Artificial Intelligence Algorithms for the Analysis of Mechanical Property of Friction Stir Welded Joints by using Python Programming

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In modern computational science, the interplay existing between machine learning and optimization process marks the most vital developments. Optimization plays an important role in mechanical industries because it leads to reduce in material cost, time consumption and increase in production rate. The recent work focuses on performing the optimization task on Friction Stir Welding process for obtaining the maximum Ultimate Tensile Strength (UTS) of the friction stir welded joints. Two machine learning algorithms i.e. Artificial Neural Network (ANN) and Decision Trees regression model are selected for the purpose. The input variables are Tool Rotational Speed (RPM), Tool Traverse Speed (mm/min) and Axial Force (KN) while the output variable is Ultimate Tensile Strength (MPa). It is observed that in case of the Artificial Neural Networks the Root Mean Square Errors for training and testing sets are 0.842 and 0.808 respectively while in case of Decision Trees regression model, the training and testing sets result Root Mean Square Errors of 11.72 and 14.61. So, it can be concluded that ANN algorithm gives better and accurate result than Decision Tree regression algorithm.


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A. MISHRA, “Artificial Intelligence Algorithms for the Analysis of Mechanical Property of Friction Stir Welded Joints by using Python Programming”, Weld. Tech. Rev., vol. 92, no. 6, pp. 7-16, Aug. 2020.
Original Articles


Russell S.J., Norvig P., Artificial Intelligence: A Modern ApproachRussell, S. J., & Norvig, P. (2010). Artificial Intelligence: A Modern Approach. Artificial Intelligence.

Fogel D.B., Defining Artificial Intelligence.In: Evolutionary Computation, 2006.

Ramos C., Augusto J.C., Shapiro D., Ambient intelligencethe next step for artificial intelligence. IEEE Intelligent Systems, 2008, Vol. 23(2), 15–8.

Ghahramani Z., Probabilistic machine learning and artificial intelligence. Nature, 2015, Vol. 521, 452–9.

Ng A., What artificial intelligence can and can’t do right now. Harvard Business Review Digital Articles, 2016,(2–4).

Chethan K.G., Artificial Intelligence: Definition, Types, Examples, Technologies.

Meredig B., Five High-Impact Research Areas in Machine Learning for Materials Science. Chem Mater, 2019, Vol. 31(23), 9579–81.

Lu L., Dao M., Kumar P., Ramamurty U., Karniadakis G.E., Suresh S., Extraction of mechanical properties of materials through deep learning from instrumented indentation. Proceedings of the National Academy of Science of the USA, 2020, Vol. 117(13), 7052–62.

Rupp M., Ramakrishnan R., Von Lilienfeld O.A., Machine Learning for Quantum Mechanical Properties of Atoms in Molecules. Journal of Physical Chemistry Letters, 2015, Vol. 6(16), 3309–13.

Gajawada S.K., The Math behind Artificial Neural Networks.2019.

Senthilnathan T., Sujay Aadithya B., Balachandar K., Prediction of mechanical properties and optimization of process parameters in friction-stir-welded dissimilar aluminium alloys. World Journal of Engineering, 2020, Vol. 17(4), 519–26.

Mishra A., Tiwari A., Dubey N.K., Machine Learning Approach to Determine Corrosion Potential of Friction Stir Welded Joints. Journal of Image Processing & Pattern Recognition Progress, 2020, Vol. 7(1), 5–17.

Hartl R., Praehofer B., Zaeh M.F., Prediction of the surface quality of friction stir welds by the analysis of process data using Artificial Neural Networks. Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, 2020, Vol. 234(5), 732–51.

Abd El-Rehim A.F., Zahran H.Y., Habashy D.M., Al-Masoud H.M., Simulation and prediction of the vickers hardness of AZ91 magnesium alloy using artificial neural network model. Crystals, 2020, Vol. 10(4), 290.

Decision Tree Introduction with example.

Bozkurt Y., Kentli A., Uzun H., Salman S., Experimental Investigation and Prediction of Mechanical Properties of Friction Stir Welded Aluminium Metal Matrix Composite Plates. MATERIALS SCIENCE (MEDŽIAGOTYRA), 2012, Vol. 18(4), 336–40.

Du Y., Mukherjee T., DebRoy T., Conditions for void formation in friction stir welding from machine learning. npj Comput Mater, 2019, Vol. 5, 68.

Hema P., Experimental Investigations on AA 6061 Alloy Welded Joints by Friction Stir Welding. In: Cooke KO, editor. Aluminium Alloys and Composites, IntechOpen Limited, 2019.

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, 2013, Vol. 64, 1227–34.

Netto N., Tiryakioglu M., Eason P., Characterization of Tool Degradation during Friction Stir Processing of 6061-T6 Aluminum Alloy Extrusions. Preprints, 2018, 2018080286.

Amirabadi H., Bani Mostafa Arab N., Safi S.V., A Novel Approach for Improving Mechanical Properties in Friction Stir Butt Welded AA6061 Aluminum Plates by Using Preheat. Preprints, 2020, 2020020183.