characterization. Iran J Sci Technol Trans Mech Eng 44:765–782

    Article  Google Scholar 

  68. Kocherlakota P, Savarimuthu J (2008) Effect of welding conditions on TIG welded AISI 304 stainless steels using FEM and experimental methods. In: Proc. of ASME Pressure Vessels and Piping Conference,Illinois, USA 221–228

  69. Piekarska W, Rek K (2017) Numerical analysis and experimental research on deformation of flat made of TIG welded 0H18N9 steel. Procedia Eng 177:182–187

    Article  Google Scholar 

  70. Wu C, Kim J-W (2018) Analysis of welding residual stress formation behavior during circumferential TIG welding of a pipe. Thin-Walled Struct 132:421–430

    Article  Google Scholar 

  71. Ikechukwu O (2019) Finite element analysis of tungsten inert gas welding temperatures on the stress profiles of AIS1 1020 low carbon steel plate. Int J Eng Technol 5:50–58

    Google Scholar 

  72. Kumar P, Kumar R, Arif A, Veerababu M (2020) Investigation of numerical modelling of TIG welding of austenitic stainless steel (304L). Mater Today Proc 27:1636–1640

    Article  Google Scholar 

  73. Prabakaran ST, Sakthivel P, Shanmugam M, Satish S, Muniyappan M, Shaisundaram VS (2021) Modelling and experimental validation of TIG welding of Inconel 718. Mater Today Proc 37:1917–1931

    Article  Google Scholar 

  74. Ajri A, Rohatgi N, Shin YC (2020) Analysis of defect formation mechanisms and their effects on weld strength during friction stir welding of Al 6061-T6 via experiments and finite element modeling. Int J Adv Manuf Technol 107:4621–4635

    Article  Google Scholar 

  75. Al-Badour F, Merah N, Shuaib A, Bazoune A (2013) Coupled Eulerian Lagrangian finite element modeling of friction stir welding processes. J Mater Process Technol 213:1433–1439

    Article  Google Scholar 

  76. Al-Badour F, Merah N, Shuaib A, Bazoune A (2014) Thermo-mechanical finite element model of friction stir welding of dissimilar alloys. Int J Adv Manuf Technol 72:607–617

    Article  Google Scholar 

  77. Ansari MA, Samanta A, Behnagh RA, Ding H (2019) An efficient coupled Eulerian-Lagrangian finite element model for friction stir processing. Int J Adv Manuf Technol 101:1495–1508

    Article  Google Scholar 

  78. Asif MM, Shrikrishana KA, Sathiya P (2015) Finite element modelling and characterization of friction welding on UNS S31803 duplex stainless steel joints. Eng Sci Technol Int J 18:704–712

    Google Scholar 

  79. Buffa G, Ducato A, Fratini L (2011) Numerical procedure for residual stresses prediction in friction stir welding. Finite Elem Anal Des 47:470–476

    Article  Google Scholar 

  80. El-Sayed MM, Shash AY, Abd-Rabou M (2018) Finite element modeling of aluminum alloy AA5083-O friction stir welding process. J Mater Process Technol 252:13–24

    Article  Google Scholar 

  81. Gök K, Aydin M (2013) Investigations of friction stir welding process using finite element method. Int J Adv Manuf Technol 68:775–780

    Article  Google Scholar 

  82. Jain R, Pal SK, Singh SB (2017) Finite element simulation of temperature and strain distribution during friction stir welding of AA2024 aluminum alloy. J Inst Eng Ser C 98:37–43

    Article  Google Scholar 

  83. Jain R, Pal SK, Singh SB (2018) Finite element simulation of pin shape influence on material flow, forces in friction stir welding. Int J Adv Manuf Technol 94:1781–1797

    Article  Google Scholar 

  84. Lepore M, Carlone P, Berto F, Sonne MR (2017) A FEM based methodology to simulate multiple crack propagation in friction stir welds. Eng Fract Mech 184:154–167

    Article  Google Scholar 

  85. Malik V, Sanjeev NK, Hebbar HS, Kailas SV (2014) Investigations on the effect of various tool pin profiles in friction stir welding using finite element simulations. Procedia Eng 97:1060–1068

    Article  Google Scholar 

  86. Myung D, Noh W, Kim J-H et al (2021) Probing the mechanism of friction stir welding with ALE based finite element simulations and its application to strength prediction of welded aluminum. Met Mater Int 27:650–666

    Article  Google Scholar 

  87. Wan ZY, Zhang Z, Zhou X (2017) Finite element modeling of grain growth by point tracking method in friction stir welding of AA6082-T6. Int J Adv Manuf Technol 90:3567–3574

    Article  Google Scholar 

  88. Chen C, Kovacevic R (2004) Thermomechanical modelling and force analysis of friction stir welding by the finite element method. Proc Inst Mech Eng Part C J Mech Eng Sci 218:509–519

