Artificial Intelligence Applications for Friction Stir Welding: A Review

P.L. Threadgilll, A.J. Leonard, H.R. Shercliff, P.J. Withers, Friction stir welding of aluminium alloys. Int. Mater. Rev. 54, 49–93 (2009). https://doi.org/10.1179/174328009X411136 K.N. Salloomi, F.I. Hussein, S.N.M. Al-Sumaidae, Temperature and stress evaluation during three different phases of friction stir welding of AA 7075-T651 alloy. Model. Simul. Eng. (2020). https://doi.org/10.1155/2020/3197813 M. Prakash, A. Daniel Das, Investigation on effect of FSW parameters of aluminium alloy using Full Factorial Design. Mater. Today Proc. (2020). https://doi.org/10.1016/j.matpr.2020.05.622 G.M. Xie, Z.Y. Ma, Z.A. Luo et al., Effect of rotation rate on microstructures and mechanical properties of FSW Mg–Zn–Y–Zr alloy joints. J. Mater. Sci. Technol. 27, 1157–1164 (2011). https://doi.org/10.1016/S1005-0302(12)60012-7 A. Dorbane, G. Ayoub, B. Mansoor et al., Microstructural observations and tensile fracture behavior of FSW twin roll cast AZ31 Mg sheets. Mater. Sci. Eng. A 649, 190–200 (2016). https://doi.org/10.1016/j.msea.2015.09.097 Y. Hovanski, R. Mishra, Y. Sato (ed.), et al. Friction stir welding and processing IX (Springer, 2017), pp. 324. https://doi.org/10.1007/978-3-319-52383-5 S. Saravanakumar, H. Vinoth Kumar, B. Allan Franklin et al., Experimental analysis of dissimilar metal of copper and brass plates fabricated friction stir welding. Mater. Today Proc. (2020). https://doi.org/10.1016/j.matpr.2020.03.740 J.H. Cho, S.H. Kang, H.N. Han, K.H. Oh, Modeling friction stir welding process of aluminum alloys. Met. Mater. Int. 14, 247–258 (2008). https://doi.org/10.3365/met.mat.2008.04.247 A. Günen, E. Kanca, M. Demir et al., Microstructural and mechanical properties of friction stir welded pure lead. Indian J. Eng. Mater. Sci. 25, 26–32 (2018) L. Prabhu, S.S. Kumar, Tribological characteristics of FSW tool subjected to joining of dissimilar AA6061-T6 and Cu alloys. Mater. Today Proc. (2020). https://doi.org/10.1016/j.matpr.2020.06.092 R.S. Mishra, Z.Y. Ma, Friction stir welding and processing. Mater. Sci. Eng. R Rep. 50, 1–78 (2005). https://doi.org/10.1016/j.mser.2005.07.001 N. Abu-warda, M.D. López, B. González et al., Precipitation hardening and corrosion behavior of friction stir welded A6005-TiB2 nanocomposite. Met. Mater. Int. (2020). https://doi.org/10.1007/s12540-020-00688-8 M.G. Jo, H.J. Kim, M. Kang et al., Microstructure and mechanical properties of friction stir welded and laser welded high entropy alloy CrMnFeCoNi. Met. Mater. Int. 24, 73–83 (2018). https://doi.org/10.1007/s12540-017-7248-x J.N. Kim, S.U. Lee, H.D. Kwun et al., SIMS evaluation of poly crystal boron nitride tool effect in thermo-mechanically affected zone of friction stir weld steels. Met. Mater. Int. 20, 1067–1071 (2014). https://doi.org/10.1007/s12540-014-6010-x E. Salvati, J. Everaerts, K. Kageyama, A.M. Korsunsky, Transverse fatigue behaviour and residual stress analyses of double sided FSW aluminium alloy joints. Fatigue Fract. Eng. Mater. Struct. 42, 1980–1990 (2019). https://doi.org/10.1111/ffe.13068 A.D. D’Souza, S.S. Rao, M.A. Herbert, Evaluation of microstructure, hardness and mechanical properties of friction stir welded Al–Ce–Si–Mg aluminium alloy. Met. Mater. Int. 26, 1394–1403 (2020). https://doi.org/10.1007/s12540-019-00372-6 T. Yoon, S. Park, S. Chung et al., Effect of surface oxidation layer on tensile strength of Cu–Ni alloy in friction stir welding. Met. Mater. Int. 22, 501–508 (2016). https://doi.org/10.1007/s12540-016-5620-x H. Yavuz, Function-oriented design of a friction stir welding robot. J. Intell. Manuf. 15, 761–775 (2004). https://doi.org/10.1023/B:JIMS.0000042662.79454.c5 A. Ankarali, M. Kalyoncu, F.M. Botsali, T. Şişman, Mathematical modeling and simulation of a flexible shaft-flexible link system with end mass. Math. Comput. Model. Dyn. Syst. 10, 187–200 (2004). https://doi.org/10.1080/13873950412331335216 M. Mondal, H. Das, E.Y. Ahn et al., Characterization of friction stir welded joint of low nickel austenitic stainless steel and modified ferritic stainless steel. Met. Mater. Int. 23, 948–957 (2017). https://doi.org/10.1007/s12540-017-6845-z I. Dinaharan, N. Murugan, Optimization of friction stir welding process to maximize tensile strength of AA6061/ZrB2 in-situ composite butt joints. Met. Mater. Int. 18, 135–142 (2012). https://doi.org/10.1007/s12540-012-0016-z A. Karimnejad, M. Ashjari, A. Heidarzadeh, T. Saeid, Structure modification of a rheocast aluminum–zinc alloy by friction stir butt welding: microstructural evolution. Met. Mater. Int. (2020). https://doi.org/10.1007/s12540-020-00643-7 H.H. Bilgic, M.A. Sen, M. Kalyoncu, Tuning of LQR controller for an experimental inverted pendulum system based on the bees algorithm. J. Vibroeng. 18, 3684–3694 (2016). https://doi.org/10.21595/jve.2016.16787 H. Yağlı, A. Koç, A. Yapıcı, H.H. Bilgiç, DeneyselBi̇rOrgani̇kRanki̇nÇevri̇mi̇nde YapaSi̇ni̇r Ağlari (Ysa) Yardimiyla Tahmi̇ni̇. Selcuk Univ. J. Eng. Sci. Technol. 