Ensuring the quality and optimizing the tool in Friction Stir Welding (FSW) process is quite complex and the solution relies on implementing Condition Monitoring. The major impact of this process yields good quality welds and cuts down the non-operational timing and cost. Condition Monitoring is the key to find a solution to the challenging problem of ensuring quality and optimizing the tool in the FSW process. The creation of a graphical user interface (GUI) and the development and comparison of several models, including Decision Tree (DT), Random Forest (RF), Light Gradient Boosted Machine (LGBM), and Extreme Gradient Boosting (XGBoost), are the main objectives of this study. By offering an uniform interface for tracking and evaluating tool condition data, GUI can make it easier for operators and the maintenance crew to collaborate. Vibration analysis is the first step in tool condition monitoring. Al5083 and AZ31B are used as the workpiece and H13 as the tool in this investigation. The signals are obtained from the experimental setup via DAQ, and LabView processes them. A Python script converts the raw signals into statistical data. Following that, the data was loaded into ML models and optimized using Optuna. TKinter has been used to create the GUI. For prediction, the best models were included in the GUI. By the deployed models, LGBM generates 96% for 1000 rpm, 96.55% for 1200 rpm, and 95.90% for 1400 rpm for Al5083 93.22% for 1000 rpm, 99.29% for 1200 rpm, and 91.50% for 1000 rpm for AZ31B. For real-time prediction, these models are thus connected to a graphical user interface. In each case, the LGBM classifier topped the others. This work served as an initial basis for the creation of a semi-onboard diagnostic approach that requires minimal human input.


Friction Stir Welding, Tool Condition Monitoring, Machine Learning, Feature Extraction, Feature Selection, Feature Classification, GUI,


Download data is not yet available.