    Article  Google Scholar 

  89. Buffa G, Hua J, Shivpuri R, Fratini L (2006) Design of the friction stir welding tool using the continuum based FEM model. Mater Sci Eng A 419:381–388

    Article  Google Scholar 

  90. Sadeghi S, Najafabadi MA, Javadi Y, Mohammadisefat M (2013) Using ultrasonic waves and finite element method to evaluate through-thickness residual stresses distribution in the friction stir welding of aluminum plates. Mater Des 52:870–880

    Article  Google Scholar 

  91. Almanar IP, Shaari MSB, Jaffarullah MS et al (2014) Temperature distribution in friction stir welding using finite element method. Int J Mech Mechatronics Eng 8:1699–1704

    Google Scholar 

  92. Jain R, Pal SK, Singh SB (2016) A study on the variation of forces and temperature in a friction stir welding process: a finite element approach. J Manuf Process 23:278–286

    Article  Google Scholar 

  93. Pashazadeh H, Gheisari Y, Hamedi M (2016) Statistical modeling and optimization of resistance spot welding process parameters using neural networks and multi-objective genetic algorithm. J Intell Manuf 27:549–559

    Article  Google Scholar 

  94. Paulo RMF, Carlone P, Paradiso V, Valente RAF, Teixeira-Dias F (2017) Prediction of friction stir welding effects on AA2024-T3 plates and stiffened panels using a shell-based finite element model. Thin-Walled Struct 120:297–306

    Article  Google Scholar 

  95. Bühr C, Ahmad B, Colegrove PA, McAndrew AR, Guo H, Zhang X (2018) Prediction of residual stress within linear friction welds using a computationally efficient modelling approach. Mater Des 139:222–233

    Article  Google Scholar 

  96. Turkan M, Karakas Ö (2021) Two different finite element models investigation of the plunge stage in joining AZ31B magnesium alloy with friction stir welding. SN Appl Sci 3:1–14

    Article  Google Scholar 

  97. Barton RR (1994) Metamodeling: a state of the art review. In: Proceedings of Winter Simulation Conference. Lake Buena Vista, USA 237–244

  98. Wang GG, Shan S (2007) Review of metamodeling techniques in support of engineering design optimization. J Mech Des Trans ASME 129:370–380

    Article  Google Scholar 

  99. Simpson TW, Poplinski JD, Koch PN, Allen JK (2001) Metamodels for computer-based engineering design: survey and recommendations. Eng Comput 17:129–150

    MATH  Article  Google Scholar 

  100. Kalita K, Dey P, Haldar S (2019) Search for accurate RSM metamodels for structural engineering. J Reinf Plast Compos 38:995–1013

    Article  Google Scholar 

  101. Song XG, Jung JH, Son HJ, Park JH, Lee KH, Park YC (2010) Metamodel-based optimization of a control arm considering strength and durability performance. Comput Math with Appl 60:976–980

    MATH  Article  Google Scholar 

  102. Srivastava S, Garg RK (2017) Process parameter optimization of gas metal arc welding on IS: 2062 mild steel using response surface methodology. J Manuf Process 25:296–305

    Article  Google Scholar 

  103. Grum J, Slabe JM (2004) The use of factorial design and response surface methodology for fast determination of optimal heat treatment conditions of different Ni-Co-Mo surfaced layers. J Mater Process Technol 155:2026–2032

    Article  Google Scholar 

  104. Gunaraj V, Murugan N (1999) Application of response surface methodology for predicting weld bead quality in submerged arc welding of pipes. J Mater Process Technol 88:266–275

    Article  Google Scholar 

  105. Kiaee N, Aghaie-Khafri M (2014) Optimization of gas tungsten arc welding process by response surface methodology. Mater Des 54:25–31

    Article  Google Scholar 

  106. Zhang P, Jin Y-F, Yin Z-Y, Yang Y (2020) Random forest based artificial intelligent model for predicting failure envelopes of caisson foundations in sand. Appl Ocean Res 101:102223

    Article  Google Scholar 

  107. Bishop CM (2013) Model-based machine learning. Philos Trans R Soc A Math Phys Eng Sci 371:20120222

    MathSciNet  MATH  Article  Google Scholar 

  108. Eren B, Guvenc MA, Mistikoglu S (2021) Artificial intelligence applications for friction stir welding: A review. Met Mater Int 27:193–219

    Article  Google Scholar 

  109. Nagesh DS, Datta GL (2010) Genetic algorithm for optimization of welding variables for height to width ratio and application of ANN for prediction of bead geometry for TIG welding process. Appl Soft Comput 10:897–907

    Article  Google Scholar 

  110. Dewan MW, Huggett DJ, Liao TW, Wahab MA, Okeil AM (2016) Prediction of tensile strength of friction stir weld joints with adaptive neuro-fuzzy inference system (ANFIS) and neural network. Mater Des 92:288–299

