The present study is aimed to analyze the effect of process parameters on the qualities of the Friction Stir Welded AZ31B Mg Alloy. Response Surface Methodology based Grey Relation Analysis technique was used to multi-optimization of the response parameters such as tensile strength (TS), percentage elongation (El), microhardness (MH), and impact strength (IMP). The mathematical models for response parameters were developed by considering tool rotational speed (RS), tool shoulder diameter (SD), and welding speed (WS) as process parameters. ANOVA (Analysis of Variance) was performed to check the adequacy of the formulated mathematical model and figure out the significant parameters. The results revealed that RS of 950 rpm, WS of 150 mm/min, and SD of 11 mm are the optimal process parameters for optimum response parameters (TS of 157.8700 MPa, IMP of 4.3001 Joule, MH of 84.1335 Hv, and El of 10.0071%). WS is the most significant factor, followed by RS and SD. The grain growth was observed in thermo-mechanically affected zone (TMAZ). The fracture analysis indicated that crack had initiated from the bottom of the centerline in the welded zone and propagated towards the advancing side.
MordikeBLEbertT.Magnesium properties - applications - potential. Mater Sci Eng A2001; 302: 37–45. doi:10.1016/S0921-5093(00)01351-4
2.
ChengYLiang QinT, Wei Wang H, Min, et al. Comparison of corrosion behaviors of AZ31, AZ91, AM60 and ZK60 magnesium alloys. Trans Nonferrous Met Soc China (English Ed2009; 19: 517–524. doi:10.1016/S1003-6326(08)60305-2
YangZLiJPZhangJX, et al. Review on research and development of magnesium alloys. Acta Metall Sin (English Lett2008; 21: 313–328. doi:10.1016/S1006-7191(08)60054-X
5.
HaoKWangHGaoM, et al. Laser welding of AZ31B magnesium alloy with beam oscillation. J Mater Res Technol2019; 8: 3044–3053. doi:10.1016/j.jmrt.2019.04.024
6.
CaoXJahaziMImmarigeonJP, et al. A review of laser welding techniques for magnesium alloys. J Mater Process Technol2006; 171: 188–204. doi:10.1016/j.jmatprotec.2005.06.068
7.
AlvesEPNetoFPAnCY.Welding of AA1050 aluminum with AISI 304 stainless steel by rotary friction welding process. JATM2010; 2: 301–306. doi:10.5028/jatm 2010.02037110
8.
OrłowskaMOlejnikLCampanellaD, et al. Application of linear friction welding for joining ultrafine grained aluminium. J Manuf Process2020; 56: 540–549. doi:10.1016/j.jmapro.2020.05.012
9.
KarolczukACarpinteriARobakG, et al. Fatigue failure analysis of three-layer Zr–Ti/Zr–steel composite plates: an insight into the evolution of cracks initiated at the interfaces. Arch Civ Mech Eng2020; 20: 1–13. DOI:doi:10.1007/s43452-020-00145-8.
10.
BöhmMKowalskiM.Fatigue life estimation of explosive cladded transition joints with the use of the spectral method for the case of a random sea state. Mar Struct2020; 71(December : 2019. DOI:doi:10.1016/j.marstruc.2020.102739.
11.
LiWVairisAPreussM, et al. Linear and Rotary Friction Welding Review Linear and Rotary Friction Welding Review 2016; 6608. doi:10.1080/09506608.2015.1109214
12.
MishraRSMaZY.Friction stir welding and processing. Mater Sci Eng R Reports2005; 50: 1–78. doi:10.1016/j.mser.2005.07.001
13.
GrujicicMArakereGPanduranganB, et al. Process modeling of Ti-6Al-4V linear friction welding (LFW. ). 2012. 21(October):2011–2023. doi:10.1007/s11665-011-0097-8
14.
BouarroudjEChikhSAbdiS, et al. Thermal analysis during a rotational friction welding. Appl Therm Eng2017; 110: 1543–1553. doi:10.1016/j.applthermaleng.2016.09.067
15.
