Thermo-mechanical Modelling of Friction Stir Welding Process

Xiaocong He

Innovative Manufacturing Research Centre,Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming,650500, P. R. China

hhxxcc@yahoo.co.uk

Keywords: Friction stir welding; Thermo-mechanical modelling; Material flow; temperature

distribution.

Abstract. Friction stir welding (FSW) is a solid-state welding process where no gross melting of

the material being welded takes place. Numerical modelling of the FSW process can provide

realistic prediction of the thermo-mechanical behaviour of the process. Latest literature relating to

finite element analysis (FEA) of thermo-mechanical behaviour of FSW process is reviewed in this

paper. The recent development in thermo-mechanical modelling of FSW process is described with

particular reference to two major factors that influence the performance of FSW joints: material

flow and temperature distribution. The main thermo-mechanical modelling used in FSW process are

discussed and illustrated with brief case studies from the literature.

Introduction

Due to the need to design lightweight structures and the increased use of lightweight metal sheets in

different industries, some joining techniques have drawn more attention in recent years because

they can join advanced metal sheets that are hard to weld [1-3].

Friction stir welding (FSW) is a relatively new solid-state fastening method which is suitable

for joining advanced lightweight metal sheets that are hard to weld. FSW is a highly complex

process. Consequently, the properties of FSW have been the subject of a considerable amount of

experimental and numerical studies [4-10]. Mishra and Ma [11] give a very comprehensive review

both on the FSW and friction stir processing. Nandan et al. [12] give a very comprehensive review

on the process, structure and properties of FSW and Threadgill et al. [13] give a very

comprehensive review on FSW of aluminum alloys.

The behavior of FSW joints is not only influenced by the geometry of the tools and the joints

but also by different process parameters. The increasing complex geometry and their three

dimensional nature combine to increase the difficulty of obtaining an overall system of governing

equations for predicting the behavior of the FSW joints. The experiments are often time consuming

and costly. To overcome these problems, the finite element analysis (FEA) is frequently used since

2000s.

For having a knowledge of the recent progress in FEA of FSW process, published work in

recent years is reviewed in this paper, in terms of modeling technique, tool design and process

parameters.

Material flow

Material flow plays a fundamental role in FSW since it determines the effectiveness of the joints.

Solid mechanics-based FE models were developed by Zhang et al. [14, 15] to study the flow

patterns and the residual stresses in FSW. It was shown that the flows on the advancing side and

retreating side are different. Wang et al. presented a 3D numerical model to study the material flow

in FSW process [16]. Results indicated that the material in front of the pin moves upwards due to

the extrusion of the pin, and then the upward material rotates with the pin. Behind the rotating tool,

the material starts to move downwards and to deposit in the wake. A FSW process was modeled by

Santiago et al. [17] using a general purpose FE based program for reproducing the material thermal

map and the corresponding mass flow. Numerical thermal results were compared against

Advanced Materials Research Vols. 774-776 (2013) pp 1155-1159Online available since 2013/Sep/04 at www.scientific.net© (2013) Trans Tech Publications, Switzerlanddoi:10.4028/www.scientific.net/AMR.774-776.1155

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experimental thermo-graphic maps. Material flow in FSW under different process parameters was

simulated by Zhang et al. [18, 19] using the FE technique based on the nonlinear continuum

mechanics. Results indicated that the distribution of the equivalent plastic strain correlates well with

the distribution of the microstructure zones in the weld.

The 3D material flow patterns on different thicknesses were studied by Zhang et al. [20]. The

3D material flow patterns was compared with the 2D case to show that the material flow obtained in

2D numerical simulation correspond to the ones near the bottom surface obtained in 3D simulation.

In a later work, a fully coupled thermo-mechanical model of the FSW process was developed by

Zhang and Zhang [21, 22]. Results indicated that the rotation of the shoulder can accelerate the

material flow behavior near the top surface. The material deformation and the temperature field can

have relations with the microstructural evolution. A fully coupled thermo-mechanical FE model

was used by Zhang et al. [23] to study the FSW process of AA2024-T3 in different thicknesses. The

computational results showed that the material flow on the retreating and the front sides are higher.