    Article  Google Scholar 

  111. Vapnik V (1963) Pattern recognition using generalized portrait method. Autom Remote Control 24:774–780

    Google Scholar 

  112. Chen J, Wang T, Gao X, Wei L (2018) Real-time monitoring of high-power disk laser welding based on support vector machine. Comput Ind 94:75–81

    Article  Google Scholar 

  113. Ganjigatti JP, Pratihar DK, Choudhury AR (2007) Global versus cluster-wise regression analyses for prediction of bead geometry in MIG welding process. J Mater Process Technol 189:352–366

    Article  Google Scholar 

  114. Khanna P, Maheshwari S (2018) Development of mathematical models for prediction and control of weld bead dimensions in MIG welding of stainless steel 409 M. Mater Today Proc 5:4475–4488

    Article  Google Scholar 

  115. Koli Y, Yuvaraj N, Aravindan S (2020) Multi-response mathematical modeling for prediction of weld bead geometry of AA6061-T6 using response surface methodology. Trans Indian Inst Met 73:645–666

    Article  Google Scholar 

  116. Kumar S, Singh R (2019) Optimization of process parameters of metal inert gas welding with preheating on AISI 1018 mild steel using grey based Taguchi method. Measurement 148:106924

    Article  Google Scholar 

  117. Pandit M, Sood S, Mishra P, Khanna P (2021) Mathematical analysis of the effect of process parameters on angular distortion of MIG welded stainless steel 202 plates by using the technique of response surface Methodology. Mater Today Proc 41:1045–1054

    Article  Google Scholar 

  118. Prabhu R, Alwarsamy T (2017) Effect of process parameters on ferrite number in cladding of 317L stainless steel by pulsed MIG welding. J Mech Sci Technol 31:1341–1347

    Article  Google Scholar 

  119. Shahabi H, Kolahan F (2015) Regression modeling of welded joint quality in gas metal arc welding process using acoustic and electrical signals. Proc Inst Mech Eng Part B J Eng Manuf 229:1711–1721

    Article  Google Scholar 

  120. Baloyi P, Akinlabi SA, Madushele N, Adedeji PA, Hassan S, Mkoko Z, Akinlabi E (2021) Two-staged technique for determining ultimate tensile strength in MIG welding of mild steel. Mater Today Proc 44:1227–1234

    Article  Google Scholar 

  121. Chaki S, Shanmugarajan B, Ghosal S, Padmanabham G (2015) Application of integrated soft computing techniques for optimisation of hybrid CO2 laser-MIG welding process. Appl Soft Comput 30:365–374

    Article  Google Scholar 

  122. Martínez RT, Bestard GA, Silva AMA, Alfaro SCA (2021) Analysis of GMAW process with deep learning and machine learning techniques. J Manuf Process 62:695–703

    Article  Google Scholar 

  123. Korra NN, Vasudevan M, Balasubramanian KR (2015) Multi-objective optimization of activated tungsten inert gas welding of duplex stainless steel using response surface methodology. Int J Adv Manuf Technol 77:67–81

    Article  Google Scholar 

  124. Rose AR, Manisekar K, Balasubramanian V, Rajakumar S (2012) Prediction and optimization of pulsed current tungsten inert gas welding parameters to attain maximum tensile strength in AZ61A magnesium alloy. Mater Des 37:334–348

    Article  Google Scholar 

  125. Ghaffarpour M, Kazemi M, Mohammadi Sefat MJ, Aziz A, Dehghani K (2017) Evaluation of dissimilar joints properties of 5083-H12 and 6061-T6 aluminum alloys produced by tungsten inert gas and friction stir welding. Proc Inst Mech Eng Part L J Mater Des Appl 231:297–308

    Google Scholar 

  126. Bacioiu D, Melton G, Papaelias M, Shaw R (2019) Automated defect classification of SS304 TIG welding process using visible spectrum camera and machine learning. NDT E Int 107:102139

    Article  Google Scholar 

  127. Bacioiu D, Melton G, Papaelias M, Shaw R (2019) Automated defect classification of aluminium 5083 TIG welding using HDR camera and neural networks. J Manuf Process 45:603–613

    Article  Google Scholar 

  128. Baskoro AS, Tandian R, Edyanto A, Saragih AS (2016) Automatic tungsten inert gas (TIG) welding using machine vision and neural network on material SS304. In: Proc. Int. Conf. Advanced Computer Science and Information Systems,Indonesia 427–432

  129. Ghanty P, Vasudevan M, Mukherjee DP, Pal NR, Chandrasekhar N, Maduraimuthu V, Bhadhuri AK, Barat P, Raj B (2008) Artificial neural network approach for estimating weld bead width and depth of penetration from infrared thermal image of weld pool. Sci Technol Weld Join 13:395–401

    Article  Google Scholar 

  130. Kesse MA, Buah E, Handroos H, Ayetor GK (2020) Development of an artificial intelligence powered TIG welding algorithm for the prediction of bead geometry for TIG welding processes using hybrid deep learning. Metals 10:451