CarvalhoGGalvãoIMendesR, et al. Friction stir welding and explosive welding of aluminum/copper: process analysis. Mater Manuf Process2019; 34: 1243–1250. doi:10.1080/10426914.2019.1644452
16.
PadmanabanGBalasubramanianV.An experimental investigation on friction stir welding of AZ31B magnesium alloy. 2010. 111–121. doi:10.1007/s00170-009-2368-1
17.
MironovSOnumaTSatoYS, et al. Microstructure evolution during friction-stir welding of AZ31 magnesium alloy. Acta Mater2015; 100: 301–312. doi:10.1016/j.actamat.2015.08.066
18.
SinghUKDubeyAK. Study of joining performance of dissimilar Mg alloys in friction stir welding. 2020. doi:10.1177/0954406220959096
19.
SenthilSMParameshwaranRRagu NathanS, et al. 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 Optim2020; doi:10.1007/s00158-020-02542-2
20.
RambabuGNaikDBRaoCHV, et al. Optimization of friction stir welding parameters for improved corrosion resistance of AA2219 aluminum alloy joints. Def Technol2015; 11: 330–337. doi:10.1016/j.dt.2015.05.003
21.
GopalPMPrakashKS.Minimization of cutting force, temperature and surface roughness through GRA, TOPSIS and taguchi techniques in end milling of Mg hybrid MMC. Measurement2018; 116 November 2016; : 178–192. doi:10.1016/j.measurement.2017.11.011
22.
KasmanŞ.Multi-response optimization using the taguchi-based grey relational analysis: a case study for dissimilar friction stir butt welding of AA6082-T6/AA5754-H111. Int J Adv Manuf Technol2013; 68: 795–804. doi:10.1007/s00170-012-4720-0
23.
DeepanrajBSivasubramanianVJayarajS.Multi-response optimization of process parameters in biogas production from food waste using taguchi – grey relational analysis. Energy Convers Manag2017; 141: 429–438. doi:10.1016/j.enconman.2016.12.013
24.
WakchaureKNThakurAGGadakhV, et al. Multi-Objective optimization of friction stir welding of aluminium alloy 6082-T6 using hybrid Taguchi-Grey relation Analysis- ANN method. Mater Today Proc2018; 5: 7150–7159. doi:10.1016/j.matpr.2017.11.380
25.
SudhagarSSakthivelMMathewPJ, et al. A multi criteria decision making approach for process improvement in friction stir welding of aluminium alloy. Measurement2017; 108: 1–8. doi:10.1016/j.measurement.2017.05.023
26.
PadmanabanGBalasubramanianV.Prediction of tensile strength and optimization of process parameters for friction stir welded AZ31B magnesium alloy. 2010. 224:1519–1528. doi:10.1243/09544054JEM1667
27.
SingarapuUAdepuKArumalleSR.Influence of tool material and rotational speed on mechanical properties of friction stir welded AZ31B magnesium alloy. J Magnes Alloy2015; 3: 335–344. doi:10.1016/j.jma.2015.10.001
28.
SahuPKPalSPalSK, et al. Influence of plate position, tool offset and tool rotational speed on mechanical properties and microstructures of dissimilar Al/Cu friction stir welding joints. J Mater Process Technol2016; 235: 55–67. doi:10.1016/j.jmatprotec.2016.04.014
29.
ComminLDumontMMasseJE, et al. Friction stir welding of AZ31 magnesium alloy rolled sheets: Influence of processing parameters. Acta Mater2009; 57: 326–334. doi:10.1016/j.actamat.2008.09.011
30.
CaoXJahaziMAndCX, et al. Effect of tool rotational speed and probe length on lap joint quality of a friction stir welded magnesium alloy. Mater Des2011; 32: 1–11. doi:10.1016/j.matdes.2010.06.048
31.
