AccScience Publishing / IJAMD / Volume 2 / Issue 4 / DOI: 10.36922/IJAMD025360032
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ORIGINAL RESEARCH ARTICLE

Machine learning and exploratory data analysis for predicting tensile and thermal responses in friction stir spot welding

Sajad N. Alasadi1 ,  Raheem Al-Sabur1*
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1 Department of Mechanical Engineering College, University of Basrah, Basrah, Iraq
IJAMD 2025, 2(4), 37–51; https://doi.org/10.36922/IJAMD025360032
Received: 1 September 2025 | Revised: 15 December 2025 | Accepted: 16 December 2025 | Published online: 30 December 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Friction stir spot welding (FSSW) has gained increasing attention over the last decade due to its promising performance compared to conventional joining methods for similar metals. However, the thermal and tensile responses in this process are highly nonlinear. This study aims to explore the thermal and tensile performance of aluminum joints welded by FSSW using an innovative method based on exploratory data analysis (EDA), followed by several machine learning (ML) approaches. The welding parameters investigated in this study were tool rotational speed, dwelling time, and aluminum sheet thickness. The ML methods included linear and nonlinear regression models for welded joints at different welding parameters. We evaluated Bayesian ridge, elastic-net, support vector regression (SVR), random forest, polynomial regression (nonlinear), and robust regression. The random forest algorithm provided accurate predictions for lap-shear fracture load (R2 = 0.96, mean squared error [MSE] = 0.01, and mean absolute error [MAE] = 0.07) in tensile performance, whereas the elastic net performed worst. Model-to-model differences were smaller for thermal performance, with the random forest model yielding the most accurate predictions (R2 = 0.97, MSE = 26.51, and MAE = 3.86) while the SVR yielded the least accurate predictions. The study indicated that using EDA to address anomalies in welding conditions provides valuable insights into the best ML methods for predicting the thermal and mechanical performance of welding joints.

Graphical abstract
Keywords
Friction stir spot welding
Machine learning
Exploratory data analysis
Tensile performance
Thermal performance
Funding
None.
Conflict of interest
The authors declare that they have no competing interests.
References
  1. Chen H, Zhu Z, Zhu Y, Sun L, Guo Y. Solid-state welding of aluminum to magnesium alloys: A review. Metals (Basel). 2023;13(8):1410. doi: 10.3390/met13081410

 

  1. Khedr M, Hamada A, Järvenpää A, Elkatatny S, Abd-Elaziem W. Review on the solid-state welding of steels: Diffusion bonding and friction stir welding processes. Metals (Basel). 2023;13(1):54. doi: 10.3390/met13010054

 

  1. Badavath HJ, Chattopadhyay S, Shankar S. Solid-State Welding and its Applications: A Methodological Review. In: AIP Conference Proceedings. Vol. 2681; 2022. doi: 10.1063/5.0116893

 

  1. Gibson BT, Lammlein DH, Prater TJ, et al. Friction stir welding: Process, automation, and control. J Manuf Process. 2014;16(1):56-73. doi: 10.1016/j.jmapro.2013.04.002

 

  1. Majeed T, Wahid MA, Alam MN, Mehta Y, Siddiquee AN. Friction stir welding: A sustainable manufacturing process. In: Materials Today: Proceedings. Vol. 46; 2020. doi: 10.1016/j.matpr.2021.04.025

 

  1. Khalaf HI, Al-Sabur R. Friction stir techniques exploring fracture and fatigue behavior in hybrid composite joints. In: Utilizing Friction Stir Techniques for Composite Hybridization. United States: IGI Global; 2024. p. 108-134. doi: 10.4018/979-8-3693-3993-0.ch007

 

  1. Avettand-Fènoël MN, Simar A. A review about friction stir welding of metal matrix composites. Mater Charact. 2016;120:1-17. doi: 10.1016/j.matchar.2016.07.010

 

  1. Belalia SE, Serier M, Al-Sabur R. Parametric analysis for torque prediction in friction stir welding using machine learning and shapley additive explanations. J Computat Appl Mech. 2024;55(1):113-124. doi: 10.22059/JCAMECH.2024.370055.924

 