    Article  Google Scholar 

  131. Kshirsagar R, Jones S, Lawrence J, Tabor J (2019) Prediction of bead geometry using a two-stage SVM-ANN algorithm for automated tungsten inert gas (TIG) welds. J Manuf Mater Process 3:39

    Google Scholar 

  132. Babu N, Karunakaran N, Balasubramanian V (2017) A study to estimate the tensile strength of friction stir welded AA 5059 aluminium alloy joints. Int J Adv Manuf Technol 93:1–9

    Article  Google Scholar 

  133. Ghangas G, Singhal S (2018) Modelling and optimization of process parameters for friction stir welding of armor alloy using RSM and GRA-PCA approach. Mater Res Express 6:26553

    Article  Google Scholar 

  134. Gill A, Dhiman DP, Gulati V, Sharma S (2018) Mathematical modeling of process parameters of friction stir welded aluminium alloy joints using central composite design. Mater Today Proc 5:27865–27876

    Article  Google Scholar 

  135. Jagathesh K, Jenarthanan MP, Babu PD, Chanakyan C (2017) Analysis of factors influencing tensile strength in dissimilar welds of AA2024 and AA6061 produced by friction stir welding (FSW). Aust J Mech Eng 15:19–26

    Article  Google Scholar 

  136. Mohamed MA, Manurung YHP, Berhan MN (2015) Model development for mechanical properties and weld quality class of friction stir welding using multi-objective Taguchi method and response surface methodology. J Mech Sci Technol 29:2323–2331

    Article  Google Scholar 

  137. Sankar BR, Umamaheswarrao P (2017) Modelling and optimisation of friction stir welding on AA6061 Alloy. Mater Today Proc 4:7448–7456

    Article  Google Scholar 

  138. Saeidi M, Manafi B, Besharati Givi MK, Faraji G (2016) Mathematical modeling and optimization of friction stir welding process parameters in AA5083 and AA7075 aluminum alloy joints. Proc Inst Mech Eng Part B J Eng Manuf 230:1284–1294

    Article  Google Scholar 

  139. Zhang H, Liu H (2013) Mathematical model and optimization for underwater friction stir welding of a heat-treatable aluminum alloy. Mater Des 45:206–211

    Article  Google Scholar 

  140. Syah A, Astuti W, Saedon J (2018) Development of prediction system model for mechanical property in friction stir welding using support vector machine (SVM). J Mech Eng 5:216–225

    Google Scholar 

  141. Bhat NN, Kumari K, Dutta S, Pal SK, Pal S (2015) Friction stir weld classification by applying wavelet analysis and support vector machine on weld surface images. J Manuf Process 20:274–281

    Article  Google Scholar 

  142. Das B, Pal S, Bag S (2017) Design and development of force and torque measurement setup for real time monitoring of friction stir welding process. Measurement 103:186–198

    Article  Google Scholar 

  143. Das B, Pal S, Bag S (2017) Torque based defect detection and weld quality modelling in friction stir welding process. J Manuf Process 27:8–17

    Article  Google Scholar 

  144. Sudhagar S, Sakthivel M, Ganeshkumar P (2019) Monitoring of friction stir welding based on vision system coupled with Machine learning algorithm. Measurement 144:135–143

    Article  Google Scholar 

  145. De Filippis LAC, Serio LM, Facchini F, Mummolo G, Ludovico AD (2016) Prediction of the vickers microhardness and ultimate tensile strength of AA5754 H111 friction stir welding butt joints using artificial neural network. Mater (Basel) 9:915

    Article  Google Scholar 

  146. Mia M, Khan MA, Rahman SS, Dhar NR (2017) Mono-objective and multi-objective optimization of performance parameters in high pressure coolant assisted turning of Ti-6Al-4V. Int J Adv Manuf Technol 90:109–118

    Article  Google Scholar 

  147. Ghosh A, Mandal S, Nandi G, Pal PK (2018) Metaheuristic based parametric optimization of TIG welded joint. Trans Indian Inst Met 71:1963–1973

    Article  Google Scholar 

  148. Koilraj M, Sundareswaran V, Vijayan S, Rao SRK (2012) Friction stir welding of dissimilar aluminum alloys AA2219 to AA5083- Optimization of process parameters using Taguchi technique. Mater Des 42:1–7

    Article  Google Scholar 

  149. Bozkurt Y (2012) The optimization of friction stir welding process parameters to achieve maximum tensile strength in polyethylene sheets. Mater Des 35:440–445

    Article  Google Scholar 

  150. Saha A, Mondal SC (2017) Multi-objective optimization of manual metal arc welding process parameters for nano-structured hardfacing material using hybrid approach. Measurement 102:80–89

    Article  Google Scholar 

  151. Ansaripour N, Heidari A, Eftekhari SA (2020) Multi-objective optimization of residual stresses and distortion in submerged arc welding process using genetic algorithm and harmony search. Proc Inst Mech Eng Part C J Mech Eng Sci 234:862–871