LiWYFuTHütschL, et al. Effects of tool rotational and welding speed on microstructure and mechanical properties of bobbin-tool friction-stir welded Mg AZ31. Mater Des2014; 64: 714–720. doi:10.1016/j.matdes.2014.07.023
32.
IslamMBuijkARais-RohaniM, et al. Advances in engineering software process parameter optimization of lap joint fillet weld based on FEM – RSM – GA integration technique. Adv Eng Softw2015; 79: 127–136. doi:10.1016/j.advengsoft.2014.09.007
33.
PeriyasamyPMohanBBalasubramanianV, et al. Multi-objective optimization of friction stir welding parameters using desirability approach to join Al/SiC p metal matrix composites. Trans Nonferrous Met Soc China2013; 23: 942–955. doi:10.1016/S1003-6326(13)62551-0
34.
ElsenSRRameshT.Optimization to develop multiple response hardness and compressive strength of zirconia reinforced alumina by using RSM and GRA. Int J Refract Met Hard Mater2015; 52: 159–164. doi:10.1016/j.ijrmhm.2015.06.007
35.
BezerraMASantelliREOliveiraEP, et al. Response surface methodology (RSM) as a tool for optimization in analytical chemistry. Talanta2008; 76: 965–977. doi:10.1016/j.talanta.2008.05.019
36.
KumarAYadavPKKhuranaM. Modelling and Optimization of Gas Metal Arc Welding Using Response Surface Methodology. 2016. (April). doi:10.16962/elkapj/si.arimpie-2016.50
37.
KumarPKanchanSSukhomayK, et al. Hybrid fuzzy-grey-Taguchi based multi weld quality optimization of Al/Cu dissimilar friction stir welded joints. Adv Manuf2016; 4: 237–247. doi:10.1007/s40436-016-0151-8
38.
Ajith Arul DanielSPugazhenthiRKumarR, et al. Multi objective prediction and optimization of control parameters in the milling of aluminium hybrid metal matrix composites using ANN and taguchi -grey relational analysis. Def Technol2019; 15: 545–556. doi:10.1016/j.dt.2019.01.001
39.
KabnureBBShindeVDKolhapureRR.Optimization to develop multiple response microstructure and hardness of ductile iron casting by using GRA. J Inst Eng India Ser D2018; 99: 235–243. doi:10.1007/s40033-018-0161-8
40.
RouniyarAKShandilyaP.Optimization of process parameters in magnetic field assisted powder mixed EDM of aluminium 6061 alloy. Proc Inst Mech Eng Part C J Mech Eng Sci2020; doi:DOI:10.1177/0954406220959108.
41.
BisariaHShandilyaP.Surface integrity of Ni-Rich NiTi shape memory alloy at optimized level of wire electric discharge machining parameters. J of Materi Eng and Perform2019; 28: 7663–7675. doi:10.1007/s11665-019-04477-2
CrimpMAVedulaKM.The relationship between cooling rate, grain size and the mechanical behavior of B2 FeAl alloys. Mater Sci Eng A1993; 165: 29–34. doi:10.1016/0921-5093(93)90623-M
44.
UgenderSKumarAReddyAS.Microstructure and mechanical properties of AZ31B magnesium alloy by friction stir welding. Procedia Mater Sci2014; 6: 1600–1609. doi:10.1016/j.mspro.2014.07.143
45.
KasmanŞYenierZ.Analyzing dissimilar friction stir welding of AA5754/AA7075. Int J Adv Manuf Technol2014; 70: 145–156. doi:10.1007/s00170-013-5256-7
46.
Motalleb-NejadPSaeidTHeidarzadehA, et al. Effect of tool pin profile on microstructure and mechanical properties of friction stir welded AZ31B magnesium alloy. Mater Des2014; 59: 221–226. doi:10.1016/j.matdes.2014.02.068
47.
UgenderS.Influence of tool pin profile and rotational speed on the formation of friction stir welding zone in AZ31 magnesium alloy. J Magnes Alloy2018; 6: 205–213. doi:10.1016/j.jma.2018.05.001