4, 7–7 (2016). https://doi.org/10.15317/scitech.2016116091 A.K. Lakshmınarayanan, V. Balasubramanıan, Comparison of RSM with ANN in predicting tensile strength of friction stir welded AA7039 aluminium alloy joints. Trans. Nonferrous Met. Soc. China Engl. Ed. 19, 9–18 (2009). https://doi.org/10.1016/S1003-6326(08)60221-6 C. Rathinasuriyan, R. Sankar, A.G. Shanbhag, V.S. SenthilKumar, Prediction of the average grain size in submerged friction stir welds of AA 6061-T6. Mater. Today Proc. 16, 907–917 (2019). https://doi.org/10.1016/j.matpr.2019.05.176 M.R. Nakhaei, A. Mostafapour, G. Naderi, Optimization of Mechanical Properties of PP/EPDM/ Clay Nanocomposite Fabricated by Friction Stir Processing With Response Surface Methodology and Neural Networks Mohammad. Polym. Polym. Compos. (2016). https://doi.org/10.1002/pc.23942 M. Jayaraman, R. Sivasubramanian, V. Balasubramanian, A.K. Lakshminarayanan, Application of RSM and ANN to predict the tensile strength of Friction Stir Welded A319 cast aluminium alloy. Int. J. Manuf. Res. 4, 306–323 (2009). https://doi.org/10.1504/IJMR.2009.026576 A. Kumar, M.K. Khurana, G. Singh, Modeling and optimization of friction stir welding process parameters for dissimilar aluminium alloys. Mater. Today Proc. 5, 25440–25449 (2018). https://doi.org/10.1016/j.matpr.2018.10.349 G. Başar, S. Mistikoǧlu, Determination of the optimum welding parameters for ultimate tensile strength and hardness in friction stir welding of Cu/Al plates using Taguchi method. J. Fac. Eng. Archit. Gazi Univ. 34, 1595–1608 (2019). https://doi.org/10.17341/gazimmfd.570891 R. Kumar, S. Mishra, P.S. De Nilesh, Friction stir welding and and processing (Mater. Sci. Eng. R Rep, Mater, 2011). https://doi.org/10.1007/978-3-319-07043-8 M. Ubaid, D. Bajaj, A.K. Mukhopadhyay, A.N. Siddiquee, Friction stir welding of thick AA2519 alloy: defect elimination. Met. Mater. Int. (2019). https://doi.org/10.1007/s12540-019-00472-3 M. Almoussawi, A.J. Smith, Thermo-mechanical effect on poly crystalline boron nitride tool life during friction stir welding (dwell period). Met. Mater. Int. 24, 560–575 (2018). https://doi.org/10.1007/s12540-018-0074-y M. Jayaraman, R. Sivasubramanian, V. Balasubramanian, S. Babu, Influences of process parameters on tensile strength of friction stir welded cast A319 aluminium alloy joints. Met. Mater. Int. 15, 313–320 (2009). https://doi.org/10.1007/s12540-009-0313-3 S.H. Kang, H.N. Han, K.H. Oh et al., Investigation of the material flow and texture evolution in friction-stir welded aluminum alloy. Met. Mater. Int. 15, 1027–1031 (2009). https://doi.org/10.1007/s12540-009-1027-2 Q. Wen, W. Li, V. Patel et al., Investigation on the effects of welding speed on bobbin tool friction stir welding of 2219 aluminum alloy. Met. Mater. Int. (2019). https://doi.org/10.1007/s12540-019-00450-9 S. Park, H. Nam, Y. Na et al., Effect of initial grain size on friction stir weldability for rolled and cast CoCrFeMnNi high-entropy alloys. Met. Mater. Int. 26, 641–649 (2020). https://doi.org/10.1007/s12540-019-00466-1 H.S. Bang, S.M. Hong, A. Das, H.S. Bang, Study on the weldability and mechanical characteristics of dissimilar materials (Al5052-DP590) by TIG assisted hybrid friction stir welding. Met. Mater. Int. (2019). https://doi.org/10.1007/s12540-019-00461-6 P. Carlone, A. Astarita, G.S. Palazzo et al., Microstructural aspects in Al–Cu dissimilar joining by FSW. Int. J. Adv. Manuf. Technol. 79, 1109–1116 (2015). https://doi.org/10.1007/s00170-015-6874-z D. Bernard, D.G. Hattingh, W.E. Goosen, M.N. James, High speed friction stir welding of 5182-H111 alloy: temperature and microstructural insights into deformation mechanisms. Met. Mater. Int. (2020). https://doi.org/10.1007/s12540-020-00622-y A.K. Lakshminarayanan, V. Balasubramanian, Understanding the parameters controlling friction stir welding of AISI 409M ferritic stainless steel. Met. Mater. Int. 17, 969–981 (2011). https://doi.org/10.1007/s12540-011-6016-6 K.H. Song, Y.D. Chung, K. Nakata, Investigation of microstructure and mechanical properties of friction stir lap jointed Monel 400 and Inconel 600. Met. Mater. Int. 19, 571–576 (2013). https://doi.org/10.1007/s12540-013-3027-5 Y. Birol, S. Kasman, Friction stir welding of twin-roll cast en AW 3003 plates. Met. Mater. Int. 19, 1259–1266 (2013). https://doi.org/10.1007/s12540-013-6018-7 Y. Birol, E. Gokcil, M.A. Guvenc, S. Akdi, Processing of high strength EN AW 6082 forgings without a solution heat treatment. Mater. Sci. Eng. A 674, 25–32 (2016). https://doi.org/10.1016/j.msea.2016.07.062 W.M. Thomas, E.D. Nicholas, Friction stir welding for the transportation industries. Mater. Des. 18, 269–273 (1997). https://doi.org/10.1016/s0261-3069(97)00062-9 D.H. Lammlein, D.R. DeLapp, P.A. Fleming et al., The application of shoulderless conical tools in friction stir welding: an experimental and theoretical study. Mater. Des. 30, 4012–4022 (2009). https://doi.org/10.1016/j.matdes.2009.05.023 S. Joo, Joining of dissimilar AZ31B magnesium alloy and SS400 mild steel by hybrid gas tungsten arc friction stir welding. Met. Mater. Int. 19, 1251–1257 (2013). https://doi.org/10.1007/s12540-013-6016-9 K.K. Mugada, K. Adepu, Role of scroll shoulder and pin designs on axial force, material flow and mechanical properties of friction stir welded Al–Mg–Si alloy. Met. Mater. Int. (2020). https://doi.org/10.1007/s12540-020-00623-x R.P. Mahto, S. Anishetty, A. Sarkar et al., Interfacial microstructural and corrosion characterizations of friction stir welded AA6061-T6 and AISI304 materials. Met. Mater. Int. 25, 752–767 (2019). https://doi.org/10.1007/s12540-018-00222-x D. Liu, M. Shen, Y. Tang et al., Effect of multipass friction stir processing on surface corrosion resistance and wear resistance of ZK60 alloy. Met. Mater. Int. 25, 1182–1190 (2019). https://doi.org/10.1007/s12540-019-00268-5 M. Azizieh, A. Sadeghi Alavijeh, M. Abbasi et al., Mechanical properties and microstructural evaluation of AA1100 to AZ31 dissimilar friction stir welds. Mater. Chem. Phys. 170, 251–260 (2016). https://doi.org/10.1016/j.matchemphys.2015.12.046 K. Kamal Babu, K. Panneerselvam, P. Sathiya et al., Parameter optimization of friction stir welding of cryorolled AA2219 alloy using artificial neural network modeling with genetic algorithm. Int. J. Adv. Manuf. Technol. 