Modelling of Friction Stir Extrusion using Artificial Neural Network (ANN)

Document Type: Original Article


1 Department of Mechanical Engineering, University of Wisconsin-Madison, USA

2 Faculty of Mechanical Engineering, Urmia University of Technology, Iran

3 Department of Industrial and Manufacturing Systems Engineering, Kansas State University, USA

4 Faculty of Electrical Engineering, Urmia University of Technology, Iran


In the present study, an artificial neural network (ANN) model is developed to predict the correlation between the friction stir extrusion (FSE) parameters and the recycled wires’ average grain sizes. FSE is a solid–state synthesis technique, in which machining chips are firstly loaded into the container, and then a rotating tool with a central hole is plunged into the chips at a selected rotational speed and feed rate to achieve indirect extrusion. Selecting rotational speed (RS), vertical speed (VS), and extrusion hole size (HS) as the input and average grain size as the output of the system, the 3–6–1 ANN is used to show the correlation between the input and output parameters. Checking the accuracy of the neural network, R squared value and Root–Mean–Square–Error (RMSE) of the developed model (0.94438 and 0.75794, respectively) have shown that there is a good agreement between experimental and predicted results. A sensitivity analysis has been conducted on the ANN model to determine the impact of each input parameter on the average grain size. The results showed that the rotational speed has more effect on average grain size during the FSE process in comparison to other input parameters.


Main Subjects

[1]     Zhang, T., Ji, Z., and Wu, S., Effect of Extrusion Ratio on Mechanical and Corrosion Properties of AZ31B Alloys Prepared by a Solid Recycling Process, Materials and Design, Vol. 32, No. 5, 2011, pp. 2742–2748.

[2]     Ansari, M. A., Behnagh, R. A., Narvan, M., Naeini, E. S., Givi, M. K. B., and Ding, H., Optimization of Friction Stir Extrusion (FSE) Parameters Through Taguchi Technique, Transaction of Indian Institute of Metals, Vol. 69, No. 7, 2016, pp. 1351–1357.

[3]     Sharifzadeh, M., Ansari, M. A., Narvan, M., Behnagh, R. A., Araee, A., and Besharati Givi, M. K., Evaluation of Wear and Corrosion Resistance of Pure Mg Wire Produced by Friction Stir Extrusion, Transaction of Nonferrous Metals Society of China, Vol. 25, No. 6, 2015, pp. 1847–1855.

[4]     Ji, Z. S., Wen, L. H., and Li, X. L., Mechanical Properties and Fracture Behavior of Mg–2.4Nd–0.6Zn–0.6Zr Alloys Fabricated by Solid Recycling Process, Journal of Materials Processing Technology, Vol. 209, No. 4, 2009, pp. 2128–2134.

[5]     Hu, M. L., Ji, Z. S., Chen, X. Y., Wang, Q. D., and Ding, W. J., Solid-State Recycling of AZ91D Magnesium Alloy Chips, Transaction of Nonferrous Metals Society of China, Vol. 22, No. 1, 2012, pp.68-73.

[6]     Chino, Y., Kishihara, R., Shimojima, K., Hosokawa, H., Yamada, Y., Wen, C., Iwasaki, H., and Mabuchi, M., Superplasticity and Cavitation of Recycled AZ31 Magnesium Alloy Fabricated by Solid Recycling Process, Mater. Trans., Vol. 43, No. 10, 2002, pp. 2437–2442.

[7]     Wen, L., Ji, Z., and Li, X., Effect of Extrusion Ratio on Microstructure and Mechanical Properties of Mg–Nd–Zn–Zr Alloys Prepared by a Solid Recycling Process, Materials Characterization, Vol. 59, No. 11, 2008, pp. 1655–1660.

[8]     Tang, W., Reynolds, A. P., Production of Wire via Friction Extrusion of Aluminum Alloy Machining Chips, Journal of Materials Processing Technology, Vol. 210, No. 15, 2010, pp. 2231–2237.

[9]     Nicholas, E. D., Friction Processing Technologies, Advanced Materials & Processes, Vol. 155, No. 6, 1999, pp. 69–71.

[10]  Fehrenbacher, A., Schmale, J. R., Zinn, M. R., and Pfefferkorn, F. E., Measurement of Tool-Workpiece Interface Temperature Distribution in Friction Stir Welding, Journal of Manufacturing Science Engineering, Vol. 136, No. 2, 2014, pp. 021009.