  1. Tiwari A, Singh P, Pankaj P, Biswas P, Kore SD. FSW of low carbon steel using tungsten carbide (WC-10wt.%Co) based tool material. J Mech Sci Technol. 2019;33(10):4931-4938. doi: 10.1007/s12206-019-0932-7

 

  1. Serier M, Jassim RJ, Al-Sabur R, Siddiquee AN. Thermal Diffusivity Modeling for Aluminum AA6060 Plates During Friction Stir Welding. In: AIP Conference Proceedings. Vol. 3051; 2024. doi: 10.1063/5.0191742

 

  1. Jassim AK, Al-Subar RK. Studying the possibility to Weld AA1100 aluminum alloy by friction stir spot welding. Int J Mater Metall Eng. 2017;11(9):660.

 

  1. Borah MJ, Saha N. Effect of Tool Geometry and Process Parameters on Strength of Various Friction Stir Spot Welded Lap Joints. In: Proceedings of the World Congress on Mechanical, Chemical, and Material Engineering; 2019. doi: 10.11159/icmie19.124

 

  1. Al‐Sabur R, Serier M. Material sustainability during friction stir joining. In: Sustainable Machining and Green Manufacturing. United States: Wiley; 2024. doi: 10.1002/9781394197866.ch7

 

  1. Sree Sabari S, Malarvizhi S, Balasubramanian V. Characteristics of FSW and UWFSW joints of AA2519-T87 aluminium alloy: Effect of tool rotation speed. J Manuf Process. 2016;22:278-289. doi: 10.1016/j.jmapro.2016.03.014

 

  1. Alasdi SN, Al-Sabur R. In-depth thermal analysis of different pin configurations in friction stir spot welding of similar and dissimilar alloys. J Manuf Mater Process. 2025;9(6):184. doi: 10.3390/jmmp9060184

 

  1. Marazani T, Jeje SO, Shongwe MB, Malatji N. Mass flash reduction strategies in friction stir processing of aluminum alloys: A review. Eng Rep. 2024;6(10):e12981. doi: 10.1002/eng2.12981

 

  1. Morgenthaler S. Exploratory data analysis. Wiley Interdiscip Rev Comput Stat. 2009;1(1):33-44. doi: 10.1002/wics.2

 

  1. Bezerra A, Silva I, Guedes LA, Silva D, Leitão G, Saito K. Extracting value from industrial alarms and events: A data-driven approach based on exploratory data analysis. Sensors (Basel). 2019;19(12):2772. doi: 10.3390/s19122772

 

  1. Mathew BK, Velmurugan C, Vanitha V. Estimating Most Influencing Parameter in TIG Welding of SS304 Using Exploratory Data Analysis. In: 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2021; 2021. doi: 10.1109/ICAECA52838.2021.9675521

 

  1. Wang LC. Experience of data analytics in EDA and test - Principles, promises, and challenges. IEEE Trans Comput Aided Des Integr Circ Syst. 2017;36(6):885-898. doi: 10.1109/TCAD.2016.2621883

 

  1. Alzubi J, Nayyar A, Kumar A. Machine learning from theory to algorithms: An overview. J Phys Conf Ser. 2018;1142:012012. doi: 10.1088/1742-6596/1142/1/012012

 

  1. Tufail S, Riggs H, Tariq M, Sarwat AI. Advancements and challenges in machine learning: A comprehensive review of models, libraries, applications, and algorithms. Electronics (Switzerland). 2023;12(8):1789. doi: 10.3390/electronics12081789

 

  1. Wang Z, Li L, Chen H, et al. Penetration recognition based on machine learning in arc welding: A review. Int J Adv Manuf Technol. 2023;125(9-10):3899-3923. doi: 10.1007/s00170-023-11035-7

 

  1. Maculotti G, Genta G, Galetto M. Optimisation of laser welding of deep drawing steel for automotive applications by Machine Learning: A comparison of different techniques. Qual Reliab Eng Int. 2024;40(1):202-219. doi: 10.1002/qre.3377

 

  1. Sun H, Ramuhalli P, Jacob RE. Machine learning for ultrasonic nondestructive examination of welding defects: A systematic review. Ultrasonics. 2023;127:106854. doi: 10.1016/j.ultras.2022.106854

 

