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

Main Article Content

AKSHANSH MISHRA

Abstract

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.

Downloads

Download data is not yet available.

Article Details

How to Cite
[1]
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.
Section
Original Articles

References

Russell S.J., Norvig P., Artificial Intelligence: A Modern ApproachRussell, S. J., & Norvig, P. (2010). Artificial Intelligence: A Modern Approach. Artificial Intelligence. https://doi.org/10.1017/S0269888900007724 DOI: https://doi.org/10.1017/S0269888900007724

Fogel D.B., Defining Artificial Intelligence.In: Evolutionary Computation, 2006. DOI: https://doi.org/10.1117/12.669679

Ramos C., Augusto J.C., Shapiro D., Ambient intelligencethe next step for artificial intelligence. IEEE Intelligent Systems, 2008, Vol. 23(2), 158. https://doi.org/10.1109/MIS.2008.19 DOI: https://doi.org/10.1109/MIS.2008.19

Ghahramani Z., Probabilistic machine learning and artificial intelligence. Nature, 2015, Vol. 521, 4529. https://doi.org/10.1038/nature14541 DOI: https://doi.org/10.1038/nature14541

Ng A., What artificial intelligence can and cant do right now. Harvard Business Review Digital Articles, 2016,(24).

Chethan K.G., Artificial Intelligence: Definition, Types, Examples, Technologies.https://doi.org/https://medium.com/@chethankumargn/artificial-intelligence-definition-types-examples-technologies-962ea75c7b9b

Meredig B., Five High-Impact Research Areas in Machine Learning for Materials Science. Chem Mater, 2019, Vol. 31(23), 957981. https://doi.org/10.1021/acs.chemmater.9b04078 DOI: https://doi.org/10.1021/acs.chemmater.9b04078

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), 705262. https://doi.org/10.1073/pnas.1922210117 DOI: https://doi.org/10.1073/pnas.1922210117

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), 330913. https://doi.org/10.1021/acs.jpclett.5b01456 DOI: https://doi.org/10.1021/acs.jpclett.5b01456

Gajawada S.K., The Math behind Artificial Neural Networks.2019. https://doi.org/https://towardsdatascience.com/the-heart-of-artificial-neural-networks-26627e8c03ba

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), 51926. DOI: https://doi.org/10.1108/WJE-01-2020-0019

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), 517.

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), 73251. https://doi.org/10.1177/1464420719899685 DOI: https://doi.org/10.1177/1464420719899685

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. https://doi.org/10.3390/cryst10040290 DOI: https://doi.org/10.3390/cryst10040290

Decision Tree Introduction with example. https://doi.org/https://www.geeksforgeeks.org/decision-tree-introduction-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), 33640. https://doi.org/10.5755/j01.ms.18.4.3092 DOI: https://doi.org/10.5755/j01.ms.18.4.3092

Du Y., Mukherjee T., DebRoy T., Conditions for void formation in friction stir welding from machine learning. npj Comput Mater, 2019, Vol. 5, 68. https://doi.org/10.1038/s41524-019-0207-y DOI: https://doi.org/10.1038/s41524-019-0207-y

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. https://doi.org/10.5772/intechopen.89797 DOI: https://doi.org/10.5772/intechopen.89797

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, 122734. https://doi.org/10.1016/j.proeng.2013.09.202 DOI: https://doi.org/10.1016/j.proeng.2013.09.202

Netto N., Tiryakioglu M., Eason P., Characterization of Tool Degradation during Friction Stir Processing of 6061-T6 Aluminum Alloy Extrusions. Preprints, 2018, 2018080286. https://doi.org/10.20944/preprints201808.0286.v1 DOI: https://doi.org/10.20944/preprints201808.0286.v1

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.