  1. R.P. Singh, S. Dubey, A. Singh, S. Kumar, A review paper on friction stir welding process. Materials Today: Proceedings, 38(1), (2021) 6-11. https://doi.org/10.1016/j.matpr.2020.05.208
  2. A. Chikh, M. Serier, R. Al-Sabur, Thermal Modeling of Tool-Work Interface during Friction Stir Welding Process. Russian Journal of Non-Ferrous Metals, 63, (2022) 690-700. https://doi.org/10.3103/S1067821222060049
  3. M. Serier, R.J. Jassim, R. Al-Sabur, A.N. Siddiquee, Thermal diffusivity modeling for aluminum AA6060 plates during friction stir welding. AIP Conference Proceedings, 3051, (2024) 070004. https://doi.org/10.1063/5.0191742
  4. Y.M. Hwang, P.L. Fan, C.H. Lin, Experimental study on Friction Stir Welding of copper metals. Journal of Materials Processing Technology, 210(12), (2010) 1667-1672. https://doi.org/10.1016/j.jmatprotec.2010.05.019
  5. P. Kah, R. Rajan, J. Martikainen, R. Suoranta, Investigation of weld defects in friction-stir welding and fusion welding of aluminium alloys. International Journal of Mechanical and Materials Engineering, 10(26), (2015) 1-10. https://doi.org/10.1186/s40712-015-0053-8
  6. N. Bhardwaj, R.G. Narayanan, U.S. Dixit, M.S.J. Hashmi, Recent developments in friction stir welding and resulting industrial practices. Advances in Materials and Processing Technologies, 5(3), (2019) 461-496. https://doi.org/10.1080/2374068X.2019.1631065
  7. R. Al-Sabur, H.I. Khalaf, A. Świerczyńska, G. Rogalski, H.A. Derazkola, Effects of Noncontact Shoulder Tool Velocities on Friction Stir Joining of Polyamide 6 (PA6). Materials, 15, (2022) 4214. https://doi.org/10.3390/ma15124214
  8. G. Byrne, D. Dornfeld, I. Inasaki, G. Ketteler, W. König, R. Teti, Tool Condition Monitoring (TCM) - The Status of Research and Industrial Application. CIRP Annals, 44(2), (1995) 541-567. https://doi.org/10.1016/S0007-8506(07)60503-4
  9. G. O'Donnell P. Young, K. Kelly, G. Byrne, toward the improvement of tool condition motoring system in manufacturing environment. Journal of Materials Processing and Technology, 119(1-3), (2001) 133-139. https://doi.org/10.1016/S0924-0136(01)00928-1
  10. T.I. Liu, B. Jolley, Tool condition monitoring (TCM) using neural networks. The International Journal of Advanced Manufacturing Technology, 78, (2015) 1999-2007. https://doi.org/10.1007/s00170-014-6738-y
  11. H.N. Teixeira, I. Lopes, A.C. Braga, Condition-based maintenance implementation: a literature review. Procedia Manufacturing, 51, (2020) 228-235. https://doi.org/10.1016/j.promfg.2020.10.033
  12. P.P. Shinde, S. Shah, A Review of Machine Learning and Deep Learning Applications, 2018 In 2018 Fourth international conference on computing communication control and automation (ICCUBEA), IEEE, (2018) 1-6. https://doi.org/10.1109/ICCUBEA.2018.8697857
  13. S. Shagan, Machine Learning: A Review of Learning Types. Preprints, (2020). https://doi.org/10.20944/preprints202007.0230.v1
  14. N.D. Ghetiya, K.M. Patel, Prediction of Tensile Strength in Friction Stir Welded Aluminium Alloy Using Artificial Neural Network. Procedia Technology, 14, (2014) 274-281. https://doi.org/10.1016/j.protcy.2014.08.036
  15. K. Balachandar, R. Jegadeeshwaran, Friction stir welding tool condition monitoring using vibration signals and Random forest algorithm–A Machine learning approach. Materials Today: Proceedings, 46, (2021) 1174-1180. https://doi.org/10.1016/j.matpr.2021.02.061
  16. S. Selcuk, Predictive maintenance, its implementation and latest trends. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 231(9), (2017) 1670-1679. https://doi.org/10.1177/0954405415601640
  17. T.M. Alamelu Manghai, R. Jegadeeshwaran, Vibration based brake health monitoring using wavelet features: A machine learning approach. Journal of Vibration and Control, 25(18), (2019) 2534-2550. https://doi.org/10.1177/1077546319859704
  18. M. Alamelu, R. Jegadeeshwaran, V. Sugumaran, Vibration based condition monitoring of a brake system using statistical features with logit boost and simple logistic algorithm. International Journal of Performability Engineering, 14(1), (2018) 1. https://doi.org/10.23940/ijpe.18.01.p1.18
  19. R. Jegadeeshwaran, V. Sugumaran, Method and apparatus for fault diagnosis of automobile brake system using vibration signals. Recent Patents on Signal Processing. 3(1), (2013) 2-11. https://doi.org/10.2174/2210686311303010002
  20. S.V. Dahe, G.S. Manikandan, R. Jegadeeshwaran, G. Sakthivel, J. Lakshmipathi, Tool condition monitoring using Random Forest and FURIA through statistical learning. Materials Today: Proceedings, 46, (2021) 1161-1166. https://doi.org/10.1016/j.matpr.2021.02.059
  21. N. Pudjihartono, T. Fadason, A.W. Kempa-Liehr, J.M. O'Sullivan, A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction. Frontiers in Bioinformatics, 2, (2022) 927312. https://doi.org/10.3389/fbinf.2022.927312
  22. A.D. Patange, R. Jegadeeshwaran N.S. Bajaj, A. Naman & Khairnar, N.A. Gavade, Application of Machine Learning for Tool Condition Monitoring in Turning. Sound & Vibration, 56, (2022) 127-145. https://doi.org/10.32604/sv.2022.014910
  23. S.A. Vendan, R. Kamal, A. Karan, L. Gao, X. Niu, A. Garg, Akhil. (2020). Supervised machine learning in friction stir welding (FSW). Welding and Cutting Case Studies with Supervised Machine Learning, (2020) 119-185. https://doi.org/10.1007/978-981-13-9382-2_3
  24. P. Lipinski, E. Brzychczy, R. Zimroz, Decision Tree-Based Classification for Planetary Gearboxes' Condition Monitoring with the Use of Vibration Data in Multidimensional Symptom Space. Sensors, 20(21), (2020) 5979. https://doi.org/10.3390/s20215979
  25. W. Dong, Y. Huang, B. Lehane, G. Ma, XGBoost algorithm-based prediction of concrete electrical resistivity for structural health monitoring. Automation in Construction, 114, (2020) 103155. https://doi.org/10.1016/j.autcon.2020.103155
  26. M. Massaoudi, S. S. Refaat, I. Chihi, M. Trabelsi, F.S. Oueslati, H. Abu-Rub, A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting. Energy, 214, (2021) 118874. https://doi.org/10.1016/j.energy.2020.118874
  27. Y. Orenes, A. Rabasa, J.J. Rodriguez-Sala, J. Sanchez-Soriano, Benchmarking Analysis of the Accuracy of Classification Methods Related to Entropy. Entropy, 23(7), (2021) 850. https://doi.org/10.3390/e23070850
  28. S.S. Stevens, On the Theory of Scales of Measurement. Science, 103(2684), (1946) 677-680. https://doi.org/10.1126/science.103.2684.677
  29. A.S. Bahedh, A.M. Raheem Al-Sabur, A.K. Jassim, Machine learning algorithms for prediction of penetration depth and geometrical analysis of weld in friction stir spot welding process. Metallurgical Research & Technology, 119(3), (2022) 305. https://doi.org/10.1051/metal/2022032
  30. R. Al-Sabur, A.K. Jassim, E. Messele, Real-time monitoring applied to optimize friction stir spot welding joint for AA1230 Al-alloys. Materials Today: Proceedings, 42(5), (2021) 2018-2024. https://doi.org/10.1016/j.matpr.2020.12.253
  31. A. Joshuva, V. Sugumaran, A lazy learning approach for condition monitoring of wind turbine blade using vibration signals and histogram features. Measurement, 152, (2019) 107295. https://doi.org/10.1016/j.measurement.2019.107295
  32. A. Mostafaeipour, M.B. Fakhrzad, S. Gharaat, M. Jahangiri, J.A. Dhanraj, S.S. Band, A. Issakhov, A. Mosavi, Machine Learning for Prediction of Energy in Wheat Production. Agriculture, 10, (2020) 517. https://doi.org/10.3390/agriculture10110517