A Novel Method for Predicting Tensile Strength of Friction Stir Welded AA6061 Aluminium Alloy Joints Based on Hybrid Random Vector Functional Link and Henry Gas Solubility Optimization

Aluminum alloys have low weldability by conventional fusion welding processes. Friction stir welding (FSW) is a promising alternative to traditional fusion welding techniques for producing high quality aluminum joints. The quality of the welded joints is highly dependent on the process parameters used during welding. In this research, a new approach was developed to predict the process parameters and mechanical properties of AA6061-T6 aluminium alloy joints in terms of ultimate tensile strength (UTS). A new hybrid artificial neural network (ANN) approach has been proposed in which Henry Gas Solubility Optimization (HGSO) algorithm has been incorporated to improve the performance of Random Vector Functional Link (RVFL) network. The HGSO-RVFL model was constructed with four parameters; rotational speed, welding speed, tilt angle, and pin profile. The validity of the model was tested, and it was demonstrated that the HGSO-RVFL model is a powerful technique for predicting the UTS of friction stir welded (FSWD) joints. In addition, the effects of process parameters on UTS of welded joints were discussed, where a significant agreement was observed between experimental results and predicted results which indicates the high performance of the model developed to predict the appropriate welding parameters that achieve optimal UTS.

[1]  P. Asadi,et al.  Multicriteria optimization of mechanical properties of aluminum composites reinforced with different reinforcing particles type , 2018 .

[2]  V. Balasubramanian,et al.  Effect of tool tilt angle on strength and microstructural characteristics of friction stir welded lap joints of AA2014-T6 aluminum alloy , 2019, Transactions of Nonferrous Metals Society of China.

[3]  S. Rajput,et al.  Modeling of flow stress of AA6061 under hot compression using artificial neural network , 2017 .

[4]  S. Rajput,et al.  Modeling of the hot deformation behavior of a high phosphorus steel using artificial neural networks , 2016 .

[5]  Vahid Moosabeiki Dehabadi,et al.  Application of artificial neural network to predict Vickers microhardness of AA6061 friction stir welded sheets , 2016 .

[6]  R. Mishraa,et al.  Friction Stir Welding And Processing , 2005 .

[7]  C. L. Philip Chen,et al.  A rapid supervised learning neural network for function interpolation and approximation , 1996, IEEE Trans. Neural Networks.

[8]  Mohamed Abd Elaziz,et al.  Improved prediction of oscillatory heat transfer coefficient for a thermoacoustic heat exchanger using modified adaptive neuro-fuzzy inference system , 2019, International Journal of Refrigeration.

[9]  Hengan Ou,et al.  A review of friction stir welding of aluminium matrix composites , 2015 .

[10]  G. L. Datta,et al.  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 , 2010, Appl. Soft Comput..

[11]  I. Dinaharan,et al.  Application of an artificial neural network model to predict the ultimate tensile strength of friction-welded titanium tubes , 2019, Journal of the Brazilian Society of Mechanical Sciences and Engineering.

[12]  M. Shojaeefard,et al.  Hybrid multi-objective optimization of microstructural and mechanical properties of B 4 C/A356 composites fabricated by FSP using TOPSIS and modified NSGA-II , 2017 .

[13]  J. Francis,et al.  A semi-analytical solution for the transient temperature field generated by a volumetric heat source developed for the simulation of friction stir welding , 2019, International Journal of Thermal Sciences.

[14]  M. Aliha,et al.  Mechanical and metallurgical properties of dissimilar AA6061-T6 and AA7277-T6 joint made by FSW technique , 2016, The International Journal of Advanced Manufacturing Technology.

[15]  T. Warren Liao,et al.  Prediction of tensile strength of friction stir weld joints with adaptive neuro-fuzzy inference system (ANFIS) and neural network , 2016 .

[16]  C. Muralidharan,et al.  Influence of friction stir welding process and tool parameters on strength properties of AA7075-T6 aluminium alloy joints , 2011 .

[17]  S. C. Saha,et al.  An experimental investigation of torque and force generation for varying tool tilt angles and their effects on microstructure and mechanical properties: Friction stir welding of AA 6061-T6 , 2018 .

[18]  L. Murr.  A Review of FSW Research on Dissimilar Metal and Alloy Systems , 2010 .

[19]  G. Karsai,et al.  Artificial neural networks applied to arc welding process modeling and control , 1989, Conference Record of the IEEE Industry Applications Society Annual Meeting,.

[20]  J. Dhas,et al.  Optimization of Multiple Performance Characteristics of Friction Stir Welded Joint with Grey Relational Analysis , 2018, Materials Research.

