Prediction of tensile strength of friction stir welded 6061 Al plates

Farghaly Ahmed A,El-Nikhaily Ahmed E,Essa A R S,2

1.Mechanical Department,Faculty of Industrial Education,Suez University,43527,Egypt;

2.Mechanical Engineering Department,Egyptian Academy for Engineering &Advanced Technology,Affiliated to Ministry of Military Production,3056,Egypt

Abstract The present paper investigates the prediction of tensile strength after friction stir welding (FSW)using artificial neural network(ANN)in the MATLAB program.The experimental results are used to develop the mathematical model.The combined influence of welding speed,rotation speed,and axial force on the tensile strength of 6061 Al plates is simulated.Results of the tensile test are used to train and test the ANN model.A multi-layer solution is developed using the ANN model to predict tensile strength.Back propagation (BP)method is initially trained using 80% of the experimental data,then,testing is performed with the rest of the data.Results indicate that predicted values are close to the corresponding measured values.

Key words prediction,friction stir welding,6061 aluminum alloy,artificial neural network model

0 Introduction

Friction stir welding is an exceptional joining method,developed in the early nineties of the 20 century.During friction stir welding,the rotating tool pin moves longitudinally at a specified tilt angle between the adjoining edges of the two welded sheets or plates[1].Friction stir welding is suitable to weld aluminum,magnesium,steel,and polymers[2].It is characterized by low energy consumption,non-environmental pollution,and can be applied to various types of joints.Compared with conventional welding process,it has metallurgical benefits as low defacement of the specimen,good stability in specimen dimensions,no losses of alloying elements,and a fine microstructure.Also,it is environmentally-friendly,as it requires no shielding gas and no surface cleaning.Moreover,it has energy benefits as lightweight[3].

The main FSW process parameters affecting the joint quality are rotation speed (Rsr/min),welding speed (Tsmm/min),and downforce (FkN).The relations between such parameters and the mechanical properties for FSW AA6061 are found elsewhere[4],where it was found that the process parameters play a major role to determine the joint quality.Also,it was found that the tensile strength of an AA6061 Al alloy welded joint is higher than that of the base plate[5].A FSW tool may be cylindrical,cylindrical with threads,tapered with threads,or threaded tapered with flutes[6].The tool geometry has a significant effect on the strength of the welded joint.The effect of the pin profile on tensile properties of FSW joint of dissimilar AA 6061 and AA 5086 aluminum alloys was studied[7].The threaded tool pin profile leads to a better flow of materials higher tensile strength (169 MPa)than the straight cylindrical pin,and taper cylindrical pin.

Prediction of unknown FSW values using computeraided models would eliminate testing cost and time.The tensile strength of FSW Al alloy was determined mathematically and experimentally[8].Hence,welding speed,pin rotational speeds,downforce value,pin profile,pin length,and shoulder geometry affect the strength of the welded joint.The tensile properties of a friction-stir welded joints were experimentally determined[9].The maximum ultimate strength is about 77% higher than that of the base material,at 1500 r/min rotation speed,and 80 mm/min welding speed.Micro friction stir welding of ultra-thin Al-6061 sheets using a taper pin with triple flats[10],increases UTS to 217 MPa,i.e,about 90% enhancement from the base material.Optimization of FSW parameters for Al-6061 alloy was carried out[11].The ultimate tensile strength is 109.87 MPa at 800 r/min,40 mm/min,and after one pass.

Process analysis and internal defects in FSW Al-6061 sheets were studied[12].Three types of flat shoulder tools with different diameters and with pins of equal probe lengths were used.Applying higher welding and rotation speeds,and higher plunge depths produce welds without internal defects.Besides,the mechanical properties of FSW of Al-6061 using circular and taper pin were investigated[13].Tensile strength using the taper thread was about 46% higher than that using circular thread.The mechanical characterization of FSW of dissimilar AZ31B and Al-6061 alloy joints were investigated[14].It is found that increasing the rotation and welding speeds leads to higher tensile strength,due to the formation of intermetallic compounds.Al-6061 alloy is welded using laser-assisteded friction stir welding machine[15].The welded plate dimensions are about 90mm×20mm×3 mm,the tool is a threaded cylindrical pin 2.5 mm diameter,while the shoulder diameter is 20 mm and the tilt angle is 3 degrees.

At 700 r/min rotation speed and 60 mm/min welding speed,the ultimate tensile strength is 196 MPa.The mechanical behavior of friction-stir welded joints is studied[16].The hardness increase is about 54% than that of the base material,at 700 r/min rotation speed,and at a welding speed of 60 mm/min.The effect of tool design and welding parameters on FSW of dissimilar Al alloys (AA 5052,AA 6061)are studied,and it is found that lower travel speeds lead to higher tensile strengths[17].The mechanical behavior of friction-stir welded joints is studied[18],and highest UTS is at 1 400 r/min and 90 mm/min.The effect of tool shape on mechanical properties of friction-stir welded aluminum alloys has been studied[19].Three types of tools have been used;column with threads,column without threads and triangular prism.Highest tensile strength was at 1500 r/min using the tool with triangular prism.Friction stir welding of dissimilar materials (AA 6061 and AA 7075 Al alloys)was studied[20].All joints exhibit very good tensile properties,and the ultimate tensile strength is higher than 215 MPa.The highest UTS is 32% higher than that required for FSW of AA6061 alloy at T6 condition in AWS standard (186 MPa).The influence of tool dimensions and welding parameters on the mechanical properties of FSW 6061 Al alloy is studied[21],and it is found that the tensile strength of welds increases with increasing the welding speed from 200 to 600 mm/min.Also,the mechanical properties of friction stir welding of 6061 to 7050 aluminum alloys are studied[22].The ultimate tensile strength of weld joints increases with increasing tool rotational speed.