94, 3117–3129 (2018). https://doi.org/10.1007/s00170-017-0897-6 M. Azizieh, A.N. Larki, M. Tahmasebi et al., Wear behavior of AZ31/Al2O3 magnesium matrix surface nanocomposite fabricated via friction stir processing. J. Mater. Eng. Perform. 27, 2010–2017 (2018). https://doi.org/10.1007/s11665-018-3277-y E. Sharghi, A. Farzadi, Simulation of strain rate, material flow, and nugget shape during dissimilar friction stir welding of AA6061 aluminum alloy and Al–Mg2Si composite. J Alloys Compd 748, 953–960 (2018). https://doi.org/10.1016/j.jallcom.2018.03.145 K.P. Mehta, V.J. Badheka, A review on dissimilar friction stir welding of copper to aluminum: process, properties, and variants. Mater. Manuf. Process. 31, 233–254 (2016). https://doi.org/10.1080/10426914.2015.1025971 G. Çam, S. Mistikoglu, M. Pakdil, Microstructural and mechanical characterization of friction stir butt joint welded 63% Cu-37% Zn brass plate. Weld. J. (Miami Fla) 88, 225–232 (2009) G. Çam, H.T. Serindaǧ, A. Çakan et al., The effect of weld parameters on friction stir welding of brass plates. Materwiss. Werksttech. 39, 394–399 (2008). https://doi.org/10.1002/mawe.200800314 A. Fall, M. Jahazi, A.R. Khdabandeh, M.H. Fesharaki, Effect of process parameters on microstructure and mechanical properties of friction stir-welded Ti–6Al–4V joints. Int. J. Adv. Manuf. Technol. 91, 2919–2931 (2017). https://doi.org/10.1007/s00170-016-9527-y A. Kar, S. Suwas, S.V. Kailas, Significance of tool offset and copper interlayer during friction stir welding of aluminum to titanium. Int. J. Adv. Manuf. Technol. 100, 435–443 (2019). https://doi.org/10.1007/s00170-018-2682-6 L. Zhou, M. Yu, H. Zhao et al., Dissimilar friction stir welding of AA6061 and Ti6Al4V alloys: a study on microstructure and mechanical properties. J. Manuf. Process. 48, 119–126 (2019). https://doi.org/10.1016/j.jmapro.2019.09.043 S. Delijaicov, D.Y. Yakabu, B. De Macedo et al., Characterization of the surface and mechanical properties of the friction stir welding in tri-dissimilar joints with aluminum alloys and titanium alloy. Int. J. Adv. Manuf. Technol. 95, 1339–1355 (2018). https://doi.org/10.1007/s00170-017-1306-x S. Yoon, R. Ueji, H. Fujii, Effect of initial microstructure on Ti–6Al–4V joint by friction stir welding. Mater. Des. 88, 1269–1276 (2015). https://doi.org/10.1016/j.matdes.2015.09.128 G. Çam, S. Mistikoglu, Recent developments in friction stir welding of al-Alloys. J. Mater. Eng. Perform. 23, 1936–1953 (2014). https://doi.org/10.1007/s11665-014-0968-x S. Lazarevic, S.F. Miller, J. Li, B.E. Carlson, Experimental analysis of friction stir forming for dissimilar material joining application. J. Manuf. Process. 15, 616–624 (2013). https://doi.org/10.1016/j.jmapro.2013.05.004 F. Pan, A. Xu, D. Deng et al., Effects of friction stir welding on microstructure and mechanical properties of magnesium alloy Mg–5Al–3Sn. Mater. Des. 110, 266–274 (2016). https://doi.org/10.1016/j.matdes.2016.07.146 L.R. Medsker, Microcomputer applications of hybrid intelligent systems. J. Netw. Comput. Appl. 19, 213–234 (1996). https://doi.org/10.1006/jnca.1996.0015 M.A. Guvenc, H. Kapusuz, S. Mistikoglu, Experimental study on accelerometer-based ladle slag detection in continuous casting process. Int. J. Adv. Manuf. Technol. 106, 2983–2993 (2020). https://doi.org/10.1007/s00170-019-04830-8 S.A. Kalogirou, Artificial intelligence for the modeling and control of combustion processes: a review. Prog. Energy Combust. Sci. 29(6), 515–566 (2003) S.A. Kalogirou, Artificial Intelligence in Energy and Renewable Energy Systems (Nova Publisher, New York, 2007) C.C. Kao, A.J. Shih, Design and tuning of a fuzzy logic controller for micro-hole electrical discharge machining. J. Manuf. Process. 10, 61–73 (2008). https://doi.org/10.1016/j.jmapro.2009.03.001 M.Q. Raza, A. Khosravi, A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renew. Sustain. Energy Rev. 50, 1352–1372 (2015). https://doi.org/10.1016/j.rser.2015.04.065 R. Liu, B. Yang, E. Zio, X. Chen, Artificial intelligence for fault diagnosis of rotating machinery: a review. Mech. Syst. Signal Process. 108, 33–47 (2018). https://doi.org/10.1016/j.ymssp.2018.02.016 A. Mellit, Artificial intelligence technique for modelling and forecasting of solar radiation data: a review. Int. J. Artif. Intell. Soft Comput. 