  1. Zhou B, Pychynski T, Reischl M, Kharlamov E, Mikut R. Machine learning with domain knowledge for predictive quality monitoring in resistance spot welding. J Intell Manuf. 2022;33(4):1139-1163. doi: 10.1007/s10845-021-01892-y

 

  1. Yu F, Zhao Y, Lin Z, Miao Y, Zhao F, Xie Y. Prediction of mechanical properties and optimization of friction stir welded 2195 aluminum alloy based on BP neural network. Metals (Basel). 2023;13(2):267. doi: 10.3390/met13020267

 

  1. Verma S, Msomi V, Mabuwa S, et al. Machine learning application for evaluating the friction stir processing behavior of dissimilar aluminium alloys joint. Proc Instit Mech Eng L J Mater Des Appl. 2022;236(3):633-646. doi: 10.1177/14644207211053123

 

  1. Elsheikh AH. Applications of machine learning in friction stir welding: Prediction of joint properties, real-time control and tool failure diagnosis. Eng Appl Artif Intell. 2023;121:105961. doi: 10.1016/j.engappai.2023.105961

 

  1. Chadha U, Selvaraj SK, Gunreddy N, et al. A Survey of machine learning in friction stir welding, including unresolved issues and future research directions. Mater Des Process Commun. 2022;2022:2568347. doi: 10.1155/2022/2568347

 

  1. Ebnonnasir A, Karimzadeh F, Enayati MH. Novel artificial neural network model for evaluating hardness of stir zone of submerge friction stir processed Al 6061-T6 plate. Mater Sci Technol. 2011;27(6):990-995. doi: 10.1179/174328409X425290

 

  1. Dinaharan I, Palanivel R, Murugan N, Laubscher RF. Application of artificial neural network in predicting the wear rate of copper surface composites produced using friction stir processing. Aust J Mech Eng. 2022;20(4):1079-1090. doi: 10.1080/14484846.2020.1769803

 

  1. Pattanaik AK, Pal K, Mishra D. Tribiological investigation and optimization of friction stir spot welding of dissimilar metals by LSSM-ANN method. Mech Based Des Struct Mach. 2022;50(5):1595-1613. doi: 10.1080/15397734.2020.1759429

 

  1. Manvatkar VD, Arora A, De A, DebRoy T. Neural network models of peak temperature, torque, traverse force, bending stress and maximum shear stress during friction stir welding. Sci Technol Weld Join. 2012;17(6):460-466. doi: 10.1179/1362171812Y.0000000035

 

  1. Matitopanum S, Pitakaso R, Sethanan K, Srichok T, Chokanat P. Prediction of the ultimate tensile strength (UTS) of asymmetric friction stir welding using ensemble machine learning methods. Processes. 2023;11(2):460-466. doi: 10.3390/pr11020391

 

  1. Bhat NN, Kumari K, Dutta S, Pal SK, Pal S. Friction stir weld classification by applying wavelet analysis and support vector machine on weld surface images. J Manuf Process. 2015;20:274-281. doi: 10.1016/j.jmapro.2015.07.002

 

  1. Sefene EM, Tsegaw AA, Mishra A. Process parameter optimization of 6061AA friction stir welded joints using supervised machine learning regression-based algorithms. J Soft Comput Civil Eng. 2022;6(1):274-281. doi: 10.22115/SCCE.2022.299913.1350

 

  1. Bock FE, Paulsen T, Brkovic N, et al. Evaluation of Mechanical Property Predictions of refill Friction Stir Spot Welding Joints via Machine Learning Regression Analyses on DoE Data. In: ESAFORM 2021 - 24th International Conference on Material Forming; 2021. doi: 10.25518/esaform21.2589

 

  1. Mothilal M, Kumar A. Predictive modeling of ultimate tensile strength in dissimilar friction stir welded aluminum alloys via machine learning approach. Philos Mag Lett. 2025;105(1):2472669. doi: 10.1080/09500839.2025.2472669

 

  1. Sambath Y, Natarajan R, Babu PK, et al. Comparative analysis of predictive modeling techniques for mechanical properties in dissimilar friction stir welding of AA6061 and AZ31B. J Mater Eng Perform. 2025;34:15597-15613. doi: 10.1007/s11665-024-10317-9

 