Process parameter optimization of Al 6061 FSW using RSM is carried out[23],and an empirical correlation to estimate UTS with average error 6.005% could be established.Furthermore,an empirical correlation to estimate UTS with 95% confidence level is established[24].ANN is used to predict the flow stress for cold forging,hot deformation,device wear monitoring,machining behavior,manufacturing processes optimization[13,14,15],and tensile strength[25,26],and it can be used to predict the mechanical properties of joints welded by FSW with RMS error values of 0.018.

Scientific research is focused on the effect of FSW process parameters on tensile properties and microstructure formation,and also on developing a mathematical model to predict the tensile strength.The aim of present work is to predict the mechanical properties of friction stir welded joints using the artificial neural network (ANN).

1 Experimental

The present work deals with FSW of AA6061 aluminum alloy.The chemical composition of base metal is presented in Table 1.Properties of AA6061 aluminum alloy are presented in Table 2.The FSW but joint samples are made of two 4 mm thick,100 mm wide,and 120 mm long plates,and the welding direction is normal to the rolling direction.Single-pass welding procedure has been followed to fabricate the joints,and the applied FSW parameters are given in Table 3.Friction stir butt welds are carried out in the Friction Stir Welding and Processing Lab in the Faculty of Petroleum and Mining Engineering,Suez University,Fig.1.Applied FSW tool specifications are presented in Table 4 and Fig.2.Tensile test specimen is cut along the joint transverse direction,Fig.3,and the test is carried outusing "Instron" tensile testing machine.

Table 1 Chemical composition of AA6061 alloy (wt%)

Table 2 Properties of AA6061 alloy

Table 3 FSW process parameters

Fig.1 FSW experiment setup

Table 4 Friction stir welding tool

Fig.2 FSW tool,smooth pin and concave shoulder with cavities

Fig.3 Tensile test specimen(mm)

2 ANN model for tensile strength of FSW joints using MATLAB

ANN was initially inspired by,but not identical to,biological simplification of neurons in the brain.ANN,as a punch of interconnected nodes,requires some previous training to perform specific tasks in the future.The training stage is one of three main steps the ANN algorithm has to run through in order to produce the final model that ends up describe the problem at hand.These three stages (training,testing,and verification)have been accomplished using the MATLAB environment.MATLAB is a high-level programming language that is designed for technical computations.Therefore,in this work,the MATLAB library of Neural Network has been used for training and testing the artificial model.The input of the developed ANN model is rotation speed (r/min),welding speed (mm/min),and downforce(kN)where the output is tensile strength (MPa).

3 Back-propagation neural network

Present experimental tensile strength values and published values are used to train and test the ANN model.Table 5[4,5,8]and Table 6 show the FSW parameters and the experimental tensile strength values measured in this study.Furthermore,a multi-layer ANNs model is developed using MATLAB to predict the tensile strength values.Backpropagation (BP)algorithm is initially trained with 70% of experimental data and the 30% experimental data is assigned for testing and verification.Equation (1)is used to normalize the experimental data between 0.1 and 0.9,and Table 7 demonstrates a sample of the normalized experimental data.

where,Zis the normalized value,Yis the value to be normalized,andX=Ymax-Ymin.

A single hidden layer with training function Traingdx is used to prepare the back-propagation algorithm.In training,“2,4,6,8,and 10”number of neurons in a hidden layer is used,to find the optimum number of neurons that describes the results at hand.

The ANN layer corresponds to input parameters (rotation speed,welding speed,and down force),and the output is the predicted tensile strength of FSW AA6061 butt joint.After successful training,the network is tested by known test data.The used training parameters are listed in Table 8.After testing,the neural network is used to predict tensile strength of FSW AA6061 butt joints.Measured and predicted values are very close to each other,The error of predicted ANN model No.4 with structure 4-8-1 is less than 1%.

Table 5 FSW parameters and measured tensile strength values

Table 6 Present parameters and measured tensile strength values.

Table 7 Normalized data of parameters and measured tensile strength values

Table 8 Artificial neural network model data

Finally,the mean-square error of model-4 with structure 4-8-1 is small (R2=0.998 7).Performance analysis of trend network,linear regression of network outputs,and the corresponding targets are shown in Fig.4.The measured and predicted values of the tensile shear load are very close to each other (Fig.5).However,the main quality indicator of a neural network is its generalization ability,and to predict accurately the output of unseen test data,as shown in Fig.4b and Fig.4c.

Fig.4 Performance analysis of trend network of the model (a)Training:R=l (b)Validation:R=0.998 16 (c)Test:R=0.996 85 (d)All:R=0.997 81

Fig.5 Measured and predicted values of the tensile shear test.

4 Conclusions

(1)The developed ANN model is used to predict the tensile strength of 6061 aluminum alloy FSW joints using a threaded pin.

(2)The predicted values are very closed to the experimental values.

(3)the overallR2value for training,validation,and testing is higher than 0.998 7 and the error of predicted values are very small for 4-8-1 ANN architect.