1, 52 (2008). https://doi.org/10.1504/ijaisc.2008.021264 B. Li, B. Hou, W. Yu et al., Applications of artificial intelligence in intelligent manufacturing: a review. Front. Inf. Technol. Electron. Eng. 18, 86–96 (2017). https://doi.org/10.1631/FITEE.1601885 M.A. Mat Daut, M.Y. Hassan, H. Abdullah et al., Building electrical energy consumption forecasting analysis using conventional and artificial intelligence methods: a review. Renew. Sustain. Energy Rev. 70, 1108–1118 (2017). https://doi.org/10.1016/j.rser.2016.12.015 Z. Ezziane, Applications of artificial intelligence in bioinformatics: a review. Expert Syst. Appl. 30, 2–10 (2006). https://doi.org/10.1016/j.eswa.2005.09.042 B. Yegnanarayana, Artificial neural networks (PHI Learning Pvt. Ltd., 2009) E. Öztemel, Yapay Si̇ni̇r Ağlari. Papatya Yayıncılık (2008) X. Yao, Evolving artificial neural networks. Proc. IEEE 87, 1423–1447 (1999). https://doi.org/10.1063/1.1661357 H. Kukreja, N. Bharath, C.S. Siddesh, S. Kuldeep, An introduction to artificial neural network. Int J Adv Res Innov Ideas Educ 1, 27–30 (2016) A.A. Tofigh, M.R. Rahimipour, M.O. Shabani et al., Optimized processing power and trainability of neural network in numerical modeling of Al Matrix nano composites. J. Manuf. Process. 15, 518–523 (2013). https://doi.org/10.1016/j.jmapro.2013.08.004 Y.F. Zhang, A.Y.C. Nee, J.Y.H. Fuh et al., A neural network approach to determining optimal inspection sampling size for CMM. Comput. Integr. Manuf. Syst. 9, 161–169 (1996). https://doi.org/10.1016/S0951-5240(96)00005-5 U. Orhan, M. Hekim, M. Ozer, EEG signals classification using the K-means clustering and a multilayer perceptron neural network model. Expert Syst. Appl. 38, 13475–13481 (2011). https://doi.org/10.1016/j.eswa.2011.04.149 G.A. Carpenter, S. Grossberg, The ART of adaptive pattern recognition by a self-organizing neural network. Computer 21, 77–88 (1988). https://doi.org/10.1109/2.33 G. Pajares, A hopfield neural network for image change detection. IEEE Trans. Neural Netw. 17, 1250–1264 (2006). https://doi.org/10.1109/TNN.2006.875978 N.F. Güler, E.D. Übeyli, I. Güler, Recurrent neural networks employing Lyapunov exponents for EEG signals classification. Expert Syst. Appl. 29, 506–514 (2005). https://doi.org/10.1016/j.eswa.2005.04.011 L. Shang, J. Cheng Lv, Z. Yi, Rigid medical image registration using PCA neural network. Neurocomputing 69, 1717–1722 (2006). https://doi.org/10.1016/j.neucom.2006.01.007 V. Singh, S. Mohan Rao, Application of image processing and radial basis neural network techniques for ore sorting and ore classification. Miner. Eng. 18, 1412–1420 (2005). https://doi.org/10.1016/j.mineng.2005.03.003 G. Cheng, Y.L. Cheng, L.H. Shen et al., Gear fault identification based on Hilbert–Huang transform and SOM neural network. Meas. J. Int. Meas. Confed 46, 1137–1146 (2013). https://doi.org/10.1016/j.measurement.2012.10.026 M.N. Jha, D.K. Pratihar, A.V. Bapat et al., Knowledge-based systems using neural networks for electron beam welding process of reactive material (Zircaloy-4). J. Intell. Manuf. 25, 1315–1333 (2014). https://doi.org/10.1007/s10845-013-0732-3 L.A. Zadeh, Fuzzy sets. Inf. Control 8, 338–353 (1965) A. Kaufmann, M. Gupta, Fuzzy Mathematical Models in Engineering and Management Science (Elsevier, Amsterdam, 1988) M.R.H. Mohd Adnan, A. Sarkheyli, A. Mohd Zain, H. Haron, Fuzzy logic for modeling machining process: a review. Artif. Intell. Rev. 43, 345–379 (2013). https://doi.org/10.1007/s10462-012-9381-8 S. Farajdadian, S.M.H. Hosseini, Design of an optimal fuzzy controller to obtain maximum power in solar power generation system. Sol. Energy 182, 161–178 (2019). https://doi.org/10.1016/j.solener.2019.02.051 C. Caraveo, F. Valdez, O. Castillo, Optimization of fuzzy controller design using a new bee colony algorithm with fuzzy dynamic parameter adaptation. Appl. Soft. Comput. J. 43, 131–142 (2016). https://doi.org/10.1016/j.asoc.2016.02.033 J. Cervantes, W. Yu, S. Salazar, I. Chairez, Takagi–Sugeno dynamic neuro-fuzzy controller of uncertain nonlinear systems. IEEE Trans. Fuzzy Syst. 25, 1601–1615 (2017). https://doi.org/10.1109/TFUZZ.2016.2612697 M. Azizi, S.A. Mousavi Ghasemi, R.G. Ejlali, S. Talatahari, Optimum design of fuzzy controller using hybrid ant lion optimizer and Jaya algorithm. Artif. Intell. Rev. 53, 1553–1584 (2020). https://doi.org/10.1007/s10462-019-09713-8 Y. Çelik, İ. Yıldız, A.T. Karadeniz, Son Üç Yılda Geliştirilen Metasezgisel Algoritmalar Hakkında Kısa Bir İnceleme. Eur. J. Sci. Technol. (2019). https://doi.org/10.31590/ejosat.638431 R.D. Koyee, U. Heisel, R. Eisseler, S. Schmauder, Modeling and optimization of turning duplex stainless steels. J. Manuf. Process. 16, 451–467 (2014). https://doi.org/10.1016/j.jmapro.2014.05.004 A.R.F. Pinto, A.F. Crepaldi, M.S. Nagano, A genetic algorithm applied to pick sequencing for billing. J. Intell. Manuf. 29, 405–422 (2018). https://doi.org/10.1007/s10845-015-1116-7 V. Riahi, M. Khorramizadeh, M.A. Hakim Newton, A. Sattar, Scatter search for mixed blocking flowshop scheduling. Expert Syst. Appl. 79, 20–32 (2017). https://doi.org/10.1016/j.eswa.2017.02.027 K.M. El-Naggar, M.R. AlRashidi, M.F. AlHajri, A.K. Al-Othman, Simulated Annealing algorithm for photovoltaic parameters identification. Sol. Energy 86, 266–274 (2012). https://doi.org/10.1016/j.solener.2011.09.032 M. Qiu, Z. Fu, R. Eglese, Q. Tang, A Tabu Search algorithm for the vehicle routing problem with discrete split deliveries and pickups. Comput. Oper. Res. 100, 102–116 (2018). https://doi.org/10.1016/j.cor.2018.07.021 A.Y. Hatata, G. Osman, M.M. Aladl, An optimization method for sizing a solar/wind/battery hybrid power system based on the artificial immune system. Sustain. Energy Technol. Assess. 