The energy entering the welding plate accounts for over 50% in the total energy and about 85% in

the energy comes from the frictional heat in FSW of AA2024-T3 and the balance from the

mechanical effects.

Texture evolution during FSW of stainless steel was investigated by Cho and Dawson [24, 25]

using a poly-crystal plasticity model together with a 3D thermo-mechanically coupled FE

formulation. The influence of frictional conditions with the tool pin and shoulder on the flow in the

through-thickness direction was examined in terms of their impact on the evolving crystallographic

texture. Trends in regard to the strengthening and weakening of the texture were discussed in

relation to the relative magnitudes of the deformation rate and spin. Fratini et al. [26] presented an

improved continuum FE model for the simulation of FSW processes aimed to obtain T joints, made

of a stringer in AA7175-T73511 and of a skin in AA2024-T4. The model, taking into account the

thermo-mechanical behaviors of the two different materials was utilized to study the occurring

material flow and residual stress state. The material flow direction during FSW was investigated by

Shimoda et al. [27] using a high speed video camera between transparent Poly-vinyl chloride (PVC)

materials as simulation experiments of aluminum alloys. Additionally, the material flow velocity

was measured by particle image velocimetry (PIV) method. Also, the material flow was

numerically simulated usingDEFORM-3D FE software.

Based on solid mechanics, FE method was used by Zhang and Zhang [28] to establish a 3D

model of FSW process. The material behavior and the mechanical features in joining non-line

welding were studied to help the understanding of the mechanism of FSW. Results indicate that the

material flow around the pin on the retreating side is much faster than that on the advancing side.

The FE method was used by Zhang et al. [29] to model the material flow of alloy Al 6061-T6 in

FSW process and establish the relationship between the material flow and the angular velocity of

the pin. With the increase of the angular velocity, the material flow becomes faster.

Temperature distribution

The temperature measurements within the stirred zone are very difficult due to the intense plastic

deformation produced by the rotation and translation of tool. Therefore, it is important to obtain

information about temperature distribution during FSW process by FEA. Thermo-mechanical

simulation of FSW process can predict the transient temperature field, maximum temperatures,

active stresses, forces and may be extended to determine the residual stress.

A comprehensive 3D thermal FE model was proposed by Khandkar and Khan [30] to predict

temperature distribution during overlap FSW process. The model accounts for moving heat

generation caused by the rotation and linear traverse of the shoulder and pin as well as the

convective/diffusive heat transfer caused by the plastic material flow in the vicinity of the shoulder

and the pin. The contact pressure distribution and consequently the surface conductance at the

contact surface between the sheets was modelled as varying with the highest pressure being applied

1156 Advanced Technologies in Manufacturing, Engineering and Materials

directly underneath the shoulder area. Temperature dependent properties of the weld-material have

been used for the numerical modelling. Ulysse presented an attempt [31] to model the FSW process

using 3D visco-plastic FE modelling. Parametric studies was conducted to determine the effect of

tool speeds on plate temperatures and to validate the model predictions with available

measurements. The model can be useful in designing welding tolls which will yield desired thermal

gradients.

A temperature field numerical model of FSW in quasi-steady-state was established by [32] with

the heat generated by the friction and the plastic deformation. Finite element software COMSOL

was used to calculate the temperature field of FSW in quasi-steady-state for 1100 aluminium alloy,

at the rotational speed of 750 r/min and welding speed of 300 mm/s. The results showed reasonably

good match between the simulated temperature profiles and experimental data. Inverse modelling

has been used by Larsen et al. [33] to determine the magnitude and spatial distribution of the heat

transfer coefficient between the workpiece and the backingplate in FSW process. The objective is to

minimize the difference between experimentally measured temperature and temperature obtained

using a 3D FE model. It was found that the heat transfer coefficient between the workpiece and the

backingplate is non-uniform and takes its maximum value in a region below the welding tool. This

was the first study using a gradient based optimization method and a non-uniform parameterization

of the heat transfer coefficient in an inverse modelling approach to determine the heat transfer

coefficient in FSW.