  1. El-Said EMS, Abd Elaziz M, Elsheikh AH. Machine learning algorithms for improving the prediction of air injection effect on the thermohydraulic performance of shell and tube heat exchanger. Appl Therm Eng. 2021;185:116471. doi: 10.1016/j.applthermaleng.2020.116471

 

  1. Ramesh K, Ramana EV, Srikanth L, Sri Harsha C, Kiran Kumar N. Identification of SMAW surface weld defects using machine learning. In: Lecture Notes in Mechanical Engineering. Berlin: Springer; 2023. doi: 10.1007/978-981-19-5347-7_28

 

  1. Tsai YA, Lo YL, Raza MM, et al. Optimization of lap-joint laser welding parameters using high-fidelity simulations and machine learning mode. J Mater Res Technol. 2023;24:6876-6892. doi: 10.1016/j.jmrt.2023.04.256

 

  1. España VJ, Aparicio J, Barber X. An adaptation of Random Forest to estimate convex non‐parametric production technologies: An empirical illustration of efficiency measurement in education. Int Trans Oper Res. 2025;32(5):2523-2546. doi: 10.1111/itor.13561

 

  1. Tabib S, Larocque D. Non-parametric individual treatment effect estimation for survival data with random forests. Bioinformatics. 2020;36(2):629-636. doi: 10.1093/bioinformatics/btz602

 

  1. Zhang Z, Yang Z, Ren W, Wen G. Random forest-based real-time defect detection of Al alloy in robotic arc welding using optical spectrum. J Manuf Process. 2019;42:51-59. doi: 10.1016/j.jmapro.2019.04.023

 

  1. Zhu C, Yuan H, Ma G. An active visual monitoring method for GMAW weld surface defects based on random forest model. Mater Res Express. 2022;9(3):036503. doi: 10.1088/2053-1591/ac5a38

 

  1. Strobl C, Malley J, Tutz G. An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychol Methods. 2009;14(4):323-348. doi: 10.1037/a0016973

 

  1. Zhang Z, Lai Z, Xu Y, Shao L, Wu J, Xie GS. Discriminative elastic-net regularized linear regression. IEEE Trans Image Process. 2017;26(3):1466-1481. doi: 10.1109/TIP.2017.2651396

 

  1. Martín Ó, Ahedo V, Santos JI, De Tiedra P, Galán JM. Quality assessment of resistance spot welding joints of AISI 304 stainless steel based on elastic nets. Mater Sci Eng A. 2016;676:173-181. doi: 10.1016/j.msea.2016.08.112

 

  1. Michimae H, Emura T. Bayesian ridge estimators based on copula-based joint prior distributions for regression coefficients. Comput Stat. 2022;37(5):2741-2769. doi: 10.1007/s00180-022-01213-8

 

  1. Šket K, Brezočnik M, Karner T, et al. Predictive modelling of weld bead geometry in wire arc additive manufacturing. J Manuf Mater Process. 2025;9(2):67. doi: 10.3390/jmmp9020067

 

  1. Hinkle J, Muralidharan P, Fletcher PT, Joshi S. Polynomial regression on Riemannian manifolds. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol 7574. Berlin: Springer; 2012. doi: 10.1007/978-3-642-33712-3_1

 

  1. Liu C, Li B, Vorobeychik Y, Oprea A. Robust linear regression against training data poisoning. In: AISec 2017 - Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, Co-Located with CCS 2017; 2017. doi: 10.1145/3128572.3140447

 

  1. Sandfeld S. Exploratory Data Analysis. In: Materials Data Science. The Materials Research Society Series. Cham: Springer; 2024. p. 179-206. doi: 10.1007/978-3-031-46565-9_9

 

  1. Li S, Li M, Liu Z, Li M. A Data-Driven Residual Life Prediction Method for Rolling Bearings. In: Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023; 2023. doi: 10.1109/DDCLS58216.2023.10166951

 

  1. Anandan B, Manikandan M. Machine learning approach for predicting the peak temperature of dissimilar AA7050- AA2014A friction stir welding butt joint using various regression models. Mater Lett. 2022;325:132879. doi: 10.1016/j.matlet.2022.132879
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International Journal of AI for Materials and Design, Electronic ISSN: 3029-2573 Print ISSN: 3041-0746, Published by AccScience Publishing
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