    Article  Google Scholar 

  152. Xue-Wu W, Yong M, Xing-sheng G (2019) Multi-objective path optimization for arc welding robot based on discrete DN multi-objective particle swarm optimization. Int J Adv Robot Syst 16: https://doi.org/1729881419879827

  153. Ghosh N, Pal PK, Nandi G (2016) Parametric optimization of MIG welding on 316L austenitic stainless steel by grey-based Taguchi method. Procedia Technol 25:1038–1048

    Article  Google Scholar 

  154. Shih J-S, Tzeng Y-F, Yang J-B (2011) Principal component analysis for multiple quality characteristics optimization of metal inert gas welding aluminum foam plate. Mater Des 32:1253–1261

    Article  Google Scholar 

  155. Jogi BF, Awale AS, Nirantar SR, Bhusare HS (2018) Metal inert gas (MIG) welding process optimization using teaching-learning based optimization (TLBO) algorithm. Mater Today Proc 5:7086–7095

    Article  Google Scholar 

  156. Moghaddam MA, Golmezergi R, Kolahan F (2016) Multi-variable measurements and optimization of GMAW parameters for API-X42 steel alloy using a hybrid BPNN-PSO approach. Measurement 92:279–287

    Article  Google Scholar 

  157. Narwadkar A, Bhosle S (2016) Optimization of MIG welding parameters to control the angular distortion in Fe410WA steel. Mater Manuf Process 31:2158–2164

    Article  Google Scholar 

  158. Sivasakthivel PS, Sudhakaran R (2020) Modelling and optimisation of welding parameters for multiple objectives in pre-heated gas metal arc welding process using nature instigated algorithms. Aust J Mech Eng 18:575–587

    Article  Google Scholar 

  159. Ganjigatti JP, Pratihar DK, RoyChoudhury A (2008) Modeling of the MIG welding process using statistical approaches. Int J Adv Manuf Technol 35:1166–1190

    Article  Google Scholar 

  160. Pal S, Pal SK, Samantaray AK (2008) Artificial neural network modeling of weld joint strength prediction of a pulsed metal inert gas welding process using arc signals. J Mater Process Technol 202:464–474

    Article  Google Scholar 

  161. Rao PS, Gupta OP, Murty SSN, Rao ABK (2009) Effect of process parameters and mathematical model for the prediction of bead geometry in pulsed GMA welding. Int J Adv Manuf Technol 45:496

    Article  Google Scholar 

  162. Sen M, Mukherjee M, Pal TK (2014) Prediction of weld bead geometry for double pulse gas metal arc welding process by regression analysis. In: Proc. of 5th International & 26th All India Manufacturing Technology, Design and Research Conference, India 814–816

  163. Kundu J, Singh H (2016) Friction stir welding: multi-response optimisation using Taguchi-based GRA. Prod Manuf Res 4:228–241

    Google Scholar 

  164. Shinde AP, Deshpande AR, Chinchanikar SS, Kulkarni AP (2017) Evaluation of tensile strength of a butt-welded joint considering the effect of welding parameters using response surface methodology. Mater Today Proc 4:7219–7227

    Article  Google Scholar 

  165. Sahu NK, Sahu AK, Sahu AK (2017) Optimization of weld bead geometry of MS plate (Grade: IS 2062) in the context of welding: a comparative analysis of GRA and PCA-Taguchi approaches. Sādhanā 42:231–244

    MATH  Article  Google Scholar 

  166. Kumaran KS, Raj SON (2018) Optimization of parameters involved in robotic MIG welding process based on quality responses. In: IOP Conference Series: Materials Science and Engineering 402:012016

  167. Meseguer-Valdenebro JL, Portoles A, Matínez-Conesa E (2018) Electrical parameters optimisation on welding geometry in the 6063-T alloy using the Taguchi methods. Int J Adv Manuf Technol 98:2449–2460

    Article  Google Scholar 

  168. Yoganandh J, Kannan T, Babu SPK, Natarajan S (2013) Optimization of GMAW process parameters in austenitic stainless steel cladding using genetic algorithm based computational models. Exp Tech 37:48–58

    Article  Google Scholar 

  169. Ghosh N, Pal PK, Nandi G (2018) Investigation on dissimilar welding of AISI 409 ferritic stainless steel to AISI 316L austenitic stainless steel by using grey based Taguchi method. Adv Mater Process Technol 4:385–401

    Google Scholar 

  170. Ramarao M, King MFL, Sivakumar A, Manikandan V, Vijayakumar M, Subbiah R (2021) Optimizing GMAW parameters to achieve high impact strength of the dissimilar weld joints using Taguchi approach. Mater Today Proc. DOI:https://doi.org/10.1016/J.MATPR.2021.06.137