Jamshidi Aval et al. [34] investigated the relationship between the microstructures of

thermo-mechanically affected zone and heat input in FSW of 5086 aluminium alloy. The results

showed that the temperature field in the FSW process is asymmetrically distributed with respect to

the welding line. Also, both experimental and predicted data illustrates that peak temperature are

higher on the advancing side than the retreating side. A FE simulation model has been developed by

Prasanna et al. [35] with improved capability to predict temperature evolution in stainless steel. The

simulation model was tested with existing experimental results on 304L stainless steel. The peak

temperature obtained was 1,057°C, which was much less than the melting point of 304 L steel

(1,450°C).

A fully-coupled thermal-mechanical FE model based on the arbitrary Lagrangian-Eulerian

(ALE) method was used by Shi et al. [36] to numerically simulate FSW with the inelastic heat

generation included in the model. The temperature fields during FSW were simulated with

rotational speeds of 600 r/min and 800 r/min. The results showed that the numerical model can

accurately simulate the temperature distribution of FSW process. In Simar et al.’s paper [37],

similar and dissimilar FSW joints made of aluminium alloys 2017-T6 and 6005A-T6 were

compared in terms of heat inputs, temperatures, material flow and resulting local and overall tensile

properties. Predictions of a 3D FE model of the tensile test transverse to the weldlline were assessed

towards local deformation fields measured by digital image correlation.

Friction stir spot welding (FSSW) is a solid state joining technology that has the potential to be

a replacement for processes like resistance spot welding and rivet technology in certain

applications. Muci-Küchler et al.’s paper [38] presented a fully coupled thermo-mechanical FE

model of the plunge phase of a modified refill FSSW. The model was developed in Abaqus/Explicit

and the simulation results included the temperature, deformation, stress, and strain distribution in

the plates being joined. In order to simulate the FSSW process efficiently, an axis-symmetric 2D

FEM model with an adaptive re-meshing algorithm was proposed by Ma et al. [39]. By using the

Advanced Materials Research Vols. 774-776 1157

proposed model, temperature distribution, joining shape of the transverse section including toe flash

on the surface and hook on the joining interface, can be simulated. Furthermore, effects of clamp

position and stirring direction on material flow and joining shape were investigated.

By means of a 3D heat FE mathematical model and software ANSYS, the temperature

distribution of FSW process on 14 mm thick 2219 aluminium alloy were calculated by Xu et al.

[40]. Calculated results indicated that in the stable welding stage, the temperature gradient presents

different distribution along thickness direction of the plate, which is wide and high at the top while

narrow and low at the bottom. The results also indicated that the peak temperature can be increased

with the increase of the rotary speed. In Buffa and Fratini’s paper [41], a continuum based FE

model for FSW of steels was proposed, which is 3D Lagrangian implicit, coupled, rigid

viscoplastic. The model can be used to predict temperature, strain and strain rate distribution,

together with thermal and mechanical loads on the welding tool, at varying main process variables.

For investigating the effect of input parameters on thermal history and mechanical properties of

AA2014 aluminium alloy FSW joints, a 3D transient thermal FE model was developed and

experimentally validated by Rajamanickam et al. [4]. Temperature measurements and analysis of

variance (ANOVA) indicated that the temperature under the tool was strongly dependent on the tool

rotation speed than the weld speed. The results also indicated that weld speed could be the main

input parameter that had the highest statistical influence on mechanical properties.

Conclusions

Although FSW has been successfully used to join materials that are difficult-to-weld, it is still in its

early development stage. Development of the FSW process for each new application has remained

largely empirical. A scientific knowledge based predictive model is of significant help for thorough

understanding of FSW process. In this paper, the recent development in thermo-mechanical

modelling of FSW process is described with particular reference to two major factors that influence

the performance of FSW joints: material flow and temperature distribution. The main

thermo-mechanical modelling used in FSW process are discussed and illustrated with brief case

studies from the literature.

Acknowledgement

Project supported by the '100 Talents Project' of Yunnan Province and Program for Innovative

Technology Research Groups in Kunming University of Science and Technology, China.

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