27, 83–93 (2018). https://doi.org/10.1016/j.seta.2018.03.002 Y. Mokhtari, D. Rekioua, High performance of maximum power point tracking using ant colony algorithm in wind turbine. Renew. Energy 126, 1055–1063 (2018). https://doi.org/10.1016/j.renene.2018.03.049 T. Vivekanandan, N.C. Sriman Narayana Iyengar, Optimal feature selection using a modified differential evolution algorithm and its effectiveness for prediction of heart disease. Comput. Biol. Med. 90, 125–136 (2017). https://doi.org/10.1016/j.compbiomed.2017.09.011 M. Imran, N.A. Pambudi, M. Farooq, Thermal and hydraulic optimization of plate heat exchanger using multi objective genetic algorithm. Case Stud. Therm. Eng. 10, 570–578 (2017). https://doi.org/10.1016/j.csite.2017.10.003 Y. Ye, C.B. Yin, Y. Gong, J. Zhou, Position control of nonlinear hydraulic system using an improved PSO based PID controller. Mech. Syst. Signal Process. 83, 241–259 (2017). https://doi.org/10.1016/j.ymssp.2016.06.010 M.A. Al Dossary, H. Nasrabadi, Well placement optimization using imperialist competitive algorithm. J. Pet. Sci. Eng. 147, 237–248 (2016). https://doi.org/10.1016/j.petrol.2016.06.017 J. Upendar, C.P. Gupta, G.K. Singh, G. Ramakrishna, PSO and ANN-based fault classification for protective relaying. IET Gener. Transm. Distrib. 4, 1197–1212 (2010). https://doi.org/10.1049/iet-gtd.2009.0488 M.H.N. Tayarani, X. Yao, H. Xu, Meta-heuristic algorithms in car engine design: a literature survey. IEEE Trans. Evol. Comput. 19, 609–629 (2015). https://doi.org/10.1109/TEVC.2014.2355174 N. Muhammad, Y.H.P. Manurung, R. Jaafar et al., Model development for quality features of resistance spot welding using multi-objective Taguchi method and response surface methodology. J. Intell. Manuf. 24, 1175–1183 (2013). https://doi.org/10.1007/s10845-012-0648-3 D. Karaboga, B. Basturk, A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. J Glob Optim 39, 459–471 (2007). https://doi.org/10.1007/s10898-007-9149-x M. Singh, B.K. Panigrahi, A.R. Abhyankar, Optimal coordination of directional over-current relays using teaching learning-based optimization (TLBO) algorithm. Int. J. Electr. Power Energy Syst. 50, 33–41 (2013). https://doi.org/10.1016/j.ijepes.2013.02.011 D. Wang, Y. Zhao, C. Yi et al., Sparsity guided empirical wavelet transform for fault diagnosis of rolling element bearings. Mech. Syst. Signal Process. 101, 292–308 (2018). https://doi.org/10.1016/j.ymssp.2017.08.038 W. Qiao, Z. Yang, Forecast the electricity price of U.S. using a wavelet transform-based hybrid model. Energy 193, 116704 (2020). https://doi.org/10.1016/j.energy.2019.116704 Ö. Bozoglu, T. Baran, Yağış Serilerinin Wavelet Dalgacık Dönüşümü ile Analizi. VII Uluslal Hidroloji Kongresi (2012) S. Kumari, R. Jain, U. Kumar et al., Defect identification in friction stir welding using continuous wavelet transform. J. Intell. Manuf. 30, 483–494 (2019). https://doi.org/10.1007/s10845-016-1259-1 Á. García, O. Anjos, C. Iglesias et al., Prediction of mechanical strength of cork under compression using machine learning techniques. Mater. Des. 82, 304–311 (2015). https://doi.org/10.1016/j.matdes.2015.03.038 J. Xiong, S.Q. Shi, T.Y. Zhang, A machine-learning approach to predicting and understanding the properties of amorphous metallic alloys. Mater. Des. 187, 108378 (2020). https://doi.org/10.1016/j.matdes.2019.108378 S.B. Kotsiantis, Supervised machine learning: a review of classification techniques. Hyperfine Interact. 160, 249–268 (2007). https://doi.org/10.1007/s10751-016-1232-6 S. Mullainathan, J. Spiess, Machine learning: an applied econometric approach. J. Econ. Perspect. 31, 87–106 (2017). https://doi.org/10.1257/jep.31.2.87 S. Wang, B. Ji, J. Zhao et al., Predicting ship fuel consumption based on LASSO regression. Transp. Res. D Transp. Environ. 65, 817–824 (2018). https://doi.org/10.1016/j.trd.2017.09.014 P. Probst, M.N. Wright, A.L. Boulesteix, Hyperparameters and tuning strategies for random forest. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 9, 1–15 (2019). https://doi.org/10.1002/widm.1301 B. Choubin, E. Moradi, M. Golshan et al., An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. Sci. Total Environ. 651, 2087–2096 (2019). https://doi.org/10.1016/j.scitotenv.2018.10.064 S. Huang, C.A.I. Nianguang, P. Penzuti Pacheco et al., Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genomics Proteomics 15, 41–51 (2018). https://doi.org/10.21873/cgp.20063 P. Markopoulos, Linear discriminant analysis with few training data. IEEE Int. Conf. Acoust. Speech Signal Process. 2017, 4626–4630 (2017). https://doi.org/10.1103/PhysRevLett.116.151104 M. Hasanipanah, M. Noorian-Bidgoli, D. Jahed Armaghani, H. Khamesi, Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling. Eng. Comput. 32, 705–715 (2016). https://doi.org/10.1007/s00366-016-0447-0 H. Yang, M. Hasanipanah, M.M. Tahir, D.T. Bui, Intelligent prediction of blasting-induced ground vibration using ANFIS optimized by GA and PSO. Nat. Resour. Res. 29, 739–750 (2020). https://doi.org/10.1007/s11053-019-09515-3 F. Beloufa, M.A. Chikh, Design of fuzzy classifier for diabetes disease using modified artificial bee colony algorithm. Comput. Methods Programs Biomed. 112, 92–103 (2013). https://doi.org/10.1016/j.cmpb.2013.07.009 O. Kadri, L.H. Mouss, M.D. Mouss, Fault diagnosis of rotary kiln using SVM and binary ACO. J. Mech. Sci. Technol. 