    Article  Google Scholar 

  171. Korra NN, Balasubramanian KR, Vasudevan M (2015) Optimization of activated tungsten inert gas welding of super duplex alloy 2507 based on experimental results. Proc Inst Mech Eng Part B J Eng Manuf 229:1407–1417

    Article  Google Scholar 

  172. Moi SC, Rudrapati R, Bandyopadhyay A, Pal PK (2019) Design optimization of welding parameters for multi-response optimization in TIG welding using RSM-based grey relational analysis. Advances in Computational Methods in Manufacturing. Springer, pp 193–203

  173. Nagaraju S, Vasantharaja P, Chandrasekhar N, Vasudevan M, Jayakumar T (2016) Optimization of welding process parameters for 9Cr-1Mo steel using RSM and GA. Mater Manuf Process 31:319–327

    Article  Google Scholar 

  174. Sada SO (2020) The use of multi-objective genetic algorithm (MOGA) in optimizing and predicting weld quality. Cogent Eng 7:1741310

    Article  Google Scholar 

  175. Magudeeswaran G, Nair SR, Sundar L, Harikannan N (2014) Optimization of process parameters of the activated tungsten inert gas welding for aspect ratio of UNS S32205 duplex stainless steel welds. Def Technol 10:251–260

    Article  Google Scholar 

  176. Srirangan AK, Paulraj S (2016) Multi-response optimization of process parameters for TIG welding of Incoloy 800HT by Taguchi grey relational analysis. Eng Sci Technol Int J 19:811–817

    Google Scholar 

  177. Mohanavel V, Ravichandran M, Kumar SS (2018) Optimization of tungsten inert gas welding parameters to: Attain maximum impact strength in AA6061 alloy joints using Taguchi Technique. Mater Today Proc 5:25112–25120

    Article  Google Scholar 

  178. Bodkhe SC, Dolas DR (2018) Optimization of activated tungsten inert gas welding of 304L austenitic stainless steel. Procedia Manuf 20:277–282

    Article  Google Scholar 

  179. Skariya PD, Satheesh M, Dhas JER (2018) Optimizing parameters of TIG welding process using grey wolf optimization concerning 15CDV6 steel. Evol Intell 11:89–100

    Article  Google Scholar 

  180. Vijayan D, Rao VS (2018) Process parameter optimization in TIG welding of AISI 4340 low alloy steel welds by genetic algorithm. In: IOP Conference Series: Materials Science and Engineering 390: 12066

  181. Naik AB, Reddy AC (2018) Optimization of tensile strength in TIG welding using the Taguchi method and analysis of variance (ANOVA). Therm Sci Eng Prog 8:327–339

    Article  Google Scholar 

  182. Sivakumar J, Vasudevan M, Korra NN (2020) Systematic welding process parameter optimization in activated tungsten inert gas (A-TIG) welding of inconel 625. Trans Indian Inst Met 73:555–569

    Article  Google Scholar 

  183. Varkey MJ, Sumesh A, Kumar KR (2020) A computational approach in optimizing process parameters influencing the heat input and depth of penetration of tungsten inert gas welding of austenitic stainless steel (AISI 316L) using response surface methodology. Mater Today Proc 24:1199–1209

    Article  Google Scholar 

  184. do Valle Tomaz I, Colaço FHG, Sarfraz S et al (2021) Investigations on quality characteristics in gas tungsten arc welding process using artificial neural network integrated with genetic algorithm. Int J Adv Manuf Technol 113:3569–3583

    Article  Google Scholar 

  185. Natrayan L, Anand R, Kumar SS (2021) Optimization of process parameters in TIG welding of AISI 4140 stainless steel using Taguchi technique. Mater Today Proc 37:1550–1553

    Article  Google Scholar 

  186. Vora J, Patel VK, Srinivasan S et al (2021) Optimization of activated tungsten inert gas welding process parameters using heat transfer search algorithm: With experimental validation using case studies. Met (Basel) 11:981

    Article  Google Scholar 

  187. Khalid M (2019) Process parameters optimization of tungsten inert gas welding by taguchi method. In: Proc. of Advances in Science and Engineering Technology International Conferences, UAE, 1–5

  188. Alkayem NF, Parida B, Pal S (2019) Optimization of friction stir welding process using NSGA-II and DEMO. Neural Comput Appl 31:947–956

    Article  Google Scholar 

  189. Gupta SK, Pandey KN, Kumar R (2018) Artificial intelligence-based modelling and multi-objective optimization of friction stir welding of dissimilar AA5083-O and AA6063-T6 aluminium alloys. Proc Inst Mech Eng Part L J Mater Des Appl 232:333–342

    Google Scholar 

  190. Medhi T, Hussain SAI, Roy BS, Saha SC (2021) An intelligent multi-objective framework for optimizing friction-stir welding process parameters. Appl Soft Comput 104:107190

    Article  Google Scholar 

  191. Pitchipoo P, Muthiah A, Jeyakumar K, Manikandan A (2021) Friction stir welding parameter optimization using novel multi objective dragonfly algorithm. Int J Light Mater Manuf 4:460–467