[21]  Seyedali Mirjalili,et al.  Henry gas solubility optimization: A novel physics-based algorithm , 2019, Future Gener. Comput. Syst..

[22]  Dulce Maria Rodrigues,et al.  Dissimilar friction stir lap welding of AA 5754-H22/AA 6082-T6 aluminium alloys: Influence of material properties and tool geometry on weld strength , 2015 .

[23]  P. Biswas,et al.  A study on friction stir welding of 12mm thick aluminum alloy plates , 2013 .

[24]  Dejan J. Sobajic,et al.  Learning and generalization characteristics of the random vector Functional-link net , 1994, Neurocomputing.

[25]  M. Abd Elaziz,et al.  Modeling of solar energy systems using artificial neural network: A comprehensive review , 2019, Solar Energy.

[26]  Ahmad Wasim,et al.  Predicting the tensile strength, impact toughness, and hardness of friction stir-welded AA6061-T6 using response surface methodology , 2016 .

[27]  Murray W. Mahoney,et al.  Effects of friction stir welding on microstructure of 7075 aluminum , 1997 .

[28]  D. S. Nagesh,et al.  Prediction of weld bead geometry and penetration in shielded metal-arc welding using artificial neural networks , 2002 .

[29]  Chuansong Wu,et al.  Friction stir based welding and processing technologies - processes, parameters, microstructures and applications: A review , 2017 .

[30]  A. Shahani,et al.  Experimental investigation and numerical modeling of the fatigue crack growth in friction stir spot welding of lap-shear specimen , 2019, International Journal of Fatigue.

[31]  Mohamed Abd Elaziz,et al.  A review on meta-heuristics methods for estimating parameters of solar cells , 2019, Journal of Power Sources.

[32]  Ibrahim N. Tansel,et al.  Optimizations of friction stir welding of aluminum alloy by using genetically optimized neural network , 2010 .

[33]  V. Balasubramanian,et al.  Comparison of RSM with ANN in predicting tensile strength of friction stir welded AA7039 aluminium alloy joints , 2009 .

[34]  Hugo Calleja,et al.  Metaheuristic Optimization Methods Applied to Power Converters: A Review , 2015, IEEE Transactions on Power Electronics.

[35]  Surjya K. Pal,et al.  Artificial neural network modeling of weld joint strength prediction of a pulsed metal inert gas welding process using arc signals , 2008 .

[36]  S. Shanavas,et al.  Parametric optimization of friction stir welding parameters of marine grade aluminium alloy using response surface methodology , 2017 .

[37]  Mohamed Abd Elaziz,et al.  Modeling of friction stir welding process using adaptive neuro-fuzzy inference system integrated with harris hawks optimizer , 2019, Journal of Materials Research and Technology.

[38]  A. H. Elsheikh,et al.  Review on applications of particle swarm optimization in solar energy systems , 2018, International Journal of Environmental Science and Technology.

[39]  W. Xu,et al.  Abnormal fracture of 7085 high strength aluminum alloy thick plate joint via friction stir welding , 2019, Journal of Materials Research and Technology.

[40]  N. Murugan,et al.  Effect of tool rotational speed and pin profile on microstructure and tensile strength of dissimilar friction stir welded AA5083-H111 and AA6351-T6 aluminum alloys , 2012 .

[41]  A. Kabeel,et al.  An artificial neural network based approach for prediction the thermal conductivity of nanofluids , 2020, SN Applied Sciences.

[42]  Erol Arcaklioğlu,et al.  Artificial neural network application to the friction stir welding of aluminum plates , 2007 .

[43]  P. N. Suganthan,et al.  A comprehensive evaluation of random vector functional link networks , 2016, Inf. Sci..

[44]  Mohamed Abd Elaziz,et al.  Noise prediction of axial piston pump based on different valve materials using a modified artificial neural network model , 2019, Alexandria Engineering Journal.

[45]  Mohamed Abd Elaziz,et al.  An enhanced productivity prediction model of active solar still using artificial neural network and Harris Hawks optimizer , 2020 .

[46]  Ammar H. Elsheikh,et al.  Effect of cutting parameters on surface residual stresses in dry turning of AISI 1035 alloy , 2019, Journal of the Brazilian Society of Mechanical Sciences and Engineering.

[47]  Mostafa Akbari,et al.  Modelling and Pareto optimization of mechanical properties of friction stir welded AA7075/AA5083 butt joints using neural network and particle swarm algorithm , 2013 .

[48]  Jiajie Guo,et al.  Temperature field sensing of a thin-wall component during machining: Numerical and experimental investigations , 2018, International Journal of Heat and Mass Transfer.