26, 601–608 (2012). https://doi.org/10.1007/s12206-011-1216-z A. Galvez, A. Iglesias, A new iterative mutually coupled hybrid GA-PSO approach for curve fitting in manufacturing. Appl. Soft Comput. J. 13, 1491–1504 (2013). https://doi.org/10.1016/j.asoc.2012.05.030 O. Ibáñez, O. Cordón, S. Damas, L.M. European, A review on the application of hybrid artificial intelligence systems to optimization problems in operations management. In: International Conference on Hybrid Artificial Intelligence Systems, pp 360–367 (2009) Y. Zhou, S. Guo, F.J. Chang, Explore an evolutionary recurrent ANFIS for modelling multi-step-ahead flood forecasts. J. Hydrol. 570, 343–355 (2019). https://doi.org/10.1016/j.jhydrol.2018.12.040 S. Barak, S.S. Sadegh, Forecasting energy consumption using ensemble ARIMA-ANFIS hybrid algorithm. Int. J. Electr. Power Energy Syst. 82, 92–104 (2016). https://doi.org/10.1016/j.ijepes.2016.03.012 L.H. Tsoukalas, R.E. Uhrig, Fuzzy and neural approaches in engineering (Wiley, New York, 1996) A.A.A.A. Alrashed, M.S. Gharibdousti, M. Goodarzi et al., Effects on thermophysical properties of carbon based nanofluids: Experimental data, modelling using regression, ANFIS and ANN. Int. J. Heat Mass Transf. 125, 920–932 (2018). https://doi.org/10.1016/j.ijheatmasstransfer.2018.04.142 H. Tran-Ngoc, S. Khatir, G. De Roeck et al., An efficient artificial neural network for damage detection in bridges and beam-like structures by improving training parameters using cuckoo search algorithm. Eng. Struct. 199, 109637 (2019). https://doi.org/10.1016/j.engstruct.2019.109637 E.M. Golafshani, A. Behnood, M. Arashpour, Predicting the compressive strength of normal and high-performance concretes using ANN and ANFIS hybridized with Grey Wolf Optimizer. Constr. Build. Mater. 232, 117266 (2020). https://doi.org/10.1016/j.conbuildmat.2019.117266 D.K. Bui, T. Nguyen, J.S. Chou et al., A modified firefly algorithm-artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete. Constr. Build. Mater. 180, 320–333 (2018). https://doi.org/10.1016/j.conbuildmat.2018.05.201 K. Zhang, Y. Chen, J. Zheng et al., Adaptive filling modeling of butt joints using genetic algorithm and neural network for laser welding with filler wire. J. Manuf. Process. 30, 553–561 (2017). https://doi.org/10.1016/j.jmapro.2017.10.025 M. Kaur, S. Ghosh, Network reconfiguration of unbalanced distribution networks using fuzzy-firefly algorithm. Appl. Soft Comput. J. 49, 868–886 (2016). https://doi.org/10.1016/j.asoc.2016.09.019 H. Tao, L. Diop, A. Bodian et al., Reference evapotranspiration prediction using hybridized fuzzy model with firefly algorithm: regional case study in Burkina Faso. Agric. Water Manag. 208, 140–151 (2018). https://doi.org/10.1016/j.agwat.2018.06.018 A. Al-Mamun, Z. Zhu, PSO-optimized fuzzy logic controller for a single wheel robot. Commun. Comput. Inf. Sci. 103, 330–337 (2010). https://doi.org/10.1007/978-3-642-15810-0_42 M.S. Alam, M.O. Tokhi, Hybrid fuzzy logic control with genetic optimisation for a single-link flexible manipulator. Eng. Appl. Artif. Intell. 21, 858–873 (2008). https://doi.org/10.1016/j.engappai.2007.08.002 S.V. Ustun, M. Demirtas, Optimal tuning of PI coefficients by using fuzzy-genetic for V/f controlled induction motor. Expert Syst. Appl. 34, 2714–2720 (2008). https://doi.org/10.1016/j.eswa.2007.05.029 H. Lu, Y. Li, M. Chen et al., Brain intelligence: go beyond artificial intelligence. Mob Netw. Appl. 23, 368–375 (2018). https://doi.org/10.1007/s11036-017-0932-8 D.I. Patrício, R. Rieder, Computer vision and artificial intelligence in precision agriculture for grain crops: a systematic review. Comput. Electron. Agric. 153, 69–81 (2018). https://doi.org/10.1016/j.compag.2018.08.001 K.W. Johnson, J. Torres Soto, B.S. Glicksberg et al., Artificial intelligence in cardiology. J. Am. Coll. Cardiol. 71, 2668–2679 (2018). https://doi.org/10.1016/j.jacc.2018.03.521 F. Shi, J. Wang, J. Shi et al., Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID-19. IEEE Rev. Biomed. Eng. (2020). https://doi.org/10.1109/RBME.2020.2987975 T. Rajaee, H. Ebrahimi, V. Nourani, A review of the artificial intelligence methods in groundwater level modeling. J. Hydrol. 572, 336–351 (2019). https://doi.org/10.1016/j.jhydrol.2018.12.037 P.L. Narayana, S.W. Kim, J.K. Hong et al., Estimation of transformation temperatures in Ti–Ni–Pd shape memory alloys. Met. Mater. Int. 24, 919–925 (2018). https://doi.org/10.1007/s12540-018-0109-4 P. Dande, P. Samant, Acquaintance to artificial neural networks and use of artificial intelligence as a diagnostic tool for tuberculosis: a review. Tuberculosis 108, 1–9 (2018). https://doi.org/10.1016/j.tube.2017.09.006 C. González García, E. Núñez-Valdez, V. García-Díaz et al., A review of artificial intelligence in the internet of things. Int. J. Interact. Multimed. Artif. Intell. 5, 9 (2019). https://doi.org/10.9781/ijimai.2018.03.004 H. Okuyucu, A. Kurt, E. Arcaklioglu, Artificial neural network application to the friction stir welding of aluminum plates. Mater. Des. 28, 78–84 (2007). https://doi.org/10.1016/j.matdes.2005.06.003 R. Hartl, B. Praehofer, M.F. Zaeh, Prediction of the surface quality of friction stir welds by the analysis of process data using artificial neural networks. Proc. Inst. Mech. Eng. L J. Mater. Des. Appl. (2020). https://doi.org/10.1177/1464420719899685 A. Ghasemi-Kahrizsangi, S.F. Kashani-Bozorg, M. Moshref-Javadi, M. Sharififar, Friction stir processing of mild steel/Al2O3 nanocomposite: modeling and experimental studies. Metallogr. Microstruct. Anal. 4, 122–130 (2015). https://doi.org/10.1007/s13632-015-0193-5 D. Raguraman, D. Muruganandam, N. Senthilkumar, L.A. Kumaraswami Dhas, Tensile strength prediction on different FSW tools using ANN and regression equations. Int. J. Appl. Eng. Res. 10, 545–551 (2015) E. Maleki, Artificial neural networks application for modeling of friction stir welding effects on mechanical properties of 7075-T6 aluminum alloy. IOP Conf. Ser. Mater. Sci. Eng. (2015). https://doi.org/10.1088/1757-899X/103/1/012034 G. Buffa, L. Fratini, F. Micari, Mechanical and microstructural properties prediction by artificial neural networks in FSW processes of dual phase titanium alloys. J. Manuf. Process. 