    Google Scholar 

  192. Rathinasuriyan C, Kumar VSS (2021) Optimisation of submerged friction stir welding parameters of aluminium alloy using RSM and GRA. Adv Mater Process Technol 7:696–709

    Google Scholar 

  193. Senthil SM, Parameshwaran R, Nathan SR et al (2020) A multi-objective optimization of the friction stir welding process using RSM-based-desirability function approach for joining aluminum alloy 6063-T6 pipes. Struct Multidiscip Optim 62:1117–1133

    Article  Google Scholar 

  194. Shojaeefard MH, Behnagh RA, Akbari M, Givi MKB, Farhani F (2013) Modelling and Pareto optimization of mechanical properties of friction stir welded AA7075/AA5083 butt joints using neural network and particle swarm algorithm. Mater Des 44:190–198

    Article  Google Scholar 

  195. Tamjidy M, Baharudin BT, Paslar S, Matori KA, Sulaiman S, Fadaeifard F (2017) Multi-objective optimization of friction stir welding process parameters of AA6061-T6 and AA7075-T6 using a biogeography based optimization algorithm. Mater (Basel) 10:533

    Article  Google Scholar 

  196. Elangovan K, Balasubramanian V, Babu S (2009) Predicting tensile strength of friction stir welded AA6061 aluminium alloy joints by a mathematical model. Mater Des 30:188–193

    Article  Google Scholar 

  197. Elatharasan G, Kumar VSS (2013) An experimental analysis and optimization of process parameter on friction stir welding of AA 6061-T6 aluminum alloy using RSM. Procedia Eng 64:1227–1234

    Article  Google Scholar 

  198. Ghetiya ND, Patel KM (2014) Prediction of tensile strength in friction stir welded aluminium alloy using artificial neural network. Procedia Technol 14:274–281

    Article  Google Scholar 

  199. Ghetiya ND, Patel KM, Kavar AJ (2016) Multi-objective optimization of FSW process parameters of aluminium alloy using Taguchi-based grey relational analysis. Trans Indian Inst Met 69:917–923

    Article  Google Scholar 

  200. Wakchaure KN, Thakur AG, Gadakh V, Kumar A (2018) Multi-objective optimization of friction stir welding of aluminium alloy 6082-T6 using hybrid Taguchi-Grey relation analysis-ANN method. Mater Today Proc 5:7150–7159

    Article  Google Scholar 

  201. Babu KK, Panneerselvam K, Sathiya P, Haq AN, Sundarrajan S, Mastanaiah M, Murthy CVS (2018) Parameter optimization of friction stir welding of cryorolled AA2219 alloy using artificial neural network modeling with genetic algorithm. Int J Adv Manuf Technol 94:3117–3129

    Article  Google Scholar 

  202. Vignesh RV, Padmanaban R (2018) Artificial neural network model for predicting the tensile strength of friction stir welded aluminium alloy AA1100. Mater Today Proc 5:16716–16723

    Article  Google Scholar 

  203. Verma S, Gupta M, Misra JP (2018) Performance evaluation of friction stir welding using machine learning approaches. MethodsX 5:1048–1058

    Article  Google Scholar 

  204. Gomathisankar M, Gangatharan M, Pitchipoo P (2018) A novel optimization of friction stir welding process parameters on aluminum alloy 6061-T6. Mater Today Proc 5:14397–14404

    Article  Google Scholar 

  205. Prabhu SRB, Shettigar AK, Herbert MA, Rao SS (2019) Multi-objective optimization of FSW process variables of aluminium matrix composites using Taguchi-based grey relational analysis. In: Advances in Computational Methods in Manufacturing,Springer,Singapore, 133–144

  206. Mishra D, Gupta A, Raj P, Kumar A, Anwer S, Pal SK, Chakravarty D, Pal S, Chakravarty T, Pal A, Misra P, Misra S (2020) Real time monitoring and control of friction stir welding process using multiple sensors. CIRP J Manuf Sci Technol 30:1–11

    Article  Google Scholar 

  207. Verma S, Misra JP, Popli D (2020) Modeling of friction stir welding of aviation grade aluminium alloy using machine learning approaches. Int J Model Simul. https://doi.org/10.1080/02286203.2020.1803605

    Article  Google Scholar 

  208. Thapliyal S, Mishra A (2021) Machine learning classification-based approach for mechanical properties of friction stir welding of copper. Manuf Lett 29:52–55

    Article  Google Scholar 

  209. Banik A, Saha A, Barma JD et al (2021) Determination of best tool geometry for friction stir welding of AA 6061-T6 using hybrid PCA-TOPSIS optimization method. Measurement 173:108573

    Article  Google Scholar 

  210. Kahhal P, Ghasemi M, Kashfi M, Ghorbani-Menghari H, Kim JH (2022) A multi-objective optimization using response surface model coupled with particle swarm algorithm on FSW process parameters. Sci Rep 12(1):1–20