[11]  Pellegrino, J. L., Margolis, N., Justiniano, M., Miller, M., and Thedki, A., Energy Use, Loss and Opportunities Analysis: US Manufacturing and Mining, DOE/ITP (U.S. Dep. Energy’s Iindustrial Technol. Program), (December), 2004, pp. 169.

[12]  Das, S. K., Green, J. A. S., Kaufman, J. G., Emadi, D., and Mahfoud, M., Aluminum Recycling-An Integrated, Industrywide Approach, The Journal of The Minerals, Metals & Materials Society, Vol. 62, No. 2, 2010, pp. 23–26.

[13]  Ansari, M. A., Sadeqzadeh Naeini, E., Besharati Givi, M. K., and Faraji, G., Theoretical and Experimental Investigation of the Effective Parameters on the Microstructure of Magnesium Wire Produced by Friction Stir Extrusion, Vol. 15, No. 6, 2015, pp. 346–352.

[14]  Huang, W., Hou, B., Pang, Y., and Zhou, Z., Fretting Wear Behavior of AZ91D and AM60B Magnesium Alloys,Wear, Vol. 260, No. 11–12, 2006, pp. 1173–1178.

[15]  Kang, S., Lee, Y., and Lee, J., Effect of Grain Refinement of Magnesium Alloy AZ31 by Severe Plastic Deformation on Material Characteristics, Journal of Materials Processing Technolology, Vol. 201, No. 1–3, 2008, pp. 436–440.

[16]  Behnagh, R. A., Shen, N., Ansari, M. A., Narvan, M., Besharati Givi, M. K., and Ding, H., Experimental Analysis and Microstructure Modeling of Friction Stir Extrusion of Magnesium Chips, Journal of Manufacturing Science and Engineering, Vol. 138, No. 4, 2015, pp. 041008.

[17]  Grum, J., Slabe, J. M., The Use of Factorial Design and Response Surface Methodology for Fast Determination of Optimal Heat Treatment Conditions of Different Ni–Co–Mo Surfaced Layers, Journal of Materials Processing Technology, Vol. 155–156, 2004, pp. 2026–2032.

[18]  Lakshminarayanan, A. K., Balasubramanian, V., Comparison of RSM with ANN in Predicting Tensile Strength of Friction Stir Welded AA7039 Aluminium Alloy Joints, Transactions of Nonferrous Metals Society of China, Vol. 19, No. 1, 2009, pp. 9–18.

[19]  Elatharasan, G., Kumar, V. S. S., An Experimental Analysis and Optimization of Process Parameter on Friction Stir Welding of AA 6061-T6 Aluminum Alloy Using RSM, Procedia Engineering, Vol. 64, 2013, pp. 1227–1234.

[20]  Palanivel, R., KoshyMathews, P., and Murugan, N., Development of Mathematical Model to Predict the Mechanical Properties of Friction Stir Welded AA6351 Aluminum Alloy, Journal of Enginering Science and Technology, Vol. 4, No. 1, 2011, pp. 25–31.

[21]  Rajakumar, S., Muralidharan, C., and Balasubramanian, V., Establishing Empirical Relationships to Predict Grain Size and Tensile Strength of Friction Stir Welded AA 6061-T6 Aluminium Alloy Joints, Transaction of Nonferrous Metals Society of China, Vol. 20, No. 10, 2010, pp. 1863–1872.

[22]  Elangovan, K., Balasubramanian, V., and Babu, S., Predicting Tensile Strength of Friction Stir Welded AA6061 Aluminium Alloy Joints by a Mathematical Model, Materials and Design, Vol. 30, No. 1, 2009, pp. 188–193.

[23]  Okuyucu, H., Kurt, A., and Arcaklioglu, E., Artificial Neural Network Application to the Friction Stir Welding of Aluminum Plates, Materials and Design, Vol. 28, No. 1, 2007, pp. 78–84.

[24]  Asadi, P., Givi, M. K. B., Rastgoo, A., Akbari, M., Zakeri, V., and Rasouli, S., Predicting the Grain Size and Hardness of AZ91/SiC Nanocomposite by Artificial Neural Networks, International Journal of Advanced Manufacturing Technology, Vol. 63, No. 9-12, 2012, pp. 1095–1107.

[25]  Yousif, Y. K., Daws, K. M., and Kazem, B. I., Prediction of Friction Stir Welding Characteristic Using Neural Network, JJMIE Jordan J. Mech. Ind. Eng., Vol. 2, No. 3, 2008, pp. 151–155.

[26]  Ghetiya, N. D., Patel, K. M., Prediction of Tensile Strength in Friction Stir Welded Aluminium Alloy Using Artificial Neural Network, Procedia Technology, Vol. 14, 2014, pp. 274–281.

[27]  Arunchai, T., Sonthipermpoon, K., Apichayakul, P., and Tamee, K., Resistance Spot Welding Optimization Based on Artificial Neural Network, Vol. 2014, 2014, pp.1-6.

[28]  Bourquin, J., Schmidli, H., Van Hoogevest, P., and Leuenberger, H., Advantages of Artificial Neural Networks (ANNs) as Alternative Modelling Technique for Data Sets Showing Non-Linear Relationships Using Data from a Galenical Study on a Solid Dosage Form, European Journal of Pharmaceutical Sciences, Vol. 7, No. 1, 1998, pp. 5–16.

[29]  Li, H. J., Qi, L. H., Han, H. M., and Guo, L. J., Neural Network Modeling and Optimization of Semi-Solid Extrusion for Aluminum Matrix Composites, Journal of Materials Processing Technology, Vol. 151, 2004, pp. 126–132.

[30]  Ates, H., Prediction of Gas Metal Arc Welding Parameters Based on Artificial Neural Networks, Materials and Design, Vol. 28, No. 7, 2007, pp. 2015–2023.

[31]  Muthukrishnan, N., Davim, J. P., Optimization of Machining Parameters of Al/SiC-MMC with ANOVA and ANN Analysis, Journal of Materials Processing Technology, Vol. 209, No. 1, 2009, pp. 225–232.

[32]  Demuth, H., Beale, M., Neural Network Toolbox For Use with MATLAB, 1992, Aerospace, p. 846.

[33]  Kumar, P., Nigam, S. P., and Kumar, N., Vehicular Traffic Noise Modeling Using Artificial Neural Network Approach, Transportation Research Part C, Vol. 40, 2014, pp. 111–122.

[34]  Ferreira, P., Ribeiro, P., Antunes, A., and Dias, F. M., A High Bit Resolution FPGA Implementation of a FNN with a New Algorithm for the Activation Function, Neurocomputing, Vol. 71, No. 1–3, 2007, pp. 71–77.

[35]  Tuntas, R., Dikici, B., An Investigation on the Aging Responses and Corrosion Behaviour of A356/SiC Composites by Neural Network: The Effect of Cold Working Ratio, Journal of Composite Materials, Vol. 50, No. 17, 2016, pp. 2323–2335.

[36]  Montano, J., Palmer, A., Numeric Sensitivity Analysis Applied to Feedforward Neural Networks, Neural Computing and Applications, Vol. 12, No. 2, 2003, pp. 119–125.

[37]  Campana, R. C., Vieira, P. C., and Plaut, R. L., Aplicability of Adaptive Neural Networks (Ann) in the Extrusion of Aluminum Alloys and in the Prediction of Hardness and Internal Defects, Materials Science Forum, Vol. 638-642, 2010, pp. 303–309.

[38]  Song, R., Zhang, Q., Tseng, M., and Zhang, B., The Application of Artificial Neural Networks to the Investigation of Aging Dynamics in 7175 Aluminium Alloys, Materials Science and Engineering C, Vol. 3, 1995, pp. 7–9.

[39]  Garson, G. D., Interpreting Neural Network Connection Weights, AI Expert, Vol. 6, No. 4, 1991, pp. 46–51.

[40]  Wang, W., Jones, P., and Partridge, D., Assessing the Impact of Input Features in a Feedforward Neural Network, Neural Computing and Applications, Vol. 9, No. 2, 2000, pp. 101–112.

[41]  Dutta, S., Gupta, J. P., PVT Correlations for Indian Crude Using Artificial Neural Networks, Journal of Petroleum Science and Engineering, Vol. 72, No. 1–2, 2010 pp. 93–109.

[42]  Gevrey, M., Dimopoulos, I., and Lek, S., Review and Comparison of Methods to Study the Contribution of Variables in Artificial Neural Network Models, Ecological Modelling, Vol. 160, 2003, pp. 249–264.