14, 289–296 (2012). https://doi.org/10.1016/j.jmapro.2011.10.007 I. Dinaharan, R. Palanivel, N. Murugan, R.F. Laubscher, Predicting the wear rate of AA6082 aluminum surface composites produced by friction stir processing via artificial neural network. Multidiscip. Model. Mater. Struct. 16, 409–423 (2019). https://doi.org/10.1108/MMMS-05-2019-0102 M. Kurtulmuş, A. Kiraz, Artificial neural network modelling for polyethylene FSSW parameters. Sci. Iran. 25, 1266–1271 (2018). https://doi.org/10.24200/sci.2018.50030.1473 V.M. Dehabadi, S. Ghorbanpour, G. Azimi, Application of artificial neural network to predict Vickers microhardness of AA6061 friction stir welded sheets. J. Cent. South Univ. 23, 2146–2155 (2016). https://doi.org/10.1007/s11771-016-3271-1 B.M. Darras, I.M. Deiab, A. Naser, Prediction of friction stir processed AZ31 magnesium alloy micro-hardness using artificial neural networks. Adv. Mater. Res. 1043, 91–95 (2014). https://doi.org/10.4028/www.scientific.net/AMR.1043.91 L.A.C. De Filippis, L.M. Serio, F. Facchini et al., Prediction of the vickers microhardness and ultimate tensile strength of aa5754 h111 friction stir welding butt joints using artificial neural network. Materials (2016). https://doi.org/10.3390/ma9110915 R.V. Vignesh, R. Padmanaban, Comparison of ANN training algorithms for predicting the tensile strength of friction stir welded aluminium alloy AA1100. Int. J. Veh. Struct. Syst. 10, 98–102 (2018). https://doi.org/10.4273/ijvss.10.2.05 R. Vaira Vignesh, R. Padmanaban, Artificial neural network model for predicting the tensile strength of friction stir welded aluminium alloy AA1100. Mater. Today Proc. 5, 16716–16723 (2018). https://doi.org/10.1016/j.matpr.2018.06.035 R. Palanivel, R.F. Laubscher, I. Dinaharan, N. Murugan, Tensile strength prediction of dissimilar friction stir-welded AA6351–AA5083 using artificial neural network technique. J. Braz. Soc. Mech. Sci. Eng. 38, 1647–1657 (2016). https://doi.org/10.1007/s40430-015-0483-5 M. Vangalapati, K. Balaji, A. Gopichand, ANN modeling and analysis of friction welded AA6061 aluminum alloy. Mater. Today Proc. 18, 3357–3364 (2019). https://doi.org/10.1016/j.matpr.2019.07.258 L. Fratini, G. Buffa, Continuous dynamic recrystallization phenomena modelling in friction stir welding of aluminium alloys: a neural-network-based approach. Proc. Inst. Mech. Eng. B J. Eng. Manuf. 221, 857–864 (2007). https://doi.org/10.1243/09544054JEM674 L. Fratini, G. Buffa, Metallurgical phenomena modeling in friction stir welding of aluminium alloys: analytical versus neural network based approaches. J. Eng. Mater. Technol. Trans. ASME 130, 0310011–0310016 (2008). https://doi.org/10.1115/1.2931142 R. Harikeshava, M.S. Srinivasan, R.V. Vignesh. R. Padmanaban, ANN model for predicting the intergranular corrosion susceptibility of friction stir processed aluminium alloy AA5083. in: 2017 2nd International Conference on Communication and Electronics Systems (ICCES) (IEEE, pp. 716–720) A. Paoletti, F. Lambiase, A. Di Ilio, Optimization of friction stir welding of thermoplastics. Procedia CIRP 33, 562–567 (2015). https://doi.org/10.1016/j.procir.2015.06.078 M.H. Shojaeefard, M. Akbari, M. Tahani, F. Farhani, Sensitivity analysis of the artificial neural network outputs in friction stir lap joining of aluminum to brass. Adv. Mater. Sci. Eng. (2013). https://doi.org/10.1155/2013/574914 M.H. Shojaeefard, A. Khalkhali, M. Akbari, P. Asadi, Investigation of friction stir welding tool parameters using FEM and neural network. Proc. Inst. Mech. Eng. L J. Mater. Des. Appl. 229, 209–217 (2015). https://doi.org/10.1177/1464420713509075 F. Sonmez, H. Basak, S. Baday, The mechanical strength of aluminum alloys which are joined with friction stir welding modelling with artificial neural networks (2017). https://doi.org/10.1109/idap.2017.8090325 H. Wang, P.A. Colegrove, J. dos Santos, Hybrid modelling of 7449-T7 aluminium alloy friction stir welded joints. Sci. Technol. Weld. Join. 18, 147–153 (2013). https://doi.org/10.1179/1362171812Y.0000000078 R. Raja, Wear rate prediction of friction stir welded dissimilar aluminum alloy by ANN. Int. J. Mech. Prod. Eng. Res. Dev. 8, 887–892 (2018). https://doi.org/10.24247/ijmperdjun201893 H.K. Mohanty, M.M. Mahapatra, P. Kumar et al., Predicting the effects of tool geometries on friction stirred aluminium welds using artificial neural networks and fuzzy logic techniques. Int. J. Manuf. Res. 8, 296–312 (2013). https://doi.org/10.1504/IJMR.2013.055245 H. Zhang, Y. Zhang, D. Dai et al., Modelling and optimization of the superconducting transition temperature. Mater. Des. 92, 371–377 (2016). https://doi.org/10.1016/j.matdes.2015.12.081 Astuti W. Armansyah, J. Saedon, Development of prediction system model for mechanical property in friction stir welding using support vector machine (SVM). J. Mech. Eng. 5, 216–225 (2018) A. D’Orazio, A. Forcellese, M. Simoncini, Prediction of the vertical force during FSW of AZ31 magnesium alloy sheets using an artificial neural network-based model. Neural Comput. Appl. 31, 7211–7226 (2019). https://doi.org/10.1007/s00521-018-3562-6 B. Das, S. Pal, S. Bag, Torque based defect detection and weld quality modelling in friction stir welding process. J. Manuf. Process. 