    Article  Google Scholar 

  211. Prabhu SR, Shettigar A, Herbert MA, Rao SS (2022) Parameter investigation and optimization of friction stir welded AA6061/TiO2 composites through TLBO. Weld World 66(1):93–103

    Article  Google Scholar 

  212. Alam MP, Sinha AN (2022) Optimization of process parameters of friction stir welding using desirability function analysis. Weld Int. https://doi.org/10.1080/09507116.2022.2026745

    Article  Google Scholar 

  213. Kesharwani RK, Panda SK, Pal SK (2014) Multi objective optimization of friction stir welding parameters for joining of two dissimilar thin aluminum sheets. Procedia Mater Sci 6:178–187

    Article  Google Scholar 

  214. Elanchezhian C, Ramnath BV, Venkatesan P et al (2014) Parameter optimization of friction stir welding of AA8011-6062 using mathematical method. Procedia Eng 97:775–782

    Article  Google Scholar 

  215. Vijayan D, Abhishek P (2018) Multi objective process parameters optimization of friction stir welding using NSGA-II. In: IOP Conference Series: Materials Science and Engineering 390: 012087

  216. Gupta SK, Pandey KN, Kumar R (2018) Multi-objective optimization of friction stir welding process parameters for joining of dissimilar AA5083/AA6063 aluminum alloys using hybrid approach. Proc Inst Mech Eng Part L J Mater Des Appl 232:343–353

    Google Scholar 

  217. Jenarthanan MP, Varma CV, Manohar VK (2018) Impact of friction stir welding (FSW) process parameters on tensile strength during dissimilar welds of AA2014 and AA6061. Mater Today Proc 5:14384–14391

    Article  Google Scholar 

  218. Kavitha M, Manickavasagam VM, Sathish T, Gugulothu B, Kumar AS, Karthikeyan S, Subbiah R (2021) Parameters optimization of dissimilar friction stir welding for AA7079 and AA8050 through RSM. Adv Mater Sci Eng Article ID 9723699. https://doi.org/10.1155/2021/9723699

  219. Seshu Kumar GSV, Anshuman K, Rajesh S, Raju Chekuri RB, Ramakotaiah K (2022) Optimization of FSW process parameters for welding dissimilar 6061 and 7075 Al alloys using Taguchi design approach. Int J Nonlinear Anal Appl 13(1):1011–1022

    Google Scholar 

  220. Rajesh PV, Gupta KK, Čep R, Ramachandran M, Kouřil K, Kalita K (2022) Optimizing friction stir welding of dissimilar grades of aluminum alloy using WASPAS. Materials 15(5):1715

    Article  Google Scholar 

  221. Sasikala G, Jothiprakash VM, Pant B, Subalakshmi R, Thirumal Azhagan M, Arul K, Praveen Kumar S (2022) Optimization of process parameters for friction stir welding of different aluminum alloys AA2618 to AA5086 by Taguchi method. Adv Mater Sci Eng Article ID 3808605. https://doi.org/10.1155/2022/3808605

  222. Bhushan RK, Sharma D (2022) Optimization of friction stir welding parameters to maximize hardness of AA6082/Si3N4 and AA6082/SiC composites joints. Silicon 14(2):643–661

    Article  Google Scholar 

  223. Alberg H, Berglund D (2003) Comparison of plastic, viscoplastic, and creep models when modelling welding and stress relief heat treatment. Comput Methods Appl Mech Eng 192:5189–5208

    MATH  Article  Google Scholar 

  224. Fachinotti VD, Alberto C (2003) Constitutive models of steel under continuous casting conditions. J Mater Process Technol 135:30–43

    Article  Google Scholar 

  225. Anca A, Alberto C, José R, Fachinotti VD (2011) Finite element modeling of welding processes. Appl Math Model 35:688–707

    MathSciNet  MATH  Article  Google Scholar 

  226. Furtado C, Pereira LF, Tavares RP, Salgado M, Otero F, Catalanotti G, Arteiro A, Bessa MA, Camanho PP (2021) A methodology to generate design allowables of composite laminates using machine learning. Int J Solids Struct 233:111095

    Article  Google Scholar 

  227. Dey S, Mukhopadhyay T, Adhikari S (2017) Metamodel based high-fidelity stochastic analysis of composite laminates: A concise review with critical comparative assessment. Compos Struct 171:227–250

    Article  Google Scholar 

  228. Kalita K, Chakraborty S, Madhu S, Ramachandran M, Gao XZ (2021) Performance analysis of radial basis function metamodels for predictive modelling of laminated composites. Materials 14(12):3306

    Article  Google Scholar 

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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 (2022). https://doi.org/10.1007/s11831-022-09797-6

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Keywords

  • Welding
  • MIG
  • TIG
  • FSW
  • FEM
  • Optimization