27, 8–17 (2017). https://doi.org/10.1016/j.jmapro.2017.03.012 B. Kosko, A multi-objective optimization application in friction stir welding: Considering thermo-mechanical aspects. in: 2010 IEEE World Congress Computing Intelligence WCCI 2010–2010 IEEE Congress on Evolutionary Computing CEC 2010, pp. 1–8 (2010). https://doi.org/10.1109/CEC.2010.5586482 R.S.S. Prasanth, K. Hans Raj, Determination of optimal process parameters of friction stir welding to join dissimilar aluminum alloys using artificial bee colony algorithm. Trans. Indian Inst. Met. 71, 453–462 (2018). https://doi.org/10.1007/s12666-017-1176-9 E. Jiménez-Macías, A. Sánchez-Roca, H. Carvajal-Fals et al., Wavelets application in prediction of friction stir welding parameters of alloy joints from vibroacoustic ANN-based model. Abstr. Appl. Anal. (2014). https://doi.org/10.1155/2014/728564 B. Das, S. Pal, S. Bag, Weld quality prediction in friction stir welding using wavelet analysis. Int. J. Adv. Manuf. Technol. 89, 711–725 (2017). https://doi.org/10.1007/s00170-016-9140-0 M. Akbari, P. Asadi, P. Zolghadr, A. Khalkhali, Multicriteria optimization of mechanical properties of aluminum composites reinforced with different reinforcing particles type. Proc. Inst. Mech. Eng. E J. Process. Mech. Eng. 232, 323–337 (2018). https://doi.org/10.1177/0954408917704994 H. Atharifar, Optimum parameters design for friction stir spot welding using a genetically optimized neural network system. Proc. Inst. Mech. Eng. B J. Eng. Manuf. 224, 403–418 (2010). https://doi.org/10.1243/09544054JEM1467 S.K. Gupta, K.N. Pandey, R. Kumar, Artificial intelligence-based modelling and multi-objective optimization of friction stir welding of dissimilar AA5083-O and AA6063-T6 aluminium alloys. Proc. Inst. Mech. Eng. L J. Mater. Des. Appl. 232, 333–342 (2018). https://doi.org/10.1177/1464420715627293 M. Akbari, M.H. Shojaeefard, P. Asadi, A. Khalkhalı, Hybrid multi-objective optimization of microstructural and mechanical properties of B4C/A356 composites fabricated by FSP using TOPSIS and modified NSGA-II. Trans. Nonferrous Met. Soc. China (Engl. Ed.) 27, 2317–2333 (2017). https://doi.org/10.1016/S1003-6326(17)60258-9 A. Khalkhali, S. Ebrahimi-Nejad, N.G. Malek, Comprehensive optimization of friction stir weld parameters of lap joint AA1100 plates using artificial neural networks and modified NSGA-II. Mater. Res. Express (2018). https://doi.org/10.1088/2053-1591/aac6f6 M.H. Shojaeefard, R.A. Behnagh, M. 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. Mater. Des. 44, 190–198 (2013). https://doi.org/10.1016/j.matdes.2012.07.025 K.N. Wakchaure, A.G. Thakur, V. Gadakh, A. Kumar, Multi-objective optimization of friction stir welding of aluminium alloy 6082-T6 using hybrid Taguchi–Grey relation analysis–ANN method. Mater. Today Proc. 5, 7150–7159 (2018). https://doi.org/10.1016/j.matpr.2017.11.380 N.F. Alkayem, B. Parida, S. Pal, Optimization of friction stir welding process parameters using soft computing techniques. Soft. Comput. 21, 7083–7098 (2017). https://doi.org/10.1007/s00500-016-2251-6 N.F. Alkayem, B. Parida, S. Pal, Optimization of friction stir welding process using NSGA-II and DEMO. Neural Comput. Appl. 31, 947–956 (2019). https://doi.org/10.1007/s00521-017-3059-8 I.N. Tansel, M. Demetgul, H. Okuyucu, A. Yapici, Optimizations of friction stir welding of aluminum alloy by using genetically optimized neural network. Int. J. Adv. Manuf. Technol. 48, 95–101 (2010). https://doi.org/10.1007/s00170-009-2266-6 Q. Song, Z. Ren, S. Ji et al., Improving the mechanical property of dissimilar Al/Mg Zn-added ultrasound-assisted friction stir lap welding joint by back propagation neural network-gray wolf optimization algorithm. Adv. Eng. Mater. 21, 1–8 (2019). https://doi.org/10.1002/adem.201900973 S. Mirjalili, S.M. Mirjalili, A. Lewis, Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014). https://doi.org/10.1016/j.advengsoft.2013.12.007 M.A. Wahid, S. Masood, Z.A. Khan et al., A simulation-based study on the effect of underwater friction stir welding process parameters using different evolutionary optimization algorithms. Proc. Inst. Mech. Eng. C J. Mech. Eng. Sci. 234, 643–657 (2019). https://doi.org/10.1177/0954406219883904 D.J. Huggett, T.W. Liao, M.A. Wahab, A. Okeil, Prediction of friction stir weld quality without and with signal features. Int. J. Adv. Manuf. Technol. 95, 1989–2003 (2018). https://doi.org/10.1007/s00170-017-1403-x R. Teimouri, H. Baseri, Forward and backward predictions of the friction stir welding parameters using fuzzy-artificial bee colony-imperialist competitive algorithm systems. J. Intell. Manuf. 26, 307–319 (2015). https://doi.org/10.1007/s10845-013-0784-4 M.W. Dewan, D.J. Huggett, T. Warren Liao et al., Prediction of tensile strength of friction stir weld joints with adaptive neuro-fuzzy inference system (ANFIS) and neural network. Mater. Des. 92, 288–299 (2016). https://doi.org/10.1016/